Issue 7: October 22, 2026

This week: Stefano Puntoni on the effort that helps us learn, an interactive idea to try with students, and new ways to put AI to work.

Contents

• Feature: Stefano Puntoni and the effort worth keeping

• OpenAI Education in the news

• Product Corner: New ways to work, research, and learn

• Try This: Make sample size visible

• Around the World: George Osborne at Oxford

• Offer: Four free months and a mission to build

• Learning with OpenAI: Guiding Student Success webinar series

Feature: Stefano Puntoni and the effort worth keeping

What effort helps us learn?

In our conversation, Wharton professor Stefano Puntoni explores how AI can remove tedious friction while preserving the effort that builds understanding. His examples range from a syllabus bot that answers routine questions to using AI to rehearse an assignment before giving it to students.

Watch the conversation with Stefano

OpenAI Education in the news

Forbes: Why learning matters in the age of AI

In Forbes, education contributor Ray Ravaglia speaks with OpenAI’s VP of Education Leah Belsky about AI as a “curiosity amplifier” and the role educators play in helping students find a reason to learn. They explore why assessment needs to reveal students’ reasoning and learning process. The article also highlights Duke professor Scott Dyreng’s use of AI to examine team discussions and OpenAI’s Learning Outcomes Measurement Suite.

Read the Forbes article

Product Corner: New ways to work, research, and learn

GPT-6.1 Sol. Stronger document understanding and multistep work can help with tasks such as reviewing a packet of materials or building a tool. Available in ChatGPT Work and Codex.

GPT-Rosalind. For life sciences researchers, this specialized model supports work such as literature synthesis, hypothesis development, and experiment planning. Access is available to eligible organizations through a trusted-access program.

Intelligent UI. In GPT-6 Chat, explanations can include controls that let learners change inputs and explore what happens. The example below turns sample size into something students can test.

Track all product updates here.

Try This: Make sample size visible

Example shown—paste the prompt below into ChatGPT to try it.

Ask students to predict which sample size will produce the most stable mean. Then choose 5, 20, or 100 die rolls, press Roll, and compare the results. Repeat each size several times before drawing a conclusion.

Try this prompt in GPT-6 Chat:

Open ChatGPT and paste in the prompt. Choose a supported reasoning setting such as High. Intelligent UI is not available in Work, Voice, or the Pro reasoning setting. See availability.

Around the World: George Osborne at Oxford

On October 15, George Osborne, Managing Director and Head of OpenAI for Countries, joined Professor Sir Nigel Shadbolt for a fireside conversation at the Oxford Generative AI Summit. His message: Britain can build on its strengths in research, technology, and life sciences by broadening access, skills, and adoption.

“Wishing away new technology is not a solution,” he said. For educators, the conversation raises a practical question: how can we give learners more opportunities to try AI, develop judgment, and put it to use?

Read the highlights from Oxford

Offer: Four free months and a mission to build

Four free months of ChatGPT Plus for eligible U.S. college students. The offer includes ChatGPT Work and is available to claim through October 31, 2026. Enrollment verification and a payment method are required; after four months, the subscription renews at $20 per month unless canceled. See eligibility and terms.

Learn more and claim the student offer

Then give students a mission to build. OpenAI’s multiplayer game mission on Handshake’s AI Skills Studio guides learners through making design decisions, building a game with ChatGPT Work, testing it, and improving what doesn’t work. The guided mission takes about 45 minutes and requires a ChatGPT account with Work mode.

Try the 45-minute game mission

Know a student who could use this? Share the offer and mission with them.

Share

Learning with OpenAI: Guiding Student Success webinar series

Help students make the most of ChatGPT. These are practical sessions built for advisors, campus trainers, and faculty. Bring a student need and leave with prompts and approaches you can adapt.

• Tuesday, October 27, 10–11 a.m. PDT: Help Students Learn and Create with ChatGPT

• Wednesday, October 28, 4–5 p.m. PDT: Help Students Plan College, Careers, and Life with ChatGPT

• Thursday, October 29, 11 a.m.–noon PDT: Lead Practical ChatGPT Workshops on Campus for Students

• Monday, November 2, noon–1 p.m. PST: Lead Practical ChatGPT Workshops on Campus for Students

• Wednesday, November 4, 3–4 p.m. PST: Help Students Learn and Create with ChatGPT

• Thursday, November 5, 10–11 a.m. PST: Help Students Plan College, Careers, and Life with ChatGPT

See all dates and register for individual workshops.

What effort is worth keeping?

Where could AI remove a routine task—and where do you want students to do the thinking themselves? Reply with one example. Your answer will help shape a future issue.

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Joshua Achiam, AI researcher and Chief Futurist at OpenAI at the time of recording, on curiosity, craftsmanship, and finding your own voice.

Joshua Achiam’s account of learning is full of curiosity, difficult books, generous teachers, and projects that forced him to turn ideas into something real. In conversation with Siya Purohit, he describes a standard of excellence built through practice, collaboration, and attention to the questions a piece of work leaves unanswered.

For Josh, that standard also shapes how he uses AI. He starts with a detailed explanation of what he wants to do and why. He pushes vague recommendations toward concrete answers, asks the system to work through real constraints, and checks the original sources. AI helps inform his research and thinking; when he writes something for others to read, he wants the words to be his own.

The conversation explores the teachers and peers who encouraged his curiosity, the discipline of building and testing ideas, and the patient listening that makes collaboration work. It closes with a case for continued learning: developing enough depth to judge the work and enough breadth to keep growing as our roles change.

The Interview (edited)

Siya: Josh, growing up, what were you like as a student?

Josh: So when I was a little kid, I was not particularly attentive. I wasn’t a great student. I would run around. I was a little unfocused. In elementary school, there’s a story that my mom loves to tell where the superintendent came to visit my school when I was in like third grade. And when the superintendent and the principal came into my classroom, I was ignoring their presentation completely so I could just read Captain Underpants. When I was little, that was the kind of student that I was. I got a bit more serious about things when I was in high school, and that was when I really started taking academics quite seriously and thinking, “Okay, this is important. I’ve got to prep for college.”

Siya: Is there a teacher that you still think about who may have seen something in you or influenced you in some way?

Josh: Quite a few of them. There was a teacher who, in middle school English class, recognized that even though I was one of the much quieter kids in the class, and I didn’t naturally pop socially the way that other kids did, I was thinking of interesting things. And he wanted to encourage that in me. And he really took time and helped engage me to think about creative writing, to think about analytical work that I could do and gave me a lot of confidence to go forward. So, Mr. Gordon, if you’re ever watching this, thank you. And in high school, I had a couple of teachers who really nurtured my interest in science and who helped me develop mathematically. One of them took a chance on having me in an AP Physics class, even though I hadn’t yet taken AP Calculus. And he just kind of gambled that I would figure out the math as I went. And he was right. And in the first week of his class, he did a review of all the calculus material for everyone. And that was how I learned calculus. It was great.

Siya: Calculus is a hard subject. So how do you think you learned it in a week?

Josh: I think he broke things down in this really nice, systematic, thoughtful way that motivated everything kind of intuitively. Part of mathematical instruction sometimes is intimidating or difficult to approach because people throw a lot at you at once and don’t really tell you what the important part is and don’t necessarily get to the intuition. But he was a teacher who really knew how to show you where the intuitive part of mathematics was. And I appreciated that a lot. It made it really clear and simple and straightforward, which is not usually what you hear about for math, but he was able to do it. And one other teacher who I just, I have to, while I’m on a camera and talking, thank. I had a teacher for Latin classes for four years in a row when I was in middle school and high school who was close to retirement, Mr. Goufar, and he was kind of like a... he was like a little curmudgeonly but in sort of a delightful and loving way. He felt like a class grandpa and he nurtured all of our interests in the classics by making it feel very accessible. There are not very many students who otherwise go into technical fields who come away with an appreciation for the Aeneid, but I totally did. And it was because I had a teacher like that who was warm and who made it accessible and made us feel like we could kind of have it become a part of ourselves in the process of studying it. And it was just great.

Siya: That’s beautifully put. You said you became more serious in high school. What prompted that?

Josh: I think my parents cared a lot about me and my sister being academically successful, successful in life, and they really encouraged us to take it seriously because we kind of had this implicit cultural pressure: Going to college is important. Getting a degree is important. Going to the best college you possibly can go to is important. And they weren’t super hard on us about it or anything like that. They didn’t really push us in a bad way, but it was encouraged to take it seriously and I think at that point I also started getting a little more competitive. I wanted to do well. There was a lesson I got from my dad which was super important though. He would always tell us, “Don’t compete against other people; compete against yourself.” And I really took that to heart. I wanted to constantly outperform myself. I wanted to do better than I could before and the material also got more interesting. So I wound up taking a lot of AP classes in high school and then when there was material that wasn’t covered in classes, one of the things I was also very lucky about was that I had a number of friends in high school who were super curious about advanced topics in mathematics and science that wouldn’t have come up in a high school class. And they were very autodidactic. They liked to go out and get their own books and study for themselves. And we had a nerdy group of friends that had jokes about advanced mathematical things. And so I felt very influenced by that. And I also wanted to go and get advanced math books and study them. And I did that. And the first one that I picked out, I went to Barnes & Noble and I was like, all right, let me find something kind of approachable. Let me find something that looks, all right, the book that’s about this thick and it’s got a nice friendly cover, it can’t be that bad. It was Vectors, Tensors and the Basic Equations of Fluid Mechanics. And it was, whew! I spent like months staring at just like the first three pages of this book trying to understand the notation. It was a challenge. But because I had this extremely hard thing in front of me that I didn’t know how to interpret at all, I was frustrated, like, “Okay, I have to do something so that I can read this and get it.” And so I pushed myself to do a bit more.

Siya: So this sounds like way beyond the curriculum of your high school.

Josh: Yeah, that was not a high school curriculum textbook.

Siya: It was just your peer group that was encouraging you to think like that.

Josh: Yeah, it’s very helpful to have a peer group that is excited about learning and is excited about academics and studying interesting topics.

Siya: What do you think that school taught you well? And what do you think you had to learn outside of school? More in terms of the skills that you developed for life.

Josh: I think my experience of school taught me that being able to engage in a really wide range of intellectual areas was going to lead to having deeper connections with people. The more that I learned, the better I was able to connect with peers and with teachers and with folks who had something really interesting to offer. I was pretty lucky. The type of school that I went to had this very academic and intellectual environment, and it was really stimulating. I think I didn’t quite get discipline, which maybe is an odd thing to say, but part of pursuing something effectively in the real world is the discipline that comes with every day making progress on a goal and especially pushing to make something, to make something tangible, to make something real, to build something, and to build something in collaboration with others. And in school, I don’t know that... Sometimes the goals were so academic that this process of actually making something wasn’t really necessary, and so the discipline that you have to have to make something wasn’t always required. And so I could spend my time studying really interesting topics or writing about really interesting topics or deriving something, but that was different from having to put together a completed project end-to-end that then I could test or physically build or run an experiment on. And so I wound up with a much more theoretical bent and not as much of an experimental bent. That I think kind of came later when I went to grad school eventually. Because in grad school you really have to put things together. You have to test your hypotheses. You have to prove that something works. You have to become very rigorous about the experiments. And I got quite lucky when I went to grad school that I had a number of mentors who really emphasized this kind of experimental and building side of things.

Siya: Was that a hard skill to pick up in grad school?

Josh: Totally.

Siya: Because you must have been, what, 22, 23 by then. So it was a very different way of approaching. How did you go about changing your mindset to be more experimental?

Josh: So what I started to do in early grad school, I wanted to pursue studies in AI. And I had these kinds of theoretical ideas of what to do with AI. In order to make them tangible and actually publish something on them and produce a project about them, I would have to get good at writing a lot of code and running a lot of experiments. And I didn’t have that skill set coming in. That wasn’t super natural. So, I had programmed since I was in middle school in settings that were not rigorous and scientific and where you were trying to make something consequential, but the extent of programming and building a codebase from scratch I hadn’t really done. So I set about doing a lot of re-implementation projects where you take a thing that already exists and you just try to figure out how do I build a copy of this thing? How do I build a replica of this thing? How do I make it from the inside out and thereby learn the contours of how it works and also learn how to make other things down the road? So I re-implemented a number of deep learning and AI papers at the time and kind of struggled through the process of doing it and learning to write a moderately sized codebase and maintain it and checking the experimental results and kind of bearing it out the hard way. I also got very lucky that the research group that I was in in grad school published certain types of starter kits where they made reference implementations for a number of different algorithms and they made logging tools that let you run experiments very, very easily. And from studying that work, I’m thinking in particular of a piece of software called rllab, which was published by Rocky Duan and Peter Chen and a couple of other folks circa 2016. I don’t know if it was an early OpenAI paper. It might have been an early OpenAI paper. It was kind of overlapped. Those people were half at OpenAI and half here in that time frame. But I studied this codebase, and I tried implementing some ideas that I had in this codebase, and that was how I started building a first research project that became a publication. So I think kind of finding your tribe that’s able to do the things and has the culture of knowledge and expertise that you want to learn is a really important part of education and kind of cultivating talent in the direction of being able to do those things.

Siya: I think one thing that really stands out in your story is how deep you go into these different topics. Sometimes, even if the topic seems so cumbersome, like it did in high school, possibly, what motivates you to actually go that deep?

Josh: The motivation usually comes from curiosity and stubbornness. I don’t know exactly how to put it, but there’s a feeling when you sink your teeth into a problem because you want to solve it, or you want to know what the solution is, and you just keep going at it over and over and over again. It fills your spare time thoughts. You wind up postponing other things because you’re more interested in that problem than you are in something else. And that intrinsic drive is kind of the important thing. If I were to think about, how to cultivate talent in others, nurturing that intrinsic drive, finding where they have it for, where someone has it for something, and encouraging them over and over to pursue it, to spend the time doing the thing that they’re really excited about, that they’re really passionate about, that they can’t get out of their head, that will cause them to then actually spend the time, do the thing, get the skill level up that comes with pushing themselves through it. And I think it leads to the outcome in the end.

Siya: Something a lot of educators ask us is about: How do you even identify that in a student? How do you identify what they’re actually motivated by? But especially when they’re using AI solutions to get answers so much more quickly than ever before. So they’re not going through that process of discovery as they used to in the past.

Josh: Yeah, I think that’s a good question. It depends a lot on what age range you’re talking about, right? Because figuring out where someone’s interests are and where someone’s passions are and when to encourage it and how to encourage it, it varies a lot by age. I think for younger kids, look for the thing where they can’t sit still and they’re distracted by something. And the thing that they’re distracted by may actually be the beginning of a superpower. It might not be bad behavior that you have to kind of push them away from. It might be something where you should figure out, all right, how do I get them to lean in on this? Know they can’t stop reading Captain Underpants in class. Maybe they’re going to be a big reader. Maybe I should just put more and more interesting books in front of them. And even if they haven’t figured out exactly how to navigate the social niceties or do the thing that they have to do in addition to that, pushing them more in that direction will give them a big unlock later in life. For folks who are maybe more at the high school or college level, there are things that you see people have in their individual personalities: the part of them that’s distinct from the people around them, the thing that they’re interested in, even if their peers kind of are all interested in something else, or the thing that they kind of, that maybe they get shy about because it’s sort of too important to them to want to talk about. And I think looking for those kinds of unique features of those people and giving them examples of things that they could do in those spaces where it would excite them to kind of do it—that’s the thing to do. There’s no getting away from the fact that young folks are going to use AI to do half of their schoolwork or half of their projects. If you give them busywork to do, they’re going to short-circuit the busywork. There’s something else they really want to be doing. And you kind of have to find that other thing because they’re not going to go and use the AI for that unless the AI is actually an important piece of doing that thing. If AI is helping them build software and the outcome that they really care about is having built an important piece of software that does something exciting for them, that’s great. Help them do it. Push them a little more in that direction. Otherwise, look for the thing that they’re not using the AI for that they don’t think of as busywork, that they think of as the fulfilling thing and help them double down on that.

Siya: Amazing. Let’s talk about OpenAI. So you once said that you kind of grew up at OpenAI, given how early you joined and how many different roles you’ve had at the company. So we’d love to hear, when do you think your standards for work became much higher at OpenAI?

Josh: It’s a good question. For what it’s worth, I think OpenAI is a place where everyone comes in with a relatively high standard of work. It’s a place that attracts top talent and it’s a place that prides itself on getting to the frontier. So I think even when I came in, my standard for work was quite high. Maybe the types of things that changed over time as I grew up in here were my expectations and norms and standards inside of collaboration and what I expected good collaboration to look like and good leadership to look like. Because those are qualities that were not really things I had to hone or learn in the type of schooling that I was doing before being at OpenAI. I came to really appreciate when folks would be able to take their ego out of a hard question and a hard strategy question or a hard question about what research direction was going to be productive and in a low-temperature, methodical, thoughtful way, elicit the pros and cons from everyone around them, just elicit the different points of view and listen very effectively. And I came in as almost like a brash hothead in some respects. I was good at doing technical research, but I was really annoying when I would try to impose my own opinions on different things. And what I learned from watching who was effective and who was less effective was that the people who were patient and thoughtful and really able to take themselves out of it for a little while to just listen and see where a consensus might exist and then nurture that kernel of consensus between others and help it grow, that that was very successful. And so that kind of gradually became my standard for collaboration and leadership within the company.

Siya: When you see a piece of work, what makes you think it’s truly exceptional versus good enough?

Josh: I love this question. I was thinking about it recently. One quality that I look for that tells me that this is really like a work of great craftsmanship—that this was something that was put together by someone who cared a lot and was extremely talented—is this: For all of the questions that I naturally have as I go through the work, do I find that the author understood the question, internalized it, and has already prepared some kind of an answer or response? Do I find that they’ve covered all the bases for the things that one would have to do? Do I find that they’ve covered all the bases for the natural set of objections that one might have to the thesis of the work or the basic thing that you would do in here? Have they prepared an answer for why they made a trade-off a certain way? Great craftsmanship involves trade-offs. There are trade-offs everywhere. You have to decide between quality and speed. In software, you have to decide latency or what does the user really need. In policy, you have to have trade-offs about what different stakeholders need. And just when you see a work where the author of the work or the authors of the work have predicted in advance all of the objections to the trade-offs that they’ve made, and they have a defensible stance on why that trade-off was chosen, and it reflects something deep in the craft, either about the technical side of things or about the cultural or societal implications of the things—that’s how you know that it’s a genuinely exceptional thing, this detailed account of the trade-offs and the depth of the craft.

Siya: What I’m hearing is someone who’s very comprehensive in their thought process. I spend a lot of time with Gen Z and it seems like that’s a skill that they’re really struggling to develop because they have such strong points of view sometimes on the things that matter to them that they almost fail to look at the other sides of it. And that’s something educators keep bringing up to us too. How does one develop comprehensiveness of thought like that ?

Josh: Oh, that’s a great question. So it’s hard for me to say one way or another what Gen Z really is like or thinks because everyone in a cohort is so unique and individual and different from each other. And I haven’t spent enough time around the Gen Z kids to know exactly what their cohort behaviors are. And I always want to be a little bit cautious and humble in how I judge the younger cohort because it’s very easy for me as now a 30-something to want to be like an old complainer and say, like, ah, you know, kids these days. But I’ve also heard these kinds of things, and I’ve seen some of it. And my impression is that there are maybe some cultural points of view right now that are common in that generation. And there’s a lot of social pressure to adopt this point of view or that point of view. I think something about the prevalence of social media, Instagram, TikTok, etc., made it much easier to apply social pressure among young people. And if we can encourage people, if we can encourage these young people to not pay so much attention to what everyone else on social media wants them to think, to not feel like seeing someone else having that thing means they’ve got to have it too, to not worry about where they are in life relative to other people who are painting a sort of false picture of how good their lives are and not feel obliged or pressured to take a stance on something before they themselves have deeply studied it, then maybe we can make some progress. And I think that’s got to come from parents. I think that’s got to come from educators. I think that’s got to come from trusted people in kids’ lives. And it’s kind of difficult. It really takes a village to help get people out of a place of receiving points of view and willing to start considering them from scratch. You often hear or see complaints these days that universities are not doing enough to encourage diversity of thought. And it’s possible that there is some truth to this. I think we can... There’s another thing that comes to mind thinking about universities. I was reading recently about universities that are considering analysis of student personalities as a part of the admission process and getting peers to maybe rate each other to figure out does this person win the approval of their peers in these respects as maybe a component of a comprehensive evaluation. And I hope that the way that we evaluate students, the way we evaluate their personal essays, can update a little bit so we’re not incentivizing them to homogenize too much, right? Because once you have an evaluation process that you’re telling students is an important gate for them to make progress in life, they start trying to figure out how do I succeed in that evaluation process. And in AI, we think about that as the AI trying to figure out how to game the benchmark. Well, students try to do that too. We tell them, like, you’ve got to write this personal essay that must make you look like either a saint or someone who comes from a very specific type of background and students are very responsive to that. They feel like, okay, if I want to get a degree in life and get to participate in the economy and have economic opportunity, I have to play this game. We should stop making kids play games like this. We should stop making kids feel like their life outcome is dependent on whether they conform to a very specific standard from early on. And that might be another piece of how we help encourage some of the diversity of thinking.

Siya: Because when I think back to college the most fun parts were the funky people you met. And it really expands your way of thinking but also how you socialize with them and how you learn how to talk to people. That really matters. What separates someone who is very smart from someone whose work you deeply respect?

Josh: Having done the work. And the subtlety here is that there are lots of people who are incredibly smart who are capable of extraordinary work. Not everyone winds up doing the best work they can possibly do. And there are lots of reasons that they don’t necessarily do it. One might be that they talk themselves out of it. Another might be that they don’t wind up in the right place to foster their talent and their creativity. I think everyone should try to go to work in a place that’s going to encourage them to do their best work and where it’s easy or possible for them to do it. Sometimes people get discouraged by something going wrong and then they wait too long and they don’t go after doing the thing. But the real differentiator between people who are just possibly capable and people who have accomplished something that I look at and I respect the work quite a lot is the process of doing the work. The point where they sit down and say, I’m going to carve out time, I’m going to find a specific type of job, I’m going to find a specific type of role, I’m going to put myself in the place where I can do the best work of my life and then I’m going to just grind really hard until it’s done. That I think is the thing. And something I like to believe is that almost everyone I meet is unique in some way and has the capacity to be exceptional in some way. Whether that’s in a local way, they can be exceptional in loving and taking care of the people in their lives, or in a global way, they can be exceptional by solving some frontier problem in science and technology that winds up having a lot of downstream impacts. I think everyone has some unique capacity to be exceptional, but it’s the thing where they resolve to themselves that they want to do it and then choose consciously day in, day out to do it that makes the difference.

Siya: When I talk to students, I talk about how important reps are. You have to go through the motions over and over to be able to master anything, whether it’s talking to a recruiter or getting even a document out. Something that I get a lot of pushback on from campuses is that students are: okay I used AI am I allowed to use in the classroom and I submitted the first submission I got back from AI and the entire conversation we’re having is like no you need to push AI to think more, think bigger. Think like, I mean, capture your own voice within that also. And that’s also going through different kinds of reps, right?

Josh: Totally.

Siya: When you use AI, how often do you push back? What is your thought process on getting really good output from AI?

Josh: I tend to push back a lot. When I am working on something where I know that the AI is going to be helpful as a piece of the loop, I put a lot of effort into trying to specify what it is that I’m trying to do. So I’ll write very, very long multi-paragraph prompts to explain what I’m going for and why I’m going for it and what the motivation is. And then the AI will generally be doing some kind of research on my behalf or making recommendations in some set of areas. And within the recommendations that it makes, I’ll push back and I’ll say I want to drill in further on that. I don’t just want this very high-level, vague description of something that could be. I want you to make this progressively more tangible and refined. I want you to grapple with the hard constraints in the reality of this thing. And I want to learn about those hard constraints as we’re going. So a focus on the depth and the detail—kind of the resolution of the answer—is, in my experience, very helpful for getting useful things out of AI. And I would encourage students to do the same.

Siya: And how do you make sure that AI sounds different for you than it does for everyone else?

Josh: It is a genuinely hard technical challenge to get the AI to have a different voice in different things. So for the type of writing that I put into the world, I don’t actually use it to produce that writing. I use it to inform my research process and my thinking and what things I know that can then be reflected in writing. I also really hold it to a very high bar for correctness and for citation to original sources because I want to make sure that the things that I go and say and advocate for are grounded in facts. But when I write, I try to write in my own voice. And I would encourage students to do the same thing. I think that learning to speak in your own voice is just such an important part of becoming an expert yourself or becoming the best and most capable version of yourself. So even if you can use AI, and maybe using it to do a whole lot of busywork is fine, but when you’re trying to say something that you think is important to say, get the AI to help you, but say it in your own voice in the end.

Siya: As AI makes more people capable of producing good work, what becomes more important?

Josh: There are a few things that I think will always be important. One, being able to listen to and engage productively with your colleagues, with your friends, with your family, with your community. Communication skills, respect, and collaboration skills are always, always going to be useful. No matter what the AI can do, we’re still going to be people who need to get along with other people. Two, I think... Coming back to depth, there are a lot of things that AI will be able to do and that we might be able to get without too much effort or too much thinking. But pushing the AI for depth and being able to yourself understand the domain in which you’re asking the AI to do work to a place where you will elicit from it better work, where you can push it, where you can be a part of composing its outputs productively—it requires people to have a relatively deep understanding and a relatively broad understanding of a lot of different things. So I think it becomes important for people to know more and study more maybe than they have and to have more theoretical knowledge, to have more ideas than they would have necessarily had to in the past. It becomes important for people to avoid over-specializing in a specific narrow area and to remain kind of capable of breadth and to maybe see themselves as lifelong learners: there’s never a point where their education is done and they’ve figured out enough to do the job that they have to do. What their job is, what their role is, it’s always going to be changing and there will always be new opportunities, and they should embrace those and be willing to just learn a lot about a lot of fields so they can productively engage with the AI to either address something that’s important societally or produce something that they feel fulfilled by creatively.

Issue 6: September 24, 2026

This week: three practical ways to help your students study, build, and explain what they know.

Contents

• Feature: Choose your mission

• Product Corner: Flashcards and quizzes

• Try This: Visualize

• Around the World: Students are building in Japan

• Offer: Free ChatGPT Plus for Students

• Learning with OpenAI: Courses and webinars

Feature: Choose your mission

What could your students build in 45 minutes?

OpenAI is a founding partner of Handshake’s new AI Skills Studio offering guided missions that help students and early-career learners practice with AI and finish with something to share. Students can highlight their project on their Handshake profile for employers to see.

Start with Create a Multiplayer Game with OpenAI. Students use ChatGPT Work to shape an idea, build and test a game, then publish a link for friends to play. No coding experience is required.

Try the 45-minute game mission

Product Corner: Flashcards and quizzes

Create flashcards in ChatGPT. Ask for flashcards on a topic you want to learn, or upload your notes and ask ChatGPT to turn them into flashcards. Tap to flip, check what you know, and practice what you missed. Ready to practice again? Just ask ChatGPT for your flashcards, or find them saved in your library.

Create quizzes in ChatGPT. Put your knowledge to the test with interactive quizzes. Ask ChatGPT to quiz you on any topic, or upload your own study materials to practice what you’ve learned.

Track all product updates here.

Try This: Visualize

Type @Visualize in ChatGPT and select the matching suggestion. Then describe the interactive visualization you want to create.

This opens up new ways to teach complex concepts, explore systems, and help students learn by interacting with ideas.

Try this prompt:

Availability varies by account and platform. Learn more.

Around the World: Students are building in Japan

On September 15, students came together for OpenAI Codex Student Builder Fest 2026 in Japan to bring their ideas to life. Their projects ranged from AI-assisted home furnishings and more accessible communication to a virtual restaurant that encourages home cooking. The winning team, Poietra, built a collaborative video creation tool that combines natural-language instructions with hands-on editing—and will head to OpenAI DevDay in San Francisco.

• Poietra demo: the winning team’s collaborative video tool combines natural-language instructions with hands-on editing.

• Foldable furnishings demo: AI-assisted designs for the home.

• Kotoba Link demo: speech, lip-reading, text, and sign language for more accessible communication.

• Virtual restaurant demo: meal photos turn home cooking into progress in a game.

Offer: Free ChatGPT Plus for Students

Four free months of ChatGPT Plus for college students. To help more students create polished work, build innovative tools, and bring ambitious ideas to life, eligible U.S. college students can now access ChatGPT Work with four months free of ChatGPT Plus.

Students can learn more and claim the offer at: chatgpt.com/students/2026

Know a student who could use this? Share the offer with them.

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Learning with OpenAI: Courses and webinars

Teaching and learning with AI. Learn how to use AI to support your studies, teach your students, learn faster, and prepare for the future in two new courses on OpenAI Academy.

• AI for Educators: Plan lessons, activities, assessments, and communications using permitted materials.

• AI for College Students: Organize coursework, plan projects, review drafts, and prepare resumes or portfolios.

Help college students make the most of ChatGPT. Sign up for our new webinar series for faculty, trainers, and advisors to explore practical ways to help students learn, create, and review their work with ChatGPT.

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Wharton’s Stefano Puntoni on learning with AI, protecting attention, and knowing what you want before you open the chat.

Stefano Puntoni used to give his students a 20-page syllabus. Finding an assignment deadline meant searching through it. Now he makes that information available through a chatbot, so students can find what they need when they need it.

For Puntoni, that is a useful kind of effort to remove. The harder question comes when the effort itself helps someone learn. Working through a difficult problem, forming an idea, or checking an answer may take time precisely because it asks us to build understanding.

In this conversation, the Wharton professor explores how he makes those decisions in his own teaching and writing. He describes using AI to rehearse an assignment, keeping responsibility for his ideas before asking for an edit, and protecting the attention needed to judge the result. The conversation also looks ahead to a university that people return to throughout their working lives.

The Interview

Don: I’m here with Stefano Puntoni from Wharton School. Super excited for you to be here. Thanks for coming. Please introduce yourself, what you teach, what you’re working on, and the world that you inhabit right now.

Stefano: Thank you, Don. It’s great to be here. My name is Stefano Puntoni and I’m a professor of Marketing at Wharton School and the co-director of something called the Wharton Human-AI Research. And my work is on the psychology of human-AI interaction. At the Wharton School, I teach a course called the Psychology of Human-AI Interaction. So that’s basically the theme. And I think it’s such an interesting boundary, this one between technology and psychology right now.

Don: So why don’t we start by exploring the thing you’re most excited about right now? You’re involved in a lot of things, but there’s probably something top of mind that you like thinking about, working on at the moment.

Stefano: Right now, I think one project I’m very excited about is one led by a postdoc at the Wharton School called Ben Lira that looks at the impact on intergroup relations of digital twins and LLM-instantiated synthetic humans.

Basically, what we do is get people to interact with an out-group member and see whether they learn something about that population that they don’t know much about, and maybe they often stereotype and perhaps dislike, by talking to a synthetic version of that out-group.

For example, we find that Democrats and Republicans tend to like Republicans and Democrats more after they’ve had an opportunity to spend a few minutes talking to a bot impersonating a member of that out-group.

Don: That’s super interesting. And it’s fighting against the stereotype that we create for people. But I wonder, a digital twin, is that also a stereotype of what the person they’re representing is?

Stefano: In fact, it is. And we do see that in the data too, but it still helps. And in this case, we were looking at AI-based interventions that can help reduce political polarization. But you can imagine also commercial applications in the domain of marketing, for example.

I’m a marketing professor. I find it interesting to know how decision-makers in companies can learn about customers. And maybe one way that is opening up right now to learn about customers might be not to talk to customers, but to talk to computers, which is a weird idea. And we don’t know exactly how to do it and how well it works and when it’s appropriate. But it seems an interesting avenue, and I think there is some early evidence that there is value in doing that.

Don: And I think this is creating a little bit of space to start asking your potential customer more questions, right? Versus, “I know about them. They generally feel this. They want this.” Just to have a few questions, actually, and start engaging your mind in a different way probably helps, probably moves the conversation.

Stefano: And I think that is maybe even a special case of a much broader application of AI, which is to do a better job. Whether you are a salesperson—I know that many people in sales now would practice or do some kind of extra dry run with a chatbot impersonating a customer to understand how to pitch things.

The same for teaching. Now, many teachers do that; I certainly do. Whenever you have an assignment or you develop some new ideas, you want to know: Is it too complicated? Is it fluent? Is it working? Obviously, you want to test on people. But for the first cut, I find it quite useful to have a student bot instantiated through ChatGPT, where you can figure out some of the mistakes you make. And typically, we always make mistakes.

But that’s probably true in a lot of other walks of life, where you can maybe pilot or try things out before you encounter a real audience.

Don: I love that idea. Because even when you’re preparing a presentation, or you’re doing a keynote or something else, it helps to put the words in your mouth and get them out without even…

Stefano: Do you do that?

Don: Of course. Because it’s one thing thinking a thing, and it’s another thing saying a thing. And then the next level is saying a thing and having a reaction back. That is this progression. And this is perfect, takes us into the space of learning.

I was just at our AI Skills Jam for K12 Teachers. We do these enablement days of bringing teachers together and teaching them about the tools. And I met a new teacher, two years in, a middle school teacher. And the first years of teaching are hard, right? No matter what, whether it’s K–12 or higher education, it’s hard. There’s a lot you’re carrying. You’re building materials. You’re interacting with the class. You’re holding them.

And he just talked about testing ideas out, the same thing. He didn’t create a digital twin. But he said, “I test these ideas out. I get feedback. Is this too complicated? Will this support the range of students I have?” And as a second-year teacher, he said, “I feel more confident. I feel more prepared. I feel better situated to really be a teacher to my students.” And I’m curious how that fits in.

Stefano: Nice example. And I think that in the last six to eight months, I’ve seen a leap in some of the capabilities of these models that now can enable you to do things which maybe a year ago you wouldn’t be able to do. Certainly, I’m thinking about my academic work, the way that I now use it as a proofreader for my articles, or whenever I have it pretend to be a mock reviewer. That’s another audience that, as an academic, you care a lot about: the academic reviewers of your articles.

And ChatGPT Sol, for example, is very good. So it’s been very helpful in making sure that the work is a lot more polished and ready, in a way, than maybe it would have been before.

Don: And there’s also this weird line of, at what point does it feel like cheating? Am I too prepared? Do I have these amazing tools? Is it okay to do that?

Stefano: I think it’s a good point. One way I think about that is to say, every job has frictions. And some frictions are good, and some frictions are bad. And you want to get rid of the bad frictions. And you don’t want to make the mistake of making it easy for yourself in the ones that are good, that you should really put the effort in.

And so maybe that’s valuable advice for everybody to think about in your own line of activity, whether that is professional life or personal life: In this context, what frictions are good, meaning they are needed for the kind of outcomes that they care to obtain, versus bad frictions?

These are things that I’ve got to do, but they are chores. They are maybe things that actually are not core in either achieving the outcome or building my capabilities or signaling to anyone we care about what it is that we’re doing.

And so differentiating the two can sometimes be hard. So I certainly see that in teaching and learning. For students, there are certain frictions that are bad. So we want students not to have to struggle with certain elements of the learning process. And the easier we can make it, the better it is.

Don: And can you double-click on that? I want to spend a little bit of time here, because where is friction good, where is friction not? That’s arbitrary. There’s probably a great line.

Stefano: And you’re right. The difficulty is that maybe my bad friction is your good friction. So that’s one challenge. I think, to some extent, it is, in many situations, a personal choice to decide what parts of an activity are meaningful and worthwhile, and which ones maybe are not. And in many situations, it doesn’t have to be a right or wrong solution, just a different taste.

But probably in some situations, there are objective answers to that question, and learning might be one of them. So if I give students a thick, 20-page syllabus—believe it or not, I used to be doing that—and they have to find information about the deadline for assignment number five on week number seven, that’s bad friction.

It’s just bureaucratic nonsense in some ways, but of course, it’s part of the process. But now I can feed that to a chatbot, which I do for the students, and then they have the service that they can access to make sure that they get information that they need at the time they need it in the smoothest possible way. Now, that’s an example of a bad friction we can get rid of because of AI.

Now, the problem is also that good friction. So maybe you give them some homework, or you give them a take-home assignment. And now the tendency, the desire, is, “I want to hang out with my friends, and we’re spending all this time doing this complicated stuff. So let me get some help.” And even though you think you are being diligent because you’re checking the answers—just copy and pasting—the truth is that by removing that friction, you are making it harder for you to build some kind of deeper understanding of the problem. And so it does hurt learning.

So I think one challenge that we all have as educators, as parents, and as students, too, is, over the next few years, as the capabilities of our AI systems are exploding, to figure out how we navigate that decision about bad frictions and good frictions.

Don. I feel like this is such a core conversation. And that leads me to: What are the skills we should be building? I think that is the next step: the good frictions to build muscle, the bad frictions to get rid of the bureaucracy.

But ultimately, the good frictions are building skills and muscles for something. What are those things that we should also look at objectively, with that framework? Because we can go arbitrarily, or with the ones that feel good and ones that don’t. But there’s probably some bigger framework that we could go to for that.

Stefano: Yeah. So I was making the distinction between good friction and bad friction, and you were making this muscle analogy. You want the good friction because you want to work out. Then the next question becomes, which muscle do you want to build?

Don: That’s exactly right. So what are the muscles?

Stefano: I don’t know that there is a one-size-fits-all. There are probably directions you can point to. That’s something I try to do in my teaching: make students reflect on that “which muscle” question.

You can imagine a few. Some have to do with the value that human labor brings that might be distinct in a particular environment. There are some obvious cases. For example, people are always going to want to go and watch a basketball game. Probably not if robots are playing. There might be something about the human endeavor, competition, excellence, beauty that we appreciate in human skill and achievement, which can never be replicated by anything else. That’s one big domain.

But there is more. Certainly leadership. I think algorithms can manage, but can they lead? That requires role modeling, personal connection, motivation, inspiration, and ultimately, it may require human communication and persuasion, feeling personal connection and trust.

Beyond those softer, interpersonal skills, which I think are on everybody’s radar—we want to be as good as possible at doing those things nowadays—I think there may also be some things in the domain of cognitive capabilities and know-how that one can point to.

One of them is pretty obvious: proficiency at using these tools. I think most jobs in the economy will be touched by AI in some shape or form. In some jobs, it will be a small part; in some jobs, it will be a massive transformation. But wherever you lie on that continuum, it seems unlikely that many people can be successful in their line of occupation or related ones unless they learn to adapt and become proficient at using that tool.

There are basically two ways of thinking about that. One is to say, “I’m going to upskill myself through learning how to do this, be good at Codex or whatever.” We call that coping strategy “direct resolution,” where you have a threat to your competencies and maybe autonomy, and you are going to deal with that directly by saying, “I’m going to make myself the best possible complement to that technology.”

Another one we call “fluid compensation.” In that approach, what you’re doing is saying, “This is a part of a job that is going to be increasingly, for a variety of reasons, done through machines. Let me pivot and be more in this part of the job where machines are less likely to have much of a footprint.” Maybe because we don’t let them, even legally, or because the capabilities are not quite there. Maybe they will never be there. Who knows? And then say, “I am going to redirect.”

Imagine that I’m a content marketer. My job is to write content for a B2B company. Before, a lot of your time was spent writing the content. Now, increasingly, you might lean on AI agents to help you build the content. But the next question becomes, “What content is it that we need? What strategy do we put around that?” Maybe you pivot to the strategy piece.

This is an example of two different ways of thinking about upskilling and reskilling. I think probably most people are going to fit. Maybe for many of us, it’s going to be a combination of the two.

Don: If we want to survive. I feel like it’s a moving target, though, constantly moving.

Stefano: There’s a whole academic graveyard with papers that come out in preprint saying AI cannot do this. Then they never get published because six months later, they can.

Don. In the education space and in the work space, I imagine more than ever there’s going to be a constant need to pivot, to build new skills, to adapt to the new tools that are out there. If you’re doing the thing that the tool can do, that’s not helpful.

Stefano: That’s another muscle people need to build. It’s a bit of a tricky one, a squishy one, which is the ability to change.

I think change is hard for everybody. But it’s hard to imagine being successful in a world where technology is changing so rapidly unless you not only accept that change is happening—you cannot wish it away—but also embrace it and say, “I embrace this uncertainty because uncertainty brings opportunity, and it brings excitement, and maybe also some fun.”

Fundamentally, I don’t quite know how to teach people to accept change and embrace change.

Don: Especially when there’s not a choice either, right? I feel like this is now being imposed. These changes are just there and everywhere. It’s reactive, or you’re somebody who is looking for that adventure anyway, and that’s who you are.

Stefano: In that sense, I think the longer you’ve been doing something, the more difficult it is to adapt. There are a lot of conversations around jobs. Most of the concerns have been around entry-level positions.

You talk to college students all the time. You probably hear a lot of this about entry-level jobs and internships and things like that. Personally, I’m actually worrying more about the older people, the people my age, because I think for them to adapt might require building that muscle. With young people, they are natives. I see what our students do.

Stefano: It’s just amazing.

Don: If you’re older in the workforce, you build this experience and expertise, and it is a huge foundation. It reminds me: I was in Italy, as I mentioned, a couple of months ago, talking to a lot of professors. One of the conversations came up about a professor saying, “I have so much experience. I’ve decades of experience in this field. I’ve built great muscle, great experience. Actually, using AI is power.”

This is the opposite side of this argument: using AI is really helpful because I also have this judgment, experience to relate, to connect, to evaluate the responses AI is giving me.

But students coming in are fresh. They don’t have that depth. What are the skills, what are the muscles? They don’t have that judgment to bring in or that comparison to bring in.

Stefano: I think that’s a good point. One of the most interesting frontiers in behavioral science and social science around AI today is that question: if you have a population that is heterogeneous, variable in some traits and capabilities, and now you throw AI into the mix, what is that doing to the dispersion of the outcomes across that diverse population?

There could be two different results. The first is that the ones who were performing less well can catch up to the performance of the other ones. It’s basically reducing inequality of outcomes within that population. Now you have the entry-level person who can deliver reports at the level of a very experienced consultant or whatever.

On the other hand, you could have the opposite pattern, where to be really proficient at using that technology requires a lot of expertise. Those who do have that expertise now can run away.

It actually is a multiplier of your effectiveness. Then what we see in that situation is that inequality of outcome within the population will increase instead of decreasing.

In the literature, you can see both patterns, depending on the task, depending on the population. I don’t think we have a good grasp, as of today, of what factors make it go one way or the other. But understanding those factors is going to be very important for bringing that technology to the workforce. In every role, you’re going to have some kind of impact, and you want to understand and anticipate whether that is going to be increasing inequality or decreasing inequality. We don’t know yet.

Don: I think there’s that pressure. You have these tools to perform better, to create more polished products, to do things faster, to do more. There’s that pressure. I think the workplace understands that, and the expectations are higher. But if you don’t have that depth of experience, you’re bumping around the surface a little bit and relying more, potentially, on tools than someone who has a lot of experience and can do this hybrid thing, because they can bring a lot more to the table.

Stefano: I think that is taking two shapes. The first is in initialization, and the second is verification. Whenever experts are working with AI, the way I think about it is like a sandwich model, where you have the human at the beginning, the human at the end, and you’ve got a lot of AI in the middle. Sometimes the sandwich has a lot of bread and sometimes a lot of condiments. But basically, you have a combination of the two.

It’s always human and AI. It’s unusual that it is only AI or only human in knowledge work nowadays. To do the verification work, but also to do the initialization work, you’ve got to know where you’re aiming. You have to be able to judge quality. When the models are so advanced and the quality of the output is so hard to judge, it actually requires a lot of expertise to be able to say, “This is good,” or “This is bad.”

Don: And really careful scrutiny, too. These things don’t just pop up; they aren’t obvious. You have to have that lens of expertise.

Stefano: Which brings us to the other muscle that we haven’t talked about so much: the ability to focus attention. I think that is a problem. Everybody talks about the human in the loop, but there’s no use having the human in the loop if the human is asleep. You need to be sharp, and verification work is very hard work.

If you’re a coder, half of the time that you’re coding, you’re really going off memories, almost copy and paste. You daydream a little bit. There’s obviously knowledge work, but there are moments in which it is, “Okay, I need to initialize this routine or whatever.” It’s easy. With verification, you don’t really have that kind of downtime. It’s all full on. You do four hours of that, you’re spent. It’s very hard work.

I think we’re losing attention really at the moment when we need it the most, because work is becoming more intense. We are losing the capability.

Don: That’s good. We’ve been losing it for years, too. It’s the pace of the world and all the stimuli that are coming in. Those things have reduced, I think, attention span. But I imagine that’s out there. Now, like you said, when we need it most, we need to build that muscle again.

What do we do? I think about the skills we’re building for learning, and how do we help people use these tools? What are some of the approaches to do that? What’s that balance?

Stefano: Put the phone away. That would be a good one. And not just in general to cultivate attention. I advise our students to immerse themselves in long-form media, whether that is a thick book or a five-hour podcast. But do something you cannot consume in five seconds.

And learn to be patient and learn to be a little bored.

I think that’s one. Then we talked about the other ones. How do you nurture this acceptance of change so that it becomes an opportunity, not a threat? How do you build the right type of skills that make you a complement to AI technology? You don’t want to be a substitute for AI technology, because, in fact, AI technology is going to be a substitute for you. You want to be a complement. It should be human and AI together. How would you bring that multiplier?

Then that ability to inspire, connect with others, communicate effectively, be trusted. All those softer things we were talking about at the beginning.

Almost like an emerging framework, I think.

Don: That’s one of the things I think about. What’s that framework? What are those things that we hold true and are careful with? What are the other things that are more flexible?

I wonder, in your world, as an educator, as a researcher, where do you feel yourself leaning as you have this growing companionship? I see this as a partnership. More and more, I think about the partnership with AI. What does that partnership look like? What is the part you need?

If you think about the range of jobs somebody can do, like you said, it’s not a hard line. If I’m not a writer, and that’s not really part of my job, I might lean on AI to do my writing for me. Whereas if I’m a writer, that’s the muscle I want to build. It depends on who you are and what you do.

For you, what does that partnership look like now? How is it growing? Do you see a direction?

Stefano: I think I struggle like everybody else to negotiate that bad friction, good friction issue at the beginning. I sometimes find myself using AI in ways that I’m thinking, “If I do this a thousand more times, I won’t remember how to do this thing. And I maybe should.” Other times, there are skills I’m very happy to lose. Honestly, there are lots of things that, say, my parents’ generation knew how to do, which I’m perfectly happy not knowing how to do.

Don: Yes. How about remembering a phone number?

Stefano: Lots of stuff like that.

Don: Maybe that’s important.

Stefano: In a way, we are a little dumber in that way, but does it matter? Actually, no. We’re smart in some other ways. So I don’t worry too much about that. But, for example, I find that if I’m approaching a blank page and I have a chat with me to help me, I basically don’t think as much. So I made myself the promise that I will not write with AI; I will edit with AI. The edit could be very extensive. It could be actually writing most of the words.

But I need to know before I talk to chat: what is it that I want to get out of this? And what are the ideas? Because if I don’t do that, it’s too easy to find myself just leaning on it, and then I become a copy-paste machine, which is not the right thing.

Don: And AI can do that too. AI can copy-paste for sure. I’m having the same experience where, at the beginning, I was using AI a lot, and I was just building a lot of stuff. I was like, “You go do it, go do it.” I had an idea, maybe, but then it could take me in a left direction. I feel like I wasn’t paying attention.

Now, I literally will not sit down in front of my computer until I have an idea of: what is the work I need to do? What is most important?

Stefano: So what do you do? Do you actually draw on a piece of paper?

You do that? Like a diagram on a piece of paper?

Don: I do. I have big pads of paper. When it’s a more extensive thing, or I need to think things through, or I need to get all the stuff somewhere outside of my brain, I’ll just get a big drawing pad and start sketching stuff out.

Stefano: I actually do that too.

Don: Otherwise, it’s very easy to be taken for a ride. And so, coming in with intention.

Stefano: There will be some tasks where that is fine. It’s literally something you don’t want to do.

And then you are happy to outsource this to an AI system. But there will be things that you do want to do.

Don: And not losing your intention and purpose. I think that’s really important. And I think that maybe is the muscle too. What do you care about? What matters? What are you trying to do? What are you bringing to the table? Because you’ve got this superpower, you’ve got this whole army of power on your side, who’s ready to do the work for you.

Stefano: I like your use of the word intention. I think the biggest fear I have is that I end up wanting what the AI wants me to want. And I think you never want to let an AI system make those decisions for you. That’s something you want to keep almost at a moral level. It seems like something that we want to actually, if anything, do more of and say: what is right? What is wrong? What is appropriate? What is inappropriate?

That kind of judgment, you should get as good as possible at doing, because there’s so much more you can do with AI. I think having the decision, the good muscle, the moral muscle. And say, “Okay, if AI is an expansion of my capabilities, then there’s so much more I can do. And there’s also much more I should not do.”

Don: Because just because you can do it doesn’t mean you should. I like that reminder of AI being this amplifier. And if you’re not bringing anything to the table, it’s not amplifying anything. It’s just doing it. It’s like I dropped this seed, or speck of dirt. It will amplify that in a way that you didn’t intend. And so coming in with that experience, with intention, with purpose, is super important.

I want to shift gears a tiny bit, and we’ll probably come back to this. But I think about schools, the classroom, and K–12 and higher ed, and definitely K–12, where I think it matters across the board, whatever grade. Relationships and psychology are as important as subject matter expertise and everything else. You ask a K–12 teacher what the most important thing is, and they will say relationships. And you can’t replace that.

But it’s a very emotional time, a very psychological time. And AI, I think, there’s all sorts of reactions to this thing as coming either to take our jobs, or our purpose, or what we value, what’s important. Why is it so emotional? I feel like this is a space that you’ve probably looked at.

Stefano: We’ve never had a technology like this. We’ve never had a technology that can do things that are so humanlike. And because of that, it’s a mirror it’s holding to us. And so it’s a challenge in that sense.

Just to illustrate what I mean by this, if you went back maybe a decade or two ago and you were reading about human exceptionalism—what is different with humans?—people would talk about their cognitive capabilities, their prefrontal cortex thinking abilities. You do that now, and you don’t hear that story. You hear a different one, which is more about our emotions.

What technology has done is to change the reference for what is not human. And when you think about what makes humans special 20 years ago, you’re thinking about other animals, other living species. And when you think about what makes humans special today, you think about AI, because AI has become so humanlike.

And I think it’s a momentous change. I do believe very few examples of technology in the history of our species have that kind of depth of impact. And I think it’s so exciting to be alive today.

Don: And the crazy thing is, things are changing so quickly that we don’t know what next year is going to look like. We don’t know what a couple of years are going to look like.

Stefano: But what I do think is that AI is going to get better. In a way, AI then becomes an opportunity for us to actually pay more attention to those human things: emotions, well-being and connectedness, and the moral side of our lives, all those kinds of things. People are worrying about jobs, they’re worrying about instability. I think, ultimately, my hope is that this is just an opportunity to become more human.

Don: I love that. We can’t see the future, but how do we spend our time now? And in K–12 and in schools, I think that connection with students, you’re never going to replace that. And again, subtly guiding student learning, there’s an art to that. That is not just a logistical path that a system can build. There’s a real art.

Stefano: Yeah. In K–12 education, an inspiring teacher has a lifelong impact on a lot of people. It’s amazing what impact a person can have.

Don: These change the direction of the course of people’s lives. You ask most people, “How did you get here?” And often it’s a teacher. It’s one teacher, it’s a series of teachers, but it just takes one to literally change the course of your life. I don’t think that’s ever going to go away. We need to lean in on building those spaces where that happens.

And how about in higher education? What does the future look like to you in higher ed? I know that’s a hard one.

Stefano: I think things are in flux. There are a lot of big changes. Some of them have to do with technology, but there are also other changes. And so the sector, I think, faces some big challenges that have to do with funding, that have to do with globalization, that have to do with regulation.

And then you have technology that is disrupting the traditional on-ramp to professional classes, which our students used to take. So we have to think about how to support students. So many of them now try out startups and so on. Can we support them in those kinds of endeavors more than maybe we ever got used to?

Then you have the pedagogy itself in the classroom. How do we use or not use this technology? Again, bad friction, good friction, very hard to do. Assessment is a nightmare right now. I’m bringing up the paper-and-pencil exam.

There’s a lot of hard decisions like that. And then even the bigger picture, I think, long run, is really about having to shift away from this pipeline model that we’ve had for a very long time. Basically, where we take people at age 18, we teach them entertain them, mind them for a few years, and then off they go at 21, and we never see them again, to a world where, because of change—and hopefully this tends to embrace change—people will need to retrain and upskill and discover new passions and interests throughout their life.

And so this pipeline model: come in as a teenager, leave as an early adult, and then the university hopes that they maybe remember us and send us some money. I think we need to think about more flexible models, maybe executive education and certifications and other ways in which we can keep touching those lives and helping people throughout their careers. It’s not just a long goodbye, but it’s more like a tool. Universities are there to support you throughout your career.

Don: That’s amazing. And that’s a vastly different model, right? It is leaning into lifelong learning, really, and universities being actually a part of that whole journey, not just a moment in time and life and learning happens elsewhere. But there is potential for universities to be.

Stefano: Lots of questions. Who’s going to be doing that? Are you going to have specialized institutions that are going to serve that need, that are going to complement what traditional institutions do in the degree programs you’ve grown used to over many generations? Or the same institutions, because they have the infrastructure, the know-how, the connections, that actually are going to venture more into that lifelong learning, as you call it?

Don: And probably good for universities to do that too. That sounds like a really interesting model. Last question for you: what are you most excited about? Looking forward today, in the near future, what are you most…

Stefano: My course is in the third quarter of the academic year, so it starts right after Christmas. And my rhythm is that I start around now thinking about the next run, because I teach an AI course. Basically, every year is a new course.

And so I have now three months where I have this huge library that I collected in the last few months, and I keep collecting new academic insights, new reports, some nice examples. And I’m going to slowly craft those into a narrative that I’m going to deliver to the students in January. I find it’s a lot of work, but I think it’s extremely rewarding, an interesting thing to do. So I’m excited about that.

Don: Well, maybe I’ll stop by in January and sit in a little bit!

Thank you so much for joining today. This has been fascinating. I feel like we’ve just started the conversation versus covered it all, so maybe we talk again sometime soon.

This summer, OpenAI launched the Codex Summer Studio for Faculty and Researchers—a four-week virtual working series designed to unlock a fundamental shift in how higher education uses AI. Most educators already rely on chat to brainstorm, draft, or refine text. But the true leap forward happens when we move beyond quick Q&A to delegate substantial, complex work to AI: assembling interactive prototypes, navigating unfamiliar codebases, automating lab workflows, and organizing messy datasets. In this agentic model, AI handles the heavy technical lift, empowering faculty to lead, direct, and innovate.

We structured the four-week Studio to bridge this gap: starting with workflow essentials like navigating repositories, moving into goal-oriented tracks, building functional artifacts by week three, and finishing with sustainable, reusable tool integrations.

“Faculty brought incredibly specific, lived challenges: a key concept students struggled to visualize, a dataset that took weeks to clean, a routine administrative task that consumed every Monday morning,” noted Siya Raj Purohit, Head of Applied AI Programs, EDU at OpenAI. “The Studio gave them dedicated space and partnership to turn those everyday pain points into working solutions ready to test before the fall semester.”

What Faculty Built

Participants grounded their projects in domain knowledge they already understood deeply. The resulting prototypes were focused, highly practical, and ready for immediate testing in classrooms and labs.

At the University of Oxford, Matt Rattley teaches first-year biochemistry. One of the most challenging topics in their course is helping students visualize how atoms move and position themselves in three-dimensional protein structures. Static slides and textbook diagrams flatten the structures, while physical molecular models quickly grow unwieldy as molecules scale.

Using Codex, Matt designed a peptide geometry explorer featuring three synchronized views: a rotatable 3D molecular model, a view aligned along a chemical bond that students can manipulate, and a dynamic Ramachandran plot mapping those angles directly to protein structures. While setting up individual 3D viewers was simple, the real triumph was ensuring synchronization so that all three perspectives update simultaneously across shared angle data. Matt will integrate the explorer directly into their LMS this year.

When Matt shared the interactive prototype with recent course alumni, their feedback was immediate: having access to the tool during the term would have significantly deepened their understanding. Reflecting on the build, Matt noted that with beginner-level programming skills, creating a multi-view interactive tool like this on their own would have been nearly impossible without Codex.

Teaching & Learning: Making the Process Visible

At San Francisco State University, Niel Shahrasbi developed a self-guided project for asynchronous MBA students with non-technical backgrounds. Working with real-world financial data, students build an interactive dashboard, author an executive summary, create reusable AI prompts, and present a short video walkthrough. Beyond the technical build, the assignment prompts students to critique raw data, verify calculations, question biased rankings, separate factual evidence from assumptions, and reflect on what both they and the AI might have overlooked.

Niel’s key insight was shifting evaluation away from final answers toward assessing learning as an active process: how students frame challenges, test assumptions, identify analytical risks, and iteratively refine their work over time.

At San José State University, Jalal Sarabadani created TeamWeaver, an intelligent assistant that helps form student groups and suggest project roles. Initial prototypes focused primarily on automated grouping, but iterative testing refined the approach. Jalal discovered that evaluating schedule availability, commitment, complementary skills, collaboration styles, and role coverage was far more impactful than relying on rigid personality categories. He also realized that simply optimizing for a high overall class average could still leave individual teams severely unbalanced.

Today, TeamWeaver prioritizes strengthening the most vulnerable team first, provides clear rationale and trade-offs behind every group recommendation, offers alternative arrangements, and leaves final composition decisions entirely to the instructor.

Melinda Jackson, a Spanish lecturer at California State University, Chico, built an interactive Spanish 101 preparation platform for Integrated Performance Assessments (IPA). The site provides practice modules across interpretive, interpersonal, and presentational communication skills without giving away actual exam answers.

Early user feedback showed that students wanted immediate validation of their reading comprehension. Melinda collaborated with Codex to incorporate optional formative feedback directly into practice passages while keeping formal evaluation content secure. With the initial module proven, she plans to expand this resource model across her open educational resource (OER) curriculum.

At Stanford University, Hae Young Noh developed a hands-on introductory activity pairing physical circuits, Arduino boards, and motion sensors. Codex assisted throughout the setup process—streamlining installation, compilation, real-time visualization, and sensor recalibration. By lowering technical hurdles, Hae Young enables students to focus on high-level engineering decisions, experimental design, and data validation rather than getting stuck on syntax errors.

Research & Analysis: Accelerating Workflows with Verification

Hao Ding, a researcher at the Oxford-Man Institute of Quantitative Finance, created the ABL Facility Registry—a public open dataset detailing asset-based lending facilities extracted from SEC filings.

Extracting this data poses significant information-retrieval challenges, as critical financial terms are buried across complex SEC exhibits where standard keyword searches fall short. Hao’s automated pipeline screened 203,748 SEC EDGAR documents, routed 34,023 candidate filings to an extraction agent, and published 6,946 credit facilities across 4,223 corporate borrowers. Out of 199,137 published financial quotations, 198,555 (99.7%) were precisely mapped and verified back to their exact locations in source filings.

While the scale is impressive, the embedded verification checks are the real highlight. Hao versioned the extraction logic in GitHub, stamped unique hashes on every dataset record, tracked prompt modifications like code commits, ran same-document consistency checks, and continuously evaluated batch accuracy.

In a candid reflection, Hao shared a moment where an interrupted script silently discarded changes, leading him to briefly report a fix that hadn’t actually applied. That experience reinforced a core principle for AI-assisted research: always verify execution outputs before reporting findings.

Samantha Pay, a DPhil candidate at the Oxford Internet Institute, designed the Hermeneutic Algorithm Audit—a methodological framework for scholars integrating AI into qualitative and hermeneutic phenomenological research. The tool makes the role of AI transparent and open to scholarly critique without reducing qualitative analysis to a passive log. Samantha described the build as a collaborative partnership where human judgment consistently directs the analytical process.

Tenzan Araki, a physics researcher at Oxford, used Codex to stress-test a novel research idea in fault-tolerant quantum computing. Before generating a summary, Tenzan instructed Codex to ask him three clarifying questions about his hypotheses. Answering those targeted questions sharpened his research scope and produced a significantly stronger, more rigorous assessment.

Looking Ahead: Key Takeaways for Higher Education

The projects created during the Studio were as diverse as the disciplines represented—spanning interactive simulations, course websites, data pipelines, team optimization tools, and workflow automations. Yet, all shared a common working philosophy: focus on a real problem, ground the AI in domain materials, build small reviewable iterations, and refine based on authentic feedback.

This approach offers valuable lessons for student engagement. As AI tools make generating polished final artifacts easier, educators can pivot toward evaluating the learning journey itself: how students frame problems, select reliable sources, prompt systems, verify outputs, debug errors, and iterate across versions. Niel’s MBA assignments make this inquiry process explicit. Jalal’s TeamWeaver keeps human expertise central to consequential team decisions. Melinda’s Spanish site offers formative practice while safeguarding evaluation integrity.

The same principles apply to research and institutional operations. Research pipelines require transparent data provenance, clear methodology, and explicit verification. Administrative workflows require defined roles, clear boundaries, data privacy, and human approval before execution.

The Codex Summer Studio provided faculty with a structured sandbox to practice these skills on projects they know deeply. As these tools deploy across classrooms and research labs this academic year, our core measure of success remains straightforward: Do these technologies help students learn more deeply, assist researchers in verifying truth faster, and give faculty more time to focus on decisions that truly require human judgment?

At Chico State, the AI conversation is top of mind for faculty. Instructors are asking what needs to change now that AI can complete parts of the assignments they have relied on for years?

That’s the idea behind Chico State’s AI Retrofit program, an applied faculty-development effort led in part by Zach Justus. The name is deliberate. Faculty don’t have to rebuild their courses from scratch. They seek to understand what AI has changed, then adjust the courses they already teach so students are still doing meaningful work.

When we spoke with Justus, he described a program that has reached almost 200 faculty members at Chico State, close to 15 percent of the instructional faculty. It’s also one approach that will be leveraged by the entire CSU system this coming academic year.

Justus doesn’t start by asking faculty to take a position on AI. His approach is simpler: faculty can love it or hate it, but they still have to adapt to a new world.

The Interview

What problem were you trying to solve with AI Retrofit?

For the last three years, a lot of my faculty development work has centered around artificial intelligence. At Chico State, we support roughly 900 to 950 instructional faculty, so the question was not just how to introduce a tool. It was how to help a large and varied faculty think through what AI means for teaching and learning.

AI Retrofit is a very practical and applied approach to integration. It’s not tangled up with a lot of abstract theory. We get faculty together, ask them to evaluate how AI has disrupted what they traditionally do, and then give them practical tools and pathways to adjust.

We learned quickly that opening with “Let’s all get on board, AI is great” creates resistance. A better starting point is more direct: you can love it or hate it, but you still have to adapt. That lowers the temperature. Some faculty arrive skeptical and leave with a clearer sense of where AI might help their work.

How does the program work?

We run it off-cycle, in May after the semester and in January between semesters. That helps make the work more effective. If you meet once a week during the semester, people forget most of what happened between sessions and lose momentum. If you give them a focused one-week sprint, they can actually stay with the work.

We also pay faculty a stipend. It’s not a huge amount of money, especially for faculty with advanced degrees. But we care about their time. There needs to be explicit value and accountability.

The structure is important because in a shared governance environment, you can’t simply tell faculty what to do. You have to create the conditions where they can choose something that’s worth their time.

How do you start?

We show faculty how to use ChatGPT to complete an assignment, and then we ask them to have ChatGPT do assignments from their own classes.

That is a tough place to start. There is usually a lot of hand-wringing. People can be pretty upset for the first day or day and a half. But it’s also a wake-up call. They see, directly and personally, that something has changed.

The point is not to embarrass anyone or tell them their courses are broken. The point is to help them arrive at the conclusion themselves: things need to change. We have a series of exercises that help faculty reach their own conclusions.

What happens after faculty see that disruption?

We ask them to talk to recent graduates, employers, and people in industry before they start making changes. The answers vary by field. For computer science, everything has changed. For people running art studios, less may have changed. But that step helps faculty think beyond assignments and ask: what are we preparing students for now?

We also give them a simple matrix of possible assessment alternatives. For example, if someone used quick writes to test comprehension, what are other ways to assess reading comprehension? We break these options out by format, because what works in person does not always work in an asynchronous online class.

We also teach faculty to use ChatGPT as a thought partner and revision tool. My background is in communication. I don’t pretend to know what should happen in a chemistry class. The faculty member is the subject matter expert. Our job is to give them tools, examples, and pathways so they can figure out what makes sense in their discipline.

What kinds of changes are faculty making?

There’s a wide range. Some people move into technology-free classrooms or blue books. While I’m not sure that’s ultimately the answer, exploring what that looks like and how students respond is up to faculty.

More commonly, faculty are redesigning assignments in ways that make the work more multimodal, more process-oriented, or more connected to real practice. Some are moving away from papers and toward podcast interviews or oral presentations. Some are using social annotation tools like Perusall. Others are finding ways to evaluate the process of learning rather than only the final product.

That process focus matters. In fifth-grade math, we tell students to show their work. AI pushes us to ask a version of that question across many disciplines: what does the learning process look like, and how can we make it visible?

What does that redesign look like in practice?

Since our conversation, several AI Retrofit alumni shared examples of changes they have carried into their courses. Together, they show that “retrofit” can mean redesigning an assessment, teaching students to challenge AI, or building a course-specific tool.

Retrofit In Practice

Josh Trout: Video, field notes, and AI-generated questions

Josh Trout replaced written discussion posts with short video responses and redesigned a final exam around a scenario students must answer in their own words on video. He also built an assignment that requires AI use: students upload field notes, ask AI to generate critical-thinking questions grounded in those notes, choose three questions, and record a five-minute reflection. Students must document the prompts they used and the AI outputs they received.

Josh Trout’s redesigned discussion asks students to respond on video and engage with peers.

Katie Mercurio: AI as a sparring partner

Katie Mercurio, an associate professor of marketing and director of online programs in Chico State’s College of Business, is deliberately teaching students to argue with AI rather than accept its first answer. In her MBA Strategic Marketing course, students evaluate AI recommendations for strengths, limitations, and assumptions, then propose and defend an alternative the model did not consider.

Her emerging process makes AI an adversary or sparring partner:

• Students make their own recommendation first and identify the case evidence supporting it.

• They ask AI to attack the recommendation by surfacing assumptions, counterarguments, risks, missing stakeholders, or another course of action.

• They return to the case evidence and decide which criticisms hold up.

• They defend, modify, or abandon the original recommendation and explain why.

• They identify something that neither they nor the AI can confidently resolve with the available information.

For example, a student might tell AI, “Act as a skeptical CFO and give me the strongest reasons my recommendation could be wrong.” The student then has to decide whether those objections survive the evidence. Mercurio’s principle is that AI can make a claim, but students remain responsible for adjudicating it. She also keeps copyrighted HBR and HBP case materials out of the model; the AI interaction centers on students’ own analyses and recommendations.

Preston Farris: A custom GPT that explains and checks

Preston Farris, a lecturer in Computer Animation and Game Development, built a CAGD 295 Toolkit during the Retrofit and still uses it as a teaching tool. The custom GPT gives students a step-by-step explanation of what counts as an acceptable Sprint Kickoff or Simulation submission. Students can also upload screenshots of the work they plan to submit and ask the GPT to check them.

The tool has a known limitation: it can struggle to distinguish profile pictures from one story to another. Farris treats that as productive friction. Students have to verify the feedback rather than assuming the model is right. A false negative prompts another check; the tool is not intended to certify incorrect work as correct.

Farris introduces the toolkit in Canvas, explains the screenshot format, and flags the known profile-picture limitation.

The Canvas instructions show students how to capture the full board state.

Students choose the assignment they are checking and upload their board-state screenshots.

The toolkit explains the rules for a successful Sprint Simulation.

It can also explain the requirements for the Sprint Kickoff.

After a student uploads screenshots, the GPT identifies what appears correct, what needs attention, and what to fix before submission.

Logan Smith: Research, course helpers, and explicit AI instruction

Logan Smith assigned students a custom GPT research project in fall 2025 and also uses course-specific GPT “helpers” in AGRI 305 and ANSC 440. He provides the helpers with course materials such as the textbook, slides, study guides, syllabus, notes, and practice questions so students can ask for explanations tied to the class. His AgriGenetics Helper is available to Chico State users: AgriGenetics Helper. Two students, April Garcia and Isabelle Harger, also used the library’s podcasting studio and green screen to record a Herd Health Plan presentation.

Smith said the custom GPT project improved engagement and gave students direct instruction in appropriate AI use—training many students said they could carry into future jobs. His subject librarian, Michelle Mussuto, taught students how to research materials for the project.

David Zeichick: A lecture companion grounded in course video

David Zeichick is prototyping an overlay for Kaltura lectures that lets students pause a video and ask ChatGPT questions about the moment they are watching. Answers are grounded in the lecture transcript. Embedded quiz questions check understanding; when a student answers incorrectly, the companion explains why. The prototype is currently hosted in ChatGPT for Zeichick’s own use while he works toward an externally accessible version.

The Lecture Companion pairs a Kaltura video with a transcript-grounded question-and-answer panel.

Embedded practice checks pause the lecture and ask students to answer a question about the content.

When an answer is incorrect, the companion gives the correct answer and a short explanation before the lecture resumes.

Why does this kind of redesign matter?

So much of this comes back to helping students understand why they are doing what they are doing. If students can connect the work to a career outcome, a real-world problem, or something they care about, they are more likely to invest themselves in learning. Credential-seeking will always be part of college, but if we can reactivate even a little more of why students enrolled in the first place, our jobs get easier and their experience gets better.

How does industry connection shape the program?

It helps faculty see that this is not just an IT issue. Some universities treat AI as if it is an IT problem that needs an IT solution. But when faculty talk to graduates or employers, they see that this is a teaching and learning revolution. It is not just a question of opting into or out of a tool.

The workplace is changing, and students are entering organizations that are still figuring out what AI means. That gives students an opportunity to help shape what comes next, but only if their courses prepare them for that reality.

What is the broader lesson?

The program works because it starts with the course, not the tool. Faculty have to look at what has changed, but they also get time, examples, and support to make changes that fit their disciplines.

For institutions trying to support faculty around AI, the model suggests a few practical lessons:

1. Start with disruption, not adoption. Let faculty test their own assignments and see what has changed.

2. Protect focused time. A one-week sprint can build more momentum than scattered meetings during the semester.

3. Signal that faculty time matters. Even a modest stipend can communicate institutional seriousness.

4. Connect course redesign to the workplace. Graduates and employers can help faculty see what students now need.

5. Keep faculty as the experts. AI can be a thought partner, but faculty still own the disciplinary judgment.

6. Look for process, not just product. The best redesigns often make student thinking, collaboration, and iteration more visible.

Reflection

Justus made another point that’s easy to miss. Much of the useful AI work in education is happening quietly in classrooms. It rarely makes the national conversation, which sometimes focuses on cheating and failure. At Chico State, faculty are making changes, students are being asked to do more meaningful work, and the institution is learning by doing.

The transition is not easy. The program works partly because it does not hide that fact. It starts with discomfort, then gives faculty a way to work through it.

For higher education, the practical path may be to redesign courses so students still have to think, create, explain, collaborate, and show their work.

About Zach Justus

Zach Justus is the Director of Faculty Development and a Professor of Communication Arts and Sciences at California State University, Chico. His work can be found in Argumentation and Advocacy and Communication Teacher and other outlets. Zach has been writing, speaking, and working at the intersection of AI and education since the launch of ChatGPT. He has collaborated to produce Inside Higher Ed and EdSouce articles, several webinars, an ongoing blog/podcast/newsletter, and a series of conference and keynote presentations.

At Lynwood Unified School District, Superintendent Dr. Patrick Gittisriboongul uses ChatGPT to help manage a full inbox. It helps him triage messages and draft responses, which he reviews before deciding what to send. For Gittisriboongul, AI does not replace the human side of leadership. It creates more time for him to be present with students, families, and staff.

“I spend less time trying to go to inbox zero and more time with relationships and being present and visible,” Gittisriboongul says.

Christina Ordonez, technology department chair at Hoffman Estates High School in Illinois’s Township High School District 211, has spent 25 years in education across English, reading, library services, technology, theater, instructional coaching, and professional learning. Both districts initially focus their ChatGPT deployments on faculty, administrators, and staff so adults can learn to use the technology responsibly in their own work and guide students when questions arise.

Although Lynwood and District 211 start in different places, both build a foundation with staff by establishing guardrails, building shared language, testing bounded workflows, and measuring how practice changes.

Why districts need an AI strategy now

Gittisriboongul frames the urgency plainly: “The question isn’t whether AI is coming to education. It’s already here. The question is what are we going to do with it?”

“Every school district needs an AI strategy, and it needs one now—one that connects policy, responsible-use guidelines, family communication, capacity-building, and a way to measure impact,” says Gittisriboongul.

For Lynwood, waiting carries its own risk. Staff members already encounter AI in their work and in conversations with families. Without district-provided access and training, each person is left to evaluate the technology independently. Gittisriboongul connects that unevenness to equity: a shared foundation should not depend on who has the time, confidence, or personal subscription to experiment.

“We have to avoid two extremes: fear, where you simply block and avoid AI, and hype, where you rush without guardrails,” Gittisriboongul says. “There’s a third path: responsible innovation.”

Lynwood forms an AI task force around equity, safety, and ethics. Its three-year roadmap begins with governance, staff awareness, and responsible-use policies, then moves into controlled educator pilots and a broader plan for faculty and staff fluency. AI is also one of the four tenets of the district’s Vision 2030. The roadmap gives staff a fixed point even as products change: build adult capacity, protect human judgment, and prepare educators to lead through a shift already under way.

Ordonez recalls an earlier technology rollout. During District 211’s one-to-one iPad work, staff receive a wide menu of tools. The district eventually accumulates what one leader calls 17 PDF makers—different products solving roughly the same problem, with teachers and students expected to keep track of them. District 211 chooses ChatGPT for Teachers as a shared AI platform so training, language, and oversight do not fragment in the same way. “We wanted a cohesive, strategic plan that added AI literacy, fluency, and deep learning over just adoption of a tool,” Ordonez says. “We worked to create a vision for the efficient, effective, ethical, and equitable use of AI, all while keeping the human-in-the-loop.”

Make privacy part of the implementation

For both districts, privacy is a condition of participation. Gittisriboongul says Lynwood chooses a managed district environment because faculty and staff need data protection and administrative oversight as they experiment. He points to OpenAI’s agreement with the California Student Data Privacy Alliance as part of what makes the deployment possible. As of August, OpenAI has data privacy agreements covering 16 states.

ChatGPT for Teachers is built for managed school use. Data shared in the workspace is not used to train OpenAI models by default, and school and district leaders have administrative controls for managing access. Those protections give educators a district-approved place to work with lesson materials, communications, and operational documents. They do not remove the need for local rules about what information belongs in a tool or who reviews the output.

Lynwood joined OpenAI’s first K–12 cohort. The cohort model includes onboarding, training, office hours, feedback loops, and conversations with peer districts. Gittisriboongul says the cohort helps Lynwood define priorities and surface the questions behind its AI literacy framework: how to enable staff, protect data and integrity, engage families, and make the work sustainable.

“Hearing and learning from other districts has been very beneficial for us,” he says. Continued support matters as much as access because staff arrive with different levels of experience. The cohort also gives district leaders a place to raise implementation problems and show OpenAI where training or product support needs more work.

OpenAI now works with 103 K–12 organizations across 30 states. Together, those systems employ more than 300,000 staff members.

What the work looks like

Gittisriboongul and his team asked departments where work stalls, repeats itself, or begins with a blank page. They identified roughly 48 use cases across district administration, curriculum and instruction, budget and finance, human resources, and technology.

In his own work, Gittisriboongul dictates school-visit notes into a playbook that helps him follow up with thank-you messages. He maintains files in Markdown for his weekly Friday community updates and uses departmental input to draft agendas. ChatGPT can also turn 100- or 200-page interim budget reports into one- or two-page summaries. In each case, a staff member checks the draft against the source and owns what happens next.

At Marco Antonio Firebaugh High School, educational specialist Rex Wang uses ChatGPT to help write code for a Google Sheets app that tracks individualized education plan compliance, giving teachers a dashboard of approaching deadlines. He also uses it to disaggregate discipline data by grade, student subgroup, day of the week, and month. Wang estimates that ChatGPT reduces a benchmark-analysis task from seven or eight hours to about 45 minutes.

Firebaugh Principal Larry Reed describes English and math coaches bringing Smarter Balanced Assessment Consortium question stems, released questions, district assessments, and the district pacing guide into a lesson-planning workflow. The coaches use the material to draft lessons that connect several requirements, including SAT preparation. “It brought a lot of things together quickly and swiftly,” Reed says.

Dayrin Flores, an English language arts teacher at Firebaugh, uses ChatGPT to brainstorm how to combine lessons or address several standards at once. For a unit on Frederick Douglass, she uses ChatGPT to prepare a contemporary-language version of an older text as a first read, then returns to the original with her class. The tool gives her another route into the material; Flores still decides what students read, how they compare it, and what they discuss.

Treat enablement as a system

District 211 uses its collaboration with OpenAI to address a similar organizational problem. “Partnering with OpenAI allowed us to bring every member of our staff into one protected environment,” Ordonez says. The district secured more than 1,000 licenses for certified staff and administrators and treats the shared workspace as the beginning of implementation rather than proof that adoption has occurred.

Planning begins before the licenses arrive. District and school leaders, instructional coaches, and discipline administrators discuss how generative AI differs from search, where it can support teaching or operational work, and what responsible use requires of adults. Staff guidance and common language move through the district’s formal approval process.

The district builds an organizational AI continuum, a staff continuum, and a skills matrix informed by professional-learning standards and its strategic and equity plans. The matrix lets staff locate their current practice and see which skills come next. It also gives leaders something more useful than a login count: a view of where people need help.

Source: D211 AI Continuum — Organization

District 211 also created a prompting framework called CGPT: Context, Goal, Persona, Task. The acronym gives faculty and staff a shared starting point. It asks them to name the situation, the intended result, the perspective the system should take, and the specific work to be done—then decide what professional judgment they still need to supply.

Source: D211 C-G-P-T Prompting Framework

OpenAI’s enablement work gives educators more than one way to learn. OpenAI Academy is a free online hub that teachers and staff can return to for practical guidance. AI Skills Jams provide a live counterpart: educators and district staff work alongside OpenAI mentors on tasks such as lesson planning, family communications, and recurring administrative work. District 211 hosts a Chicago Jam, extending the hands-on workshop model Ordonez and her colleagues already use.

More than 140 professional-learning sessions follow across ethical use, multilingual learners, college readiness, classroom practice, and administrative efficiency. Training reaches beyond teachers and administrators. Librarians, maintenance staff, switchboard operators, and principal secretaries learn how ChatGPT can support their jobs and how to respond when students raise questions about AI.

In District 211, choir teacher Matthew Schlesinger uses ChatGPT to research musical styles and historical context, helping preserve rehearsal time for singing. Art teacher Jonathan Schaefer uses it for research, brainstorming, and feedback while protecting class time for painting and sculpture. Multilingual Learner Department Chair Mauricio Orozco uses ChatGPT to create sentence frames that connect to students’ backgrounds and strengthen their sense of belonging. Ordonez describes the time this work gives back as a return to “the soul of their curriculum.”

District 211 has also begun looking beyond training attendance. “We measured how people grew from just being aware to actually using AI with purpose,” Ordonez says. From the first year to the second, among more than 700 staff members surveyed, the share of staff identifying as “AI Engaged” rose from 22% to 66%. The district now uses its skills matrix to identify priorities for further professional learning as AI capabilities evolve.

“I wasn’t as integrated as I thought. I was experimenting, not progressing,” one staff member says. “Seeing how the Continuum was designed made everything finally click; this is how we grow,” another says.

A practical path for other districts

Lynwood and District 211 come from different histories, but their early moves form a sequence other school systems can examine: name the purpose, choose bounded adult workflows, set privacy and review rules, train people on a common platform, and measure where practice is changing.

One way to begin is with work a staff member already owns: turning a school-visit note into a follow-up message, reviewing a long budget report, adapting a lesson from district materials, or drafting a family communication. The employee compares the result with the source material, revises it, and remains accountable for the final work. A useful pilot makes that review visible rather than counting every generated draft as a success.

Governance and training move together in both districts. Lynwood’s roadmap keeps pilots connected to policy and community goals. District 211’s continuum and skills matrix show where professional learning needs to deepen. A shared platform and prompting language reduce tool sprawl, while office hours, peer learning, and repeated training give cautious staff a way into the work.

The evidence is still early, but both districts are building the conditions for responsible adoption: a clear purpose, privacy protections, sustained professional learning, and ways to measure how staff practice changes.

“Our purpose is to help our students be prepared for wherever their path in life takes them next,” Ordonez says. “By continuing to develop our staff’s knowledge and use of AI, we give them time and tools to support each and every one of our students.”

Learn more about OpenAI resources for K12 Education.

Sources and further reading

District 211 Resources

• Township High School District 211 website

• District 211 Guidelines for Staff Use of Artificial Intelligence

• Statement regarding AI in the District 211 handbook and syllabi

• District 211 Organizational AI Implementation Pathway

• District 211 Staff AI Implementation Continuum

• District 211 Staff AI Continuum Skills Matrix

• District 211 C-G-P-T Prompting Framework

• District 211 Professional Learning Menus

• Engaging Everyone: Building AI Integration Through Strategic Leading

Lynwood Unified School District Resources

• Lynwood Unified School District website

• Lynwood Unified Vision 2030

• Lynwood Unified Artificial Intelligence Taskforce

• Lynwood Unified AI Policy Lab announcement

• AI Spotlight: Lynwood Unified School District

• K–12 District-Level Perspectives on AI

• AI on the Front Lines in Primary and Secondary Education

A consequential moment for AI and education

It’s a new school year, and advances in AI are opening up entirely new ways to learn, create, and build. In just a few years, learners have moved from prompting text chatbots to working with systems that can speak naturally, turn ideas into working products, and collaborate across complex projects and assessments.

This creates a remarkable opportunity, but one of the most consequential research challenges in education to date.

Our greatest hope is that when used well, AI could give learners patient, personal support, help teachers reach students who might otherwise be left behind, and enable people to pursue ideas and problems once beyond their reach. There’s an opportunity to understand the conditions under which AI deepens curiosity, strengthens judgment, and builds lasting human capability.

But this future is not guaranteed. Research findings on the efficacy of AI for learning are early and mixed, and we’re paying attention to the risk that AI could shortcut learning and encourage cognitive offloading. We hear these concerns in conversations with school districts every day, amidst a wider reckoning with technology use for young people, from the return of flip phones to the current debate on screen-free classrooms. In addition, building the best possible models and products for learning is essential, but it’s not enough. Whether AI strengthens learning and builds lasting capability will depend just as much on the wider environment in which it is used: what education systems teach, value and assess; how educators, learners and families are prepared and supported; the norms, incentives and safeguards that shape its use; and whether every learner has a fair opportunity to benefit.

Why did we launch the Learning Lab?

These challenges cannot be solved by a better model or a single study. Addressing these challenges requires diverse data from across the ecosystem of learners, classrooms, cultures and education systems, alongside expertise spanning learning science, neuroscience, economics, sociology, policy and technology.

We’re standing up a network called the Learning Lab to support that effort, learning from independent researchers, educators, students and builders to ask shared questions, develop common measures, study what works across different settings and turn evidence into action.

The Learning Lab has three goals:

1. Build a sustained peer network around an ambitious shared research agenda and collaborations that persist across future model releases.

2. Share emerging findings to learn from one another, inform the product, and contribute new knowledge to the field.

3. Deepen understanding of frontier AI through hands-on enablement and access to OpenAI technical teams.

What did we learn in our first Learning Lab convening?

Last week, OpenAI’s Education team convened leading researchers, educators and builders from around the world at our San Francisco headquarters to set the frontier education research agenda and begin the collaborations that will carry it forward.

We kicked off with researchers from the University of Tartu, who took us inside Estonia’s national AI rollout and described their study of how AI affects cognition over time. Tartu and Stanford are working with OpenAI to develop new tools for measuring learning outcomes.

The gathering also opened new opportunities to collaborate, including research with Stanford’s Accelerator for Learning, participation in Oxford’s AIEOU global community of practice, and work on challenges from Cornell’s National Tutoring Observatory

We heard directly from students and teachers, including Ethan Truong, a member of the inaugural ChatGPT Futures class, who described how AI strengthened his sense of agency and urged the room to open new lines of inquiry here. OpenAI’s Economic Research team shared open-source tools and collaboration approaches. Professor Alfonso Gambardella and OpenAI Chief Economist Ronnie Chatterji also shared findings from a new Bocconi study.

The findings sparked a lively debate among economists and education researchers: AI improved the quality of students’ work, critical-thinking training increased originality, and students need both access and instruction.

Join the network

Good research takes time, and some of the questions we’re asking may take years to answer well. We’re committed working with—and amplifying—the researchers tackling this important work. Alongside longer-term causal studies, we’re also looking for studies and rapid proof points that can offer useful signals now. Along the way, we will make research tools, methodologies, and learnings more widely available.

If you are studying how ChatGPT affects teaching and learning, exploring these questions from creative and difficult new angles, and developing measures that are useful for the field, we’d love to hear from you.

Register your interest in the Learning Lab.

Our ambition is to help realize a future in which AI strengthens learning and builds lasting human capability. Through the Learning Lab, we hope to build the evidence that informs choices across classrooms, communities and education systems, guided by the needs of learners and the expertise of those who support them.

Resources

• OpenAI Research Index

• Model Spec

• OpenAI Researcher Access Program — API credits for eligible research

• ChatGPT for Academic Researchers — program information

• OpenAI Economic Research — research on AI’s economic and societal effects

• Learning Outcomes Measurement Suite

• OpenAI Forum — an expert community, events, and public resources

• OpenAI Academy — courses, events, and practical learning resources

Issue 5: September 1, 2026

In This Issue

• Feature: ChatGPT for Teachers expands to more U.S. school districts.

• Product Corner: Interactive quizzes bring quick practice into ChatGPT.

• Around the World: Lynwood Unified shows how districts can build trust, policy, and agency around AI.

• Try This: Two ready-to-open prompts for K-12 and higher education, plus a sticker idea.

• Offer: Free ChatGPT Plus for college students.

• Learning with OpenAI: Two new education courses from OpenAI Academy.

Feature: ChatGPT for Teachers Expanding Across the Country

ChatGPT for Teachers is expanding to 55 additional school systems across 20 states, reaching more than 100,000 additional educators and staff.

With this new group, OpenAI is now working with more than 100 K-12 organizations across 30 states to provide free access and training to more than 300,000 educators and staff. The expansion also includes a data privacy agreement across 16 states, giving districts a clearer framework for evaluating ChatGPT for Teachers against student-data requirements.

For schools, the work is not only about access. It is about helping educators build confidence, ask better questions, protect student data, and decide where AI can responsibly support the work they already do.

Read the full article

Product Corner: Interactive Quizzes

ChatGPT now makes it easier to practice with interactive quizzes. Students can ask ChatGPT to quiz them on a topic and answer directly in the conversation, with feedback along the way.

For educators, this can be a quick way to model retrieval practice, help students check understanding before a larger assessment, or turn class notes into a low-stakes review routine.

Prompt to try

Open this prompt in ChatGPT

Around the World: Lynwood’s AI Roadmap

Patrick Gittisriboongul, superintendent of Lynwood Unified School District, has framed AI as a question of agency: students should be early participants in the future, not late recipients.

Lynwood’s approach starts with trust. The district created an AI task force grounded in equity, safety, and ethics, then mapped a three-year path: first policies and listening, then teacher pilots, and finally student-created AI tools for tutoring, learning, and problem-solving.

One practical example is DistrictKit.AI, a set of free tools across district administration, curriculum and instruction, budget and finance, HR, and technology. The point is to keep educators in control while giving them more support for the work in front of them.

Watch the full Forum conversation

Try This: Seating Plans, Practice Scenarios, and Stickers

For K-12 teachers: plan the room before moving the furniture. A classroom setup can shape movement, attention, collaboration, and routines. If you have dimensions, furniture, fixed features, and photos, ChatGPT can help compare possible layouts before anyone starts moving desks.

Prompt to try

Open this prompt in ChatGPT

For higher education: build a short practice scenario. Use ChatGPT to turn a course goal into a quick rehearsal students can try before a discussion, lab, oral exam, advising conversation, clinical case, or project review.

Prompt to try

Open this prompt in ChatGPT

Bonus idea: classroom stickers. Use ChatGPT to draft short, printable sticker copy for the first few weeks of school: effort, revision, curiosity, collaboration, and responsible AI habits.

Prompt to try

Open this prompt in ChatGPT

Offer: Student Offers for ChatGPT Plus and Codex

U.S. college students who don't have ChatGPT EDU provided by their campus can claim four free months of ChatGPT Plus and experience the power of ChatGPT Work through Oct. 31, 2026.

Read about the ChatGPT Plus student offer

Learning with OpenAI: New Academy Courses

Two new Academy courses: Mark your calendar. Two role-specific education courses launch in OpenAI Academy on September 9.

• AI for Educators helps educators use permitted course materials to plan lessons, create activities and assessments, add support or challenge, and adapt communications for students, families, or colleagues.

• ChatGPT for Higher Education Students helps students organize coursework, plan group projects, review drafts, and turn experience into material for resumes, portfolios, applications, or interviews.

Browse OpenAI Academy courses

See upcoming OpenAI Academy events

Have a story idea?

We want to hear how educators, students, and school communities are using AI in practical, responsible, and creative ways. Reply to this email with a story, classroom example, question, or idea for a future issue.

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Meet Andrew O’Malley, Doyle Srader, and Dr. Carlos Castañeda.

Dr. Andrew O’Malley gives medical students more patients to learn from

At the University of St Andrews, Dr. Andrew O’Malley wanted medical students to practice history-taking, clinical reasoning, and empathy more often than the timetable allowed. Standardized patients are invaluable, but they also require people, rooms, coordination, and money. Students may see only a narrow range of cases before a placement. O’Malley’s SimPatient project gives them another place to rehearse—not instead of meeting patients, but before those encounters carry real consequences.

SimPatient uses generative AI models from OpenAI to power a virtual patient. A student can choose a case, ask questions, follow clues, and work toward a diagnosis while the model stays within the patient’s history and responds to the student’s approach. A chest-pain case, for example, can require the learner to notice which follow-up questions matter, respond to emotion, and separate a plausible diagnosis from a premature one. The system can also present rare or complex scenarios that a student may not encounter during a short clinical placement.

The useful design choice is what happens after the conversation. SimPatient gives immediate, individualized feedback on questioning, clinical reasoning, empathy, and the proposed diagnosis. Students can review what they asked, notice a missed clue, and try the case again with a different approach. O’Malley grounds that loop in experiential learning and deliberate practice: attempt, feedback, adjustment, repetition. The interface can keep cognitive load manageable for beginners, then introduce more ambiguity as learners become capable.

Make this your own

You don’t need a full platform. Start with one learning objective, such as taking a focused history for abdominal pain. Write a case brief with facts the patient reveals only when asked, details the AI must not invent, and clear escalation rules. Give students a short rubric before they begin, then collect the transcript and a reflection naming one missed clue and one better next question. Keep the simulation inside the educational scenario, state that it is not medical advice, and route consequential judgment back to faculty.

Dr. Doyle Srader moves conflict practice out of the spotlight

At Bushnell University, Dr. Doyle Srader teaches conflict resolution—a subject that almost demands rehearsal. Traditional role-play can work against the lesson, though. Nervous laughter, fear of looking foolish, and uneven acting may take over. Students end up managing the performance instead of practicing de-escalation.

Srader uses ChatGPT’s voice mode to lower those stakes. He starts with carefully selected disputes from The Guardian’s “You Be the Judge,” which include enough detail to support two plausible perspectives. In one memorable case, Edward wants his wife Alice’s siblings to stop using his toothbrush. Srader uploads the case to ChatGPT, asks the model to play Alice, and lets the student take Edward’s side. The specificity gives the voice conversation something real to work with.

A student enters the ChatGPT conversation and tries to move the dispute toward resolution. Voice mode maintains the other person’s perspective while responding to the student’s tone and tactics. Srader can also adjust the prompt so the character becomes defensive, verbally aggressive, or caught in one of the reciprocation patterns discussed in class. Students can pause, restart, and repeat without asking a classmate to absorb their first attempt. That privacy creates room to test language that may feel awkward at first.

Srader is developing the exercise into an assessment. Students choose three cases, but he does not tell them exactly how he has configured ChatGPT’s behavior. A rubric asks them not to escalate, to adapt to what the character says, and to work toward a collaborative rather than distributive solution. The transcript or recording becomes evidence of performance. Students can point to the moment the exchange changed, explain what they noticed, and say what they would try with a real person.

Make this your own

Choose one real-world dispute with no safety or privacy risk. Give the AI a role, a motivation, two non-negotiables, and a rule not to settle too quickly. Give students three target behaviors and ten minutes. Let them run it twice, changing one tactic on the second attempt. The value is not perfect imitation. It’s the chance to rehearse privately, then debrief what worked, what felt unrealistic, and how the same words might land with a real person.

Dr. Carlos Castañeda expands counseling rehearsal without confusing practice for care

Dr. Carlos Castañeda saw a similar bottleneck in counselor education at St. Edward’s University and Palo Alto University. Students need repeated exposure to difficult conversations, but faculty cannot provide a live client for every practice session. Peer role-play helps, though classmates may know the framework too well or hesitate to surface a complicated concern. Castañeda configured an AI Teaching Adjunct as a custom GPT in ChatGPT so students could practice between classes without mistaking the exercise for actual care.

The custom GPT provides simulated counseling clients whose needs match the learner’s level. Undergraduate students begin with common college concerns such as homesickness, low motivation, or academic stress. Graduate students can encounter more ambiguous cases, diagnostic questions, and cultural-competency checks. Students start in text chat, then progress to ChatGPT voice conversations later in the term. Saying “simulation over” ends the role-play and prompts structured feedback on strengths and areas to improve.

The workflow has four parts. First, Castañeda defines the client profile, presenting concern, boundaries, and facts the custom GPT may disclose. Second, the student conducts a time-boxed session. Third, ChatGPT produces a transcript and rubric-aligned feedback. Fourth, the student submits the record for faculty review. Castañeda is explicit that the model’s score is advisory: he reads the transcript, adds his own feedback, and assigns the grade. The OpenAI tool creates more opportunities to practise; it does not become the supervisor.

Castañeda’s guardrails are central to the design. The simulated client should never be presented as real care, never advise an actual person in crisis, and never invite students to upload identifiable client information. The custom GPT stays in character until the exercise ends, avoids crisis-level scenarios unless an instructor deliberately assigns one, and directs real concerns back to a professor, therapist, or other qualified professional. He also builds in culturally responsive and neurodivergent-affirming language and keeps final judgment with the human educator.

Make this your own

Use one fictional client and one micro-skill, such as reflecting emotion before asking the next question. Have students complete the same scenario twice. Between attempts, they review the transcript, mark one place where they moved too quickly, and rewrite that response. Faculty can compare the two interactions instead of grading a single polished performance. Across medicine, conflict resolution, and counseling, the goal is the same: one more safe, structured try before the stakes are real.

About the educators

Dr. Andrew O’Malley is a Senior Lecturer at the University of St Andrews School of Medicine, where he leads the Division of Education. His work focuses on bringing generative AI into medical education, including SimPatient, an AI-powered simulated-patient platform.

Dr. Doyle Srader has taught communication at Bushnell University since 2007 and has received four campus-wide teaching awards. He has taught conflict in American and Japanese classrooms and at young-professional conferences.

Dr. Carlos Castañeda, PhD, LPC, NCC, is an Assistant Professor of Psychology at St. Edward’s University, owner of The Missing Peace Clinic, and founder of ThinkAITA LLC.

Watch Andrew O’Malley on OpenAI Academy.

Watch Doyle Srader on OpenAI Academy.

Watch Dr. Carlos Castañeda on OpenAI Academy.

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Nearly 200 people enrolled in ECIM 504: Introduction to AI for Everyday Life or joined its waitlist. Dr. Renah Wolzinger knew a standard course page wouldn’t be enough. Her students at Saddleback College’s Emeritus Institute needed a place that felt clear, useful, and easy to enter.

So she built the AI Project Studio with ChatGPT Work. The website gives adult learners a set of small projects they can use to try AI without needing to know where to begin.

A student might plan a trip, shape a family story, organize a week, explore music, research a topic, or find a way to help in the community. Every project comes with a starter prompt, a short set of steps, and a reminder to pause and check the work.

Building around the students

Dr. Wolzinger started with a plainspoken request:

Dr. Wolzinger also uploaded the syllabus and shared a general description of her students, so the website could reflect the actual course rather than a generic introduction to AI.

Between the prompt and the syllabus, ChatGPT had the basics:

1. Who the students were

2. What the site needed to help them do

3. Where the website fit in the course

4. How she wanted the experience to feel

5. The syllabus and other course material

ChatGPT used that material to sketch the first version. Dr. Wolzinger didn’t need to map every page before she started; she could look at a draft and decide what belonged.

Choosing the look and feel

Before building the site, ChatGPT showed her a few visual directions. Dr. Wolzinger chose Warm Creative Studio. It felt right for a room of adult learners who would arrive with very different levels of comfort with technology.

The finished page has room to breathe. Projects are easy to scan, the language is plain, and the next step is always visible. At the top, students see a simple invitation: “Turn curiosity into something you can use.”

Making each project useful

Dr. Wolzinger kept returning to one question: What would a student be able to make here?

She used five questions to shape each project:

1. What will the student make?

2. Which tools might support the project?

3. What prompt could help the student begin?

4. What are the three basic steps?

5. What responsible AI question should the student consider?

The weekly planning project, for instance, begins with this prompt:

Before students use it, the site reminds them to leave out account numbers, medical details, and other people’s information.

With those pieces in place, an idea becomes something a student can actually try in one sitting.

Keep what works

The first draft was useful, but its look felt more restrained than Dr. Wolzinger’s creative class. She asked for one change:

ChatGPT made several paper-collage illustrations. Dr. Wolzinger kept three, each tied to a project category:

1. Create and Tell

2. Plan and Organize

3. Explore and Learn

Books, music notes, calendars, maps, clocks, and travel landmarks make the categories easy to recognize. Each image also has alt text for visitors who use assistive technology.

The illustrations help students scan the page and find a place to start.

Dr. Wolzinger also gave ChatGPT a boundary: “Keep the current site.” She wanted the visuals to change, not the project cards, instructions, or safety guidance. That kind of direction matters in revision. It tells the tool what to leave alone.

Putting responsible AI in the work

Safety guidance sits inside the projects, close to the moment when a student will need it.

The reminders change with the task:

• Confirm current hours and prices through official travel websites.

• Ask permission before sharing another person’s story or likeness.

• Avoid imitating living artists.

• Verify claims against original sources.

• Protect passwords, account details, and security codes.

• Consult qualified professionals before making medical, legal, financial, or safety decisions.

The site groups those reminders in a short checklist called SAFE:

• S — Stop and notice. Pause when a result creates a strong reaction or requests private information.

• A — Ask what is missing. Look for missing context, perspectives, sources, and accessibility needs.

• F — Fact-check. Verify important information with current, trustworthy primary sources.

• E — Evaluate the impact. Consider privacy, bias, ownership, transparency, and who may be affected.

SAFE gives students a way to slow down and look again. Four questions are easier to carry from one project to the next than a long list of rules.

A simple rhythm for learning

Every project follows the same four steps:

• Ask. Describe the goal, audience, and constraints.

• Create. Generate a starting point and personalize it.

• Check. Review facts, privacy, accessibility, and fairness.

• Reflect. Identify what worked, what changed, and what should be improved next time.

The pattern leaves room to experiment while keeping students in charge. They still decide what to keep, what to question, and what to change.

Prompts students can make their own

The starter prompts help students begin and then make the work their own.

Writing and Communication

Family Storytelling

Image Creation

Research and Media Literacy

Reflection

Most of these prompts ask for context before asking for an answer. Some call out uncertainty or tradeoffs; others ask students to reflect on what they contributed and what they would change.

How to build a version for your course

Dr. Wolzinger built the site in rounds. She brought the course materials, reviewed what ChatGPT made, and decided what to change next.

For an educator who wants to try the same approach, the work might look like this:

1. Describe the students first: what they know, what they need, and what might make them hesitate.

2. Share a syllabus or course outline. Real course material gives ChatGPT something solid to work from.

3. Ask for a rough first version and two or three visual directions.

4. Read the result as a student would. Is the path clear? Can someone finish an activity and leave with something useful?

5. Change one thing at a time—the steps, the tone, the images, or the safety guidance.

6. Keep the images that help students navigate. Write alt text for the ones that carry meaning.

7. Tell ChatGPT what not to change.

8. Try one project from beginning to end before sharing the site with students.

The first prompt gets the process moving. The real work comes in the revisions: keeping what works, naming what doesn’t, and asking for a specific change.

A prompt to get started

Here’s a starting prompt. Replace the brackets, then attach a syllabus or course outline if you have one:

After the first draft, a short follow-up prompt can be enough:

What Dr. Wolzinger made

The AI Project Studio reflects a series of choices by Dr. Wolzinger. She decided what would help her students start, what needed another pass, and which safeguards belonged beside each activity. ChatGPT Work helped her turn those decisions into a website. Her teaching judgment is visible throughout.

This fall, nearly 200 learners will arrive at a site built with them in mind. It gives them a clear first step, then room to experiment.

Explore the AI Project Studio

Bio

Dr. Renah Wolzinger is a professor, creative professional, and professional musician. For more than 20 years, she has taught and led programs in California’s community colleges. Her work focuses on practical, accessible, and responsible uses of AI in teaching, workforce education, and the creative arts. She is also the technical director at Eduvators and an active creative practitioner.

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“Student voice” can sound abstract until a learner has to speak in a seminar, write an official document, or defend a creative idea. In those moments, the barrier is often not a lack of ideas. It is the risk of expressing them before they feel ready.

Below we share the work and custom GPTs of three educators. Mudit Sharma, Nurullah Gungor, and Elettra Fiumi use AI as a private rehearsal space. Their designs give themselves and students structure and feedback while keeping the final act of expression—and the judgment behind it—human.

Mudit Sharma: Rehearsing the seminar before the room fills up

At the Harvard Graduate School of Education, Mudit Sharma wanted to prepare for inclusive seminar discussions. An instructor can design a engaging question and still discover that the conversation favors the quickest or most confident voices. Anticipating how different students might enter the discussion takes time, especially when a class includes varied experiences, language backgrounds, and levels of familiarity with the topic.

Sharma built a custom GPT that acts as a panel of simulated student voices. Each persona has a defined perspective, prior knowledge, participation style, and potential concern. Before class, Sharma tests a discussion prompt with the panel. One persona may misread a term, another may connect the issue to practice, and another may hesitate because the question assumes background knowledge. The responses are not predictions of actual students. They are prompts for the instructor to notice possible access points and barriers.

That distinction is important. The tool doesn’t caricature demographic groups or claim to represent a real student. Useful personas are grounded in learning conditions: a student new to the concept, a student with relevant work experience, a skeptical student, or a student who prefers time to formulate an answer. The instructor uses the range to improve the plan, not to label people in the room.

A practical workflow takes fifteen minutes. Enter the learning objective, the assigned material, and the main discussion question. Ask four personas to respond briefly, then ask the system to identify where the wording may exclude prior knowledge, where a follow-up would deepen reasoning, and which participation structure could widen access. Revise the question and prepare two entry points: a low-risk individual response and a deeper group prompt.

During class, the AI panel disappears. Students respond as themselves. The instructor can begin with a written minute, pair discussion, or anonymous poll before opening the full seminar. Sharma reports that this preparation can cut planning time roughly in half while helping quieter voices enter the conversation.

The value is not simulated diversity. It is better instructional anticipation. AI creates a fast rehearsal, and the teacher decides how to design the real room with more care.

Nurullah Gungor: Teaching official writing through guided revision

At Ankara Medipol University, Nurullah Gungor teaches a practical form of writing with little room for ambiguity: the official petition. Students must develop a request clearly while following institutional conventions, regulations, and templates. A generic chatbot can produce a polished-looking letter that uses the wrong form, omits required information, or invents a rule.

Gungor’s approach combines prompt training with an AI environment (a custom GPT) grounded in the relevant regulations and examples. Students don’t ask the system to “write my petition” and submit the result. They move through a cycle: identify the purpose and audience, draft, compare the draft with the requirements, revise, and make the final editorial decision.

The grounding material gives the feedback a stable reference. The custom GPT can ask whether the addressee is correct, whether the request is explicit, whether the supporting facts are ordered logically, and whether required elements appear in the expected form. Students then compare the suggestion with the source rather than treating the model’s confidence as proof.

Every week, students prepare a petition with ChatGPT. For example, they might be asked to write a petition to a district governorship about opening a library. Gungor provides them with more than ten prompt examples to use after they have written a draft, such as, “Revise the petition I provide according to the official correspondence regulation. Simplify the language, shorten sentences, but keep the legal meaning, show missing parts, and give suggestions.” In class, we discuss both the student’s and ChatGPT’s versions, making the learning interactive and practical.

Gungor reports meaningful practical gains: errors fell by about 60 percent, and average completion time dropped from roughly 40 minutes to 15. Those figures should be presented as results from his context, not a universal promise. The more sustained outcome is that students learn a repeatable process for high-stakes institutional writing.

Keep the guardrails plain. Use public or instructor-created scenarios, not a student’s sensitive personal case. Do not let the AI invent policy. Require a final human check against the official source. With those boundaries, the tool becomes a revision coach that helps students express a real request accurately and efficiently.

Elettra Fiumi: Giving creative work room to grow

Elettra Fiumi works across AI cinema and creative education at institutions including Locarno, NABA, Franklin University Switzerland, and LAC. Creative students often face a different expression problem: an early idea may be too fragile to present, while a general-purpose chatbot may flatten it into familiar language. The challenge is to support experimentation without substituting the model’s taste for the student’s.

Fiumi creates module-specific custom GPTs for different stages of filmmaking, including script development, storyboarding, and editing. Each tool has a bounded craft role. A script assistant may question character motivation or scene purpose. A storyboard assistant may help translate an emotional beat into visual options. An editing assistant may ask what rhythm or point of view the cut should preserve. The student remains the director.

Students also build their own assistants around a project. That creates continuity across modules: the system can retain the project’s premise, aesthetic constraints, vocabulary, and unresolved questions.

A practical pilot can begin with one stage of the creative process. Ask students to write a one-page project brief containing intent, audience, references, constraints, and three choices that define the work. Give the custom GPT a critic’s role: ask questions, offer two contrasting options, and never produce a final scene or shot list unless the student requests a limited example. Require students to keep a decision log showing which suggestions they accepted, rejected, or transformed.

The private interaction can matter for confidence. Fiumi describes a hesitant student using AI to work through ideas before presenting them. The tool did not supply the voice; it gave the student enough rehearsal to bring that voice into the room. Faculty critique and peer response then operate on a more developed idea.

Summary

Sharma, Gungor, and Fiumi use AI at three different thresholds: before speaking, while revising, and during creative exploration. The practical pattern is consistent. Give the student a private first attempt, provide structured feedback, and create a clear handoff to a real audience. The technology should make expression more possible—not make the expression less theirs.

Learn more.

• Mudit Sharma - Video

• Nurullah Gungor - Video

• Elettra Fiumi - Substack

Issue 4: August 12, 2026

Welcome back to The Edu Prompt, a field guide for learning, teaching, and building with AI from OpenAI Education.

This week is about what happens when educators move from “what can AI do?” to “what can I build and try by Friday?” We’re looking at OpenAI Academy Skills Jams, new education plugins, a faculty-built course example, and a few practical ways to get ready for back to school.

In This Issue

• Feature: Skills Jams with OpenAI Academy.

• Product Corner: New education plugins for ChatGPT Work and Codex.

• ChatGPT Work in the World: Robert Voss builds an interactive American West course.

• Back to School Idea: Write the AI rules before the assignment starts.

• Learning with OpenAI: Upcoming Academy sessions.

Feature Story: Skills Jams with OpenAI Academy

K-12 teachers, staff, and district leaders have been joining OpenAI Academy Skills Jams across the country this summer. The format is deliberately practical: educators learn with OpenAI mentors, district partners, and peers, then leave with at least one ready-to-use AI workflow, prompt, or classroom idea.

One pattern we’re seeing: small, shareable ideas travel well. Educators are turning everyday work into simple prompt cards with three parts: the idea, the prompt, and how it helps. Another pattern: teams are using the Jams to move from curiosity to implementation, including lesson planning, family communication, administrative workflows, and first steps with ChatGPT Work.

Here are a few ideas that surfaced:

• Use assessment data to form student groups and plan interventions
  - Lindsey Thames, fourth-grade teacher

• Make grade-level texts more accessible for students with learning differences - Ava Morton, 8th-grade special education teacher

• Translate complex classroom materials for multilingual families and students - Antonio Ellis, STEM coordinator

• Simulate hard-to-visualize science concepts and labs - Karl Craddock, chemistry and physics teacher

Read more about who we met at each Jam and what they’re building.

Phoenix, AZ • San Bernardino, CA • Chicago, IL • Orlando, FL • Clayton County, GA • Fairfax County, VA

Product Corner: New Education Plugins

Teaching and learning are high-context work. A useful lesson, study plan, or course activity depends on the right materials, audience, and goal.

That’s why OpenAI launched three education plugins for ChatGPT Work and Codex: K-12 Educator, College Educator, and College Student. Each one brings together role-specific skills, apps, instructions, and common workflows so educators and college students can get started without writing complex prompts from scratch.

Watch the launch video and learn more about the new education plugins.

ChatGPT Work in the World: Building a Course You Can Inspect

Dr. Robert Voss, Associate Professor of History and Director of the MSEd in Teaching History program at Northwest Missouri State University, used ChatGPT Work and the new College Educator plugin to build two interactive websites for a 15-week American West course: one for faculty and one for students.

The faculty site turns the course into a reusable teaching lab with a full course structure, lecture decks, Canvas-ready materials, assignment architecture, and validation reports. The student site gives learners guiding questions, assignments, AI-use boundaries, and a syllabus assistant grounded only in the published guide.

The first output looked like a solid traditional course, but he wanted to push it further. He revised toward a design where students can use AI, but the graded work still depends on in-class reasoning, evidence, and judgment.

Explore the faculty course lab and the student course companion.

Back to School Idea: Write the AI Rules Before the Assignment Starts

Before students ask “Can I use ChatGPT for this?”, try writing assignment-level AI guidance. Keep it short, specific, and tied to what you actually want students to practice.

Try this: Choose one upcoming assignment and define what AI can support, what students must do themselves, what they should disclose, and how you will give feedback.

Prompt:

Act as a thoughtful teaching partner. 
I am preparing for the first weeks of class. 
Help me create clear, assignment-level AI guidance for one upcoming task. 
Ask me for the assignment, learner level, course goals, and what students should practice. 
Then draft: 
1) what AI use is encouraged 
2) what is not allowed
3) what students should disclose
4) one reflection question that keeps the educator responsible for final review.

Open this prompt in ChatGPT.

Learning with OpenAI: Upcoming Academy Sessions

OpenAI Academy has several short, practical sessions coming up for teams that want to build stronger AI habits at work.

• 08.12 Codex bootcamp 301: Advanced automation

• 08.13 Skill Lab: Building sites and dashboards

• 08.13 ChatGPT Work for finance teams

• 08.20 ChatGPT Work for data analytics teams

Browse all upcoming OpenAI Academy events.

Have a story idea?

We’d love to hear how you’re using ChatGPT, Codex, or OpenAI Academy in your classroom, campus, district, or daily work. Reply with a short note about what you tried, what changed, and what others might learn from it.

One easy ChatGPT Work idea for daily life: Ask ChatGPT Work to turn a recurring task, like planning meals, prepping for a trip, tracking house projects, or organizing a weekly family schedule, into a simple checklist or mini app.

Thanks for reading. If this issue gave you something useful to try, share it with an educator, staff member, or team who might use it too.

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Today, the National University of Singapore (NUS) and OpenAI announced a strategic collaboration to expand how students, educators, researchers, and staff can learn, build and lead with AI.

The announcement comes as new research* shows that around eight in ten university students in Singapore believe AI skills will give them an advantage in the global workforce (81%), and be essential to their careers (77%). The findings also point to an ambitious, optimistic generation eager to shape its future with AI.

In a survey of over 500 university students exploring AI habits, career aspirations, and appetite for innovation, 94% said they use AI several times a week or more, with more than half (55%) using it multiple times each day.

As Singapore’s leading university, NUS recognizes its role in helping students navigate a rapidly evolving future, particularly as 84% of student respondents believe AI literacy will become as fundamental as digital literacy, writing and numeracy.

Singapore’s education system is among the world’s best, and young Singaporeans are already highly active AI users. Among 18-24-year-olds, OpenAI found around 43% of ChatGPT usage is related to learning and education.

The next step is deepening AI literacy to support teaching, improve learning outcomes, and prepare young people for the future workforce.

The collaboration will:

1. Expand AI access across NUS. Every NUS student, faculty member, and member of staff will have access to ChatGPT Edu, including enterprise-level security and controls.

2. Bring advanced AI tools into teaching and learning. ChatGPT Edu will initially be piloted in selected courses, before being introduced into undergraduate courses where it can meaningfully support learning objectives.

3. Create more opportunities for students to build. Students across disciplines will be able to participate in hackathons and build days, focusing on real-world problems, with opportunities to connect to OpenAI’s global networks and communities.

4. Explore new research collaborations. NUS researchers and OpenAI teams will work together on important questions about how AI develops and how it can be applied responsibly.

5. Support responsible adoption across the university. OpenAI’s tools will complement NUS’s internally developed AI-Know platform as the university introduces AI into education, research, and administration in a governed, secure, and sustainable way.

These focus areas reflect what students say they want from their universities. In the same survey, 95% of students in Singapore wanted more opportunities to use advanced AI tools on real-world university projects, and 88% wanted to build an AI app, product or company while at university.

Beyond using generative AI as a thinking partner, AI agents are of increasing interest to students to help get work done, with a third (35%) having already built their own AI agent or assistant to help manage their studies, work or personal life.

Building on our earlier collaboration with the NUS School of Computing, we’ve seen users engage more deeply with ChatGPT and adopt Codex, OpenAI’s coding agent, at higher rates than users at similarly sized universities. One of the interesting projects that has surfaced is Research Brain.

Building a second brain for research

Gao Bin is a postdoctoral researcher in Computer Science at the NUS School of Computing. While working on their main research, Gao Bin and his team kept running into the same challenge. The information they needed was scattered across research papers, technical reports, white papers, blogs, and GitHub repositories.

The team was spending too much time searching for, downloading, and organising information before they could begin using it.

So they built what Gao calls a Research Brain.

With access to Codex and OpenAI models through NUS, the team created a system that regularly finds, reads, and organizes the material, and brings the text and figures together in one searchable knowledge base.

Instead of repeatedly searching across different websites and reopening stacks of PDFs, Gao’s team can now start from one organized collection. They can ask questions across the material, compare experimental methods, find connections between papers, and explore possible directions for their own research.

The Research Brain was not their main project. It was a side project designed to make their main research easier.

That is precisely why Codex made a difference. Building this kind of research infrastructure manually would have required considerable time and effort. With Codex, the team could turn a recurring frustration into a working tool while continuing to focus on their primary research.

When people use AI to make something they care about, we believe that’s where much of AI’s value begins.

When educators become builders

The same curiosity is visible among NUS educators.

OpenAI recently supported a faculty hackathon with the NUS School of Computing, where faculty members worked hands-on with AI tools including Codex to prototype real solutions in a short sprint. The results showed just how broad the possibilities could be.

• One team built a Video Assessment Coach, which turns recordings of student performances into focused, evidence-linked feedback

• Another created Continuum, a knowledge-graph tool designed to bring structure to meeting notes, discussions, and decisions.

Beyond access

Making ChatGPT available across NUS is only one part of this journey.

Every university is working through some version of the same question: How might AI support new ways of teaching? What does responsible use look like? Where can AI open up new kinds of research?

As a research lab, OpenAI will explore research collaboration with NUS, bringing together the questions our teams are working on with the ideas and expertise already here. That matters because universities should help shape how it develops and how it is used.

Developing AI talent for Singapore

This collaboration also supports our broader commitment through OpenAI for Singapore, a multi-year partnership with the Singapore government to expand access to AI and strengthen Singapore’s applied AI capabilities.

A central part of this commitment is hiring more than 200 applied technology and technical-success roles in Singapore in the coming years. Education will be essential to that work. Singapore’s long-term advantage will come from developing people who know how to apply, question, govern, and build with AI.

Students also connect these capabilities to Singapore’s competitiveness. The vast majority of respondents (81%) believe that AI skills will give Singaporean graduates an advantage in the global workforce, while 77% said those skills would be essential to succeeding in their chosen career.

We are already seeing NUS talent making a difference in this space at OpenAI.

Jordan Seow, a 2021 graduate of the NUS School of Computing, is now a Forward Deployed Engineer at OpenAI. Based in Singapore, he works directly with organizations to translate frontier AI capabilities into systems that solve practical problems.

Fellow NUS alumnus Sharan Thangavel also returned to Singapore after working across several international markets. He now helps organisations move from promising AI ideas to systems that create value in complex operating environments.

Their journeys show one possible path from university classrooms to the teams deploying AI across Singapore and the region. As more students gain practical experience with ChatGPT and Codex, we hope many more will help shape what comes next not only at OpenAI, but also across Singapore’s companies and institutions, and across its wider AI ecosystem.

Building what comes next, together

NUS has long understood that a university should do more than prepare people for the future. It should help shape that future.

Used well, AI can give people more time, more confidence, and more room for the things that remain deeply human: questioning, teaching, discovering, imagining, and caring.

We’re excited to see what the NUS community builds next.

About the research:

*This study was conducted by Pureprofile on behalf of OpenAI from 3rd to 7th August 2026, surveying 514 university students in Singapore aged 18+ years, studying either on-campus or remotely.

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Background

When Marion Carré founded Ask Mona nearly ten years ago, she had a deceptively simple ambition: let people have a conversation with the Mona Lisa.

At the time, the technology wasn’t ready. Museums were still largely built around a one-to-many model. Curators created exhibitions. Visitors consumed them. Everyone received essentially the same experience—a wall label, an audio guide, a predetermined narrative.

But Carré believed curiosity didn’t work that way.

“Two people can stand in front of the same painting and have completely different questions,” she says. “Both questions are interesting.”

Today, that belief sits at the heart of Ask Mona, the Paris-based company helping museums, heritage sites, and cultural institutions create AI-powered conversational experiences. The company now works with institutions across 18 countries, building experiences that allow visitors to engage with art, history, and culture through natural conversation.

Shaping AI in service of curiosity

Carré often describes Ask Mona’s mission in simple terms.

The idea emerged from a challenge she saw repeatedly in cultural institutions. Visitors arrive with different backgrounds, interests, languages, and levels of knowledge. Yet museums traditionally have limited ways to adapt to those differences.

“You arrive in a museum and you have the same content everywhere,” Carré says. “But everyone wants to learn something different.”

Conversation changes that.

Rather than following a predefined path, visitors can ask the questions that matter to them. A child may focus on one detail. An art historian on another. A tourist may simply want context.vThe experience becomes personal rather than prescribed.

“We wanted people to be able to ask any question they have in their own language and with their own curiosity.”

Everything changed with generative AI

The vision behind Ask Mona came long before the technology could fully support it.

In the company’s early days, creating conversational experiences required anticipating every possible question a visitor might ask. Every interaction had to be designed in advance.

“If I take the example of the Mona Lisa, I had to think of all the questions people could ask themselves in front of the artwork,” Carré says. “That’s something very hard to do.”

Generative AI changed the equation.

“When we saw those tools emerge, we felt it was finally the right time to deliver the vision we had from the beginning.”

Today, visitors can have genuine conversations with cultural content while still relying on the expertise of the institution itself. Every interaction remains grounded in museum knowledge, but adapts dynamically to the visitor’s interests.

“It’s always the knowledge from the museum,” Carré says. “But delivered in a different way because it depends on the questions visitors ask.”

A bridge between technology and culture

Carré describes Ask Mona as a bridge. On one side is frontier AI technology. On the other is the deep expertise held by museums and cultural institutions.

“We’re embedding amazing technology,” she says. “But we’re also adding a layer that includes identity, knowledge, and culture.”

Internally, Ask Mona refers to this framework as IKC. Each experience is built around three elements: identity, knowledge, and culture. An artwork needs a voice. Historical knowledge needs context. Cultural references need to feel authentic to local audiences.

Sometimes, Carré says, the work feels closer to storytelling than software development.

“It’s almost like writing a novel. Every project is a new character.”

Whether the speaker is a historical figure, a statue, or an artwork, every interaction requires careful consideration of voice, context, and cultural sensitivity.

Creating experiences that were previously impossible

One of Ask Mona’s best-known projects lives in the gardens of the Palace of Versailles.

At the Palace of Versailles, visitors can hold conversations with statues throughout the gardens. The exchange might begin with a playful question—Are you cold standing in water all day?—before moving into discussions of history, mythology, or architecture.

For Carré, the significance isn’t the novelty. It’s the scale.

The result isn’t simply a new interface for information. It’s a different model for learning.

Traditionally, museums have faced a difficult tradeoff. The most personalized experience would be a dedicated guide for every visitor. But that’s impossible to deliver at scale.

AI changes the economics.

Every visitor can now ask unlimited questions, in their own language, at their own pace.

The future of learning

One of Carré’s favorite memories happened entirely by chance.

While traveling during a summer holiday, she overheard a family discussing their recent visit to Versailles. A mother asked her son what he had enjoyed most. His answer wasn’t the palace or the gardens.

“It was incredible to talk with the AI.”

The child immediately followed with another question: would the other museums they visited that summer have the same experience?

“I was super happy about that,” Carré says.

Moments like these reinforce her belief that AI can fundamentally reshape how people learn and discover. The goal isn’t to replace curators, educators, or cultural institutions. It’s to make their knowledge more accessible, more personal, and more responsive to the questions people actually want to ask.

What excites her most isn’t a specific feature or product announcement. It’s the unknown.

“We don’t know yet everything we can do with it,” she says. “Every day we discover new use cases, new experiences, and new ways to help people learn.”

For Carré, that’s the real opportunity. Not replacing culture with technology. Helping more people engage with it.

Dan Wang, Lambert Family Professor of Social Enterprise at Columbia Business School, is the co-founder of CAiSEY, a voice-first AI discussion partner that deepens student learning. He has received multiple awards for teaching classes on technology and strategy over 14 years at Columbia.

For many discussion-based classes, preparation follows a familiar rhythm: students read, write a short response, and arrive with whatever thinking they managed to do alone.

Dan Wang wondered what might happen if that preparation became more interactive.

His course centers on competition, ambiguity, judgment, discussion, and debate. When ChatGPT became publicly available in late 2022, he watched many educators either ignore AI or ban it. He chose a different route: invite students to use it, ask them to disclose how they used it, and study what happened.

What he saw surprised him. Students were more hesitant to use AI than expected. And when they did use it, many were not simply outsourcing the work. They were using it as a thinking partner.

That observation eventually became CAiSEY, a voice-based GPT-powered learning tool designed to help students practice one-on-one conversations with course materials before they enter the classroom. The work moved from written assignments, to text chat, to voice conversation. Each shift changed the learning experience.

Voice, Wang says, unlocked a different kind of engagement.

Johanna Chouraqui, CAiSEY’s product lead, sees the tool as something distinct from a replacement for reading, writing, or classroom discussion. “It’s something new,” she explains: a way for students to have a live conversation with their materials before class.

The Interview

What was the classroom problem that led to CAiSEY?

Wang: I teach technology strategy at Columbia Business School. The class is about how business leaders make big decisions under competition and ambiguity, so a lot of it is discussion based. That is not unique to my class. Many MBA classes that involve judgment use some version of the Socratic method, discussion, and debate.

Our students are smart and rigorous, but they are also careful with their time. If they think a class isn’t worth their time, they’re not shy about saying so.

The motivation behind CAiSEY was to make the classroom experience sharper and more memorable, so students take something away when they enter the real world. But that starts in preparation. If students come in better prepared to discuss, challenge, and revise their thinking, the classroom can become much richer.

What did you do when generative AI first arrived in the classroom?

Wang: When ChatGPT was released in November 2022, everyone suddenly experienced generative AI in a newly accessible form. The first thought was excitement. The second thought, for educators everywhere, was existential fear.

My sense was that most instructors took one of two paths. Some ignored it and hoped it would go away. Others banned it. A small group of us decided to embrace it.

For written assignments in my class, I encouraged students to use ChatGPT. All I asked was that they tell me how they used it.

Two things came from that experiment.

First, students were far more hesitant to use it than I expected. I thought 90% or 100% would use it. In reality, on any given homework assignment, it was more like 20% to 40%.

Wang: Second, when students did use it, they often used it in a way that made learning more involved, not less. I thought it would become a copy-and-paste exercise. Instead, students documented back-and-forth conversations where they used ChatGPT to craft and refine their thoughts.

The written homework in my class was simple: two to four sentences to help students come to class prepared. But ChatGPT was turning even that into a more dynamic thinking process.

How did that become a product idea?

Wang: After that first experiment, I worked with a former MBA student, Johnny Lee (who became CAiSEY’s cofounder), to create something more dynamic. Rather than asking students to answer prompts through a written assignment, we built a rough custom GPT that engaged them in a discussion about those same prompts.

By today’s standards, it was a pretty basic chatbot. But in fall 2023, it felt striking. Students were no longer submitting a one-way response. They were having a written dialogue.

What I realized was that students learned more. The quality of class discussion was higher. Because they had experienced pushback and had a real conversation, their minds changed during preparation. And that was after just two to five minutes of interaction.

Why move from text chat to voice?

Wang: In 2024, when OpenAI’s Realtime API became available, another former MBA student, Jill Cohen (CAiSEY co-founder), and I looked at each other and said: we should build a voice debate. We should build a voice agent.

With only hours to spare, we deployed it in my class the next semester. There were 222 students. From that class, students submitted 1,300 conversations as part of their CAiSEY assignments. Specifically, before a given class session, students had the option to engage in a CAiSEY discussion, tailored to that session, to prepare for class. They were only required to select six sessions out of 20 for which to prepare using CAiSEY. I was unsure of how it would all go down, but students embraced it and many did more than I required them to do.

The difference between a passive written assignment and a text-based chatbot was huge. But the difference between a text-based chatbot and a voice-based conversational agent was even greater.

It engages students differently and activates a different type of thinking. In a new working paper with a Postdoctoral Fellow, Neelam Jain, we analyzed CAiSEY transcript data across almost 1,000 submissions, half of which were submitted as a voice-based conversation and the other half as a text-based conversation. With voice, students spoke more, used more filler words, and tended to iterate and repeat themselves more. But that doesn’t mean their thinking became less sharp.

Instead, we found that a voice-based conversation led to more divergent ideation–that is, more creative reasoning and a broader array of ideas generated–compared to an equivalent text-based conversation, even one that was around the same length in word count.

What does the student experience look like?

Wang: After a CAiSEY conversation, students come to class more committed, more confident, and more connected. In fact, in class, they often reference their CAiSEY conversations when they contribute to class discussions. My interpretation is that this means that the mode of interaction–a voice-based conversation–left a deeper impression in my students’ minds because of the productive friction they experience through CAiSEY.

It is not a written assignment or a reading assignment. It is a live, one-on-one, real-time practice discussion. It may take 10 to 15 minutes, but the density of knowledge exchanged in that time is high compared with a traditional written or reading assignment. From the student’s point of view, when instructors deploy CAiSEY in their classes, students treat it as part of the class requirements, like just another homework assignment. Except, in this particular assignment, a student will be interacting live with an AI-powered interlocutor focused on reacting to their ideas and pushing their ideas further.

In terms of deployment in a class, a student logs into their CAiSEY account which has been registered to their class. Upon logging in, students select an assignment, which then leads them to initiate a conversation about the topic of the assignment, framed typically around a specific discussion question that the instructor has set up. Instructors sometimes assign a reading for the student to absorb prior to the conversation as preparation. Conversations range anywhere from 10 to 20 minutes, after which CAiSEY prompts the student to end. Many students often choose to extend the conversation after being alerted by CAiSEY to end.

That CAiSEY has been effective in engaging students does not replace the role of reading and writing. CAiSEY is complementary. CAiSEY adds value because students come to class more motivated to say something because they have done it before. They have considered alternatives. They have had the experience.

We see this especially with students who tend to be shy or for whom speaking up is a challenge. They appear to benefit the most. This is based on direct feedback from instructors who observe this pattern in their classes after students have used CAiSEY. This also aligns with systematic evidence based on some early results from a large field experiment across over 1,000 students who were randomly assigned to use CAiSEY.

They also come in with more confidence because the material is closer to them. They have talked through it. They remember it. They are not just willing to speak; they have something they are willing to share.

You also mentioned accessibility. What have you observed there?

Wang: One thing we observed anecdotally, and then saw in the data, was that students who reported liking CAiSEY and benefiting from it most often included students who spoke English as a second language and students who later revealed learning conditions such as dyslexia or dysgraphia.

That made me think harder about written assignments. We often assume the written word is a proxy for cognition. But that may not work for a meaningful share of students.

So voice matters. It can give students another way to engage with ideas, practice their thinking, and arrive in class ready to contribute.

Johanna, from the curriculum and operations side, what does CAiSEY make possible for instructors?

Chouraqui: I keep coming back to the question of what this opens up for instructors and students.

The instinct with AI is often that it replaces something: reading, discussion, writing. What is interesting about what we have built is that it is not really substituting for a form of preparation or discussion. It is something new that has not existed before: a conversation at home with your materials.

Can you walk through one course example?

Chouraqui: One recent example came from outside the business school. An instructor at Columbia Climate School first encountered CAiSEY when his partner was using it nearby. He asked what it was, got interested, and reached out.

His idea was to help students practice difficult climate conversations. He liked the brainstorming, open-ended questions, and debates CAiSEY could support, but he wanted to know whether students could practice conversations through different personas.

He sent us course materials, a framework, and chapters from the books students were reading. We brainstormed together and decided to build a different role play for each of six personas. Students would have conversations with these different people throughout the course.

I built out the questions, and we iterated together to make sure it sounded like him, came from his class, reflected his learning objectives, and met the needs he had for the course.

Once he approved it, students received a link, registered for the course, and saw their assignments in CAiSEY. They completed the assignments according to the instructor’s guidelines, with due dates managed through the learning management system.

When an assignment was submitted, students received feedback and a summary of their conversation. The instructor could see each transcript and summary, then go into the next class understanding what worked well, what trends appeared, and what common feedback students received.

How do you keep the conversations from narrowing student thinking?

Wang: A lot of what CAiSEY is good for involves ambiguity and judgment. The underlying system instructions are designed to create guardrails and structure, but also to stimulate divergent thinking.

That can happen through a debate, where there are clear opposing positions, or through open-ended brainstorming. CAiSEY looks simple from the outside. It can look like a voice chat. What you don’t see are the years of iteration around flows, structures, and sensitivities that make the conversations engaging, pedagogically meaningful, and useful. The input materials that inform CAiSEY’s system instructions come from hundreds of pages of teaching notes, recorded discussions and debates, and distilled best practices from decades of discussion-based teaching at Columbia Business School.

These ideas also largely align with the principles that have been thoroughly researched and expressed in widely available scholarship on difficult conversations. Julia Minson, who has also been featured in this Substack, explores similar principles in How to Disagree Better. We were also careful to make CAiSEY’s persona explicit to the student and instructor. Rather than being a tutor, TA, or teacher, students know that they are entering a conversation with an adversarial yet friendly peer who is informed about the class materials.

In other words, students know going in that they will be challenged. Whatever position they take, there will be some prodding and poking. The terms of the conversation are set and shared. But they also know what they are buying into.

What have you learned about voice and divergent thinking?

Wang: We wrote a paper about this because I was interested in whether CAiSEY stimulated original ideas.

We compared voice and text-based conversations. We took the data we had and used computational linguistics to analyze semantic markers, embeddings, and different ways of interpreting reasoning and thought.

For the same conversation about the same topic, at the same length, controlling for student background, demographics, and other factors, we found that voice produced about 70% more divergent thinking on average.

That was measured by semantic diversity in students’ thoughts. It was not lexical diversity, or the range of words used. In fact, lexical diversity is lower in voice because we are speaking in real time and are usually less polished than when we write. But semantic diversity, which is a measure of cognitive flexibility and breadth of thought, was much higher in voice than in text.

How have model improvements changed the product?

Wang: During the time CAiSEY has been around, the Realtime model has undergone significant upgrades. Each time, we have had to adjust on the fly.

That has created learning opportunities. In my technology strategy class, I teach students how to interact with an AI ecosystem. When you have dependencies, that changes how you think about which technology to adopt, what is relevant, and what the best use case is. Experiencing that firsthand gives me something concrete to bring back to students.

With newer voice models and GPT-Live, conversations are even more natural sounding and follow instructions better.

Chouraqui: We noticed that after updates, some of the prompting we had used was no longer needed. For example, we had a behavior prompt telling the model to speak very slowly, in three to five words, so the conversation would feel natural and useful for learning. With the newer real-time model, it did that more naturally on its own.

What is CAiSEY’s scale?

Wang: Since that classroom deployment, mostly through word of mouth and talks I have given, CAiSEY has expanded to about 24 institutions, mostly business schools, and has reached roughly 4,000 to 4,500 students.

The team has collected about 2,000 hours of conversation. Students want more specific feedback on how they handled moments in the conversation. Instructors see value in a new kind of classroom signal, but they also need help making sense of many long transcripts. New beta features are meant to help students apply what they learned and help instructors identify key tensions before class.

What is the larger story you want educators to understand?

Wang: The story is not that AI should replace class preparation or classroom discussion. It is that AI can create a new mode of preparation that makes the discussion better.

Students can come to class more committed because they have already practiced. They can come with more confidence because they have talked through the material. And they can feel more connected because the mode of interaction may work better for students who are less well served by writing-only preparation.

For instructors, it can also make the classroom more valuable. They don’t have to spend as much time reteaching what students should have worked through at home. They can do the work of teaching in the classroom. Because they have unprecedented visibility into how their students are thinking about the very problems that animate the class, instructors can also stage more tailored classroom discussions and activities that tailor the classroom experience to their students’ needs.

End Note

CAiSEY points to a practical shift in how educators can think about AI.

The question is not only whether AI helps students write faster. It is whether AI can help students rehearse their thinking before it matters publicly: before the case discussion, before the debate, before the moment when they need to explain an idea clearly to someone else.

For discussion-based learning, that may be the important opening. A student does not just submit an answer. They try an idea out loud, get challenged, revise it, and arrive in class with more to say.

Technical Notes

CAiSEY uses OpenAI’s Realtime API to power voice-based discussion and debate. Its text-based assignment option and instructor/student feedback workflows use OpenAI language models to generate summaries, surface key tensions, and support follow-up reflection.

Interested instructors can learn more on the CAiSEY website, where they can demo a CAiSEY assignment as a student and contact the CAiSEY team to create a CAiSEY assignment based on their own class materials.

Welcome back to The Edu Prompt, a field guide for learning, teaching, and building with AI from OpenAI Education.

This week brings new launches, practical workflows, and field notes from education teams around the world.

In This Issue

• Feature: ChatGPT Work and what it could mean for education teams.

• Product Corner: Sites for turning ideas into simple, shareable pages. Also worth noting from last week: GPT-5.6 and GPT-Live.

• Try this with ChatGPT: Make a scene.

• Try this with ChatGPT Work: Calibrate an assessment.

• Pro-tip: Build a skill.

• Around the World: OpenAI Education for Countries meets leaders in Italy.

• Learning with OpenAI: New hands-on Skill Labs.

Feature Story: Introducing ChatGPT Work

Faculty and staff around the world already use ChatGPT to brainstorm, draft, analyze, and solve problems. ChatGPT Work, powered by our latest frontier model, GPT-5.6, extends those familiar workflows into projects that involve multiple steps, sources, tools, and deliverables.

AI is becoming less about one-off answers and more about helping people organize, draft, analyze, and reuse work across the flow of a day. For educators, staff, and leaders, that matters because the bottleneck is rarely a lack of ideas. It is turning those ideas into something usable: a lesson plan, a family message, a grant outline, a meeting brief, a rubric, a project plan, or a first draft that can be improved.

ChatGPT Work is designed for tasks that need context, tools, and follow-through.

Read the full article.

Product Corner: Build a webpage with Sites

With ChatGPT Work, Codex, and Sites, you can move from a rough idea to a lightweight webpage that is easy to share. For education teams, that can mean a resource hub, project tracker, event page, course companion, or internal guide without starting from a blank page.

Example

Create an experience focused on W. B. Yeats’s poem “The Song of Wandering Aengus.” 
Include the full public-domain poem, a brief Yeats biography, historically appropriate 
images, and interactive annotations on selected lines and phrases. 
Style it with an early-20th-century Arts and Crafts / Celtic Revival look, 
using aged paper textures, restrained ornament, period typography, and accessible contrast. 
Build the site directly in @sites and return an editable result.

Read more about Sites.

Try this with ChatGPT

Create a time-travel classroom: Use ChatGPT to generate images that help students bring ideas, people, and concepts together. Use this as a discussion starter, not a historical source.

Create a realistic classroom where Leonardo da Vinci, Ada Lovelace, 
Albert Einstein, Maya Angelou, and Marie Curie are all students learning together. 
Educational, respectful, historically recognizable.

Fine-tune output: Once you get an image, tap “Edit” and use the comment feature to select areas you’d like to modify.

Open this prompt in ChatGPT.

Try this with ChatGPT Work

Calibrate assessments: Review scoring patterns without assigning or changing grades.

Review the de-identified submissions, rubric, grading export, 
sample feedback, course outcomes, and calibration rules I provide.

Create a calibration workbook that:
- flags possible scoring inconsistencies
- summarizes common strengths and gaps
- identifies feedback themes
- suggests rubric clarifications
- cites evidence for every flag

Do not assign or change grades. Route every scoring judgment to faculty review.

Don’t forget to validate the findings: Faculty reviewers should check the source evidence, confirm that comparisons are like-for-like, and look for missing context such as accommodations or assignment variants.

Open the calibration workbook prompt in ChatGPT

Pro-tip: Build a skill for repeated workflows

You can turn a repeated workflow into a reusable Skill that you can invoke later by name. Here’s an example for prioritizing your inbox.

Help me create a reusable skill that reviews recent email and prioritizes what needs my attention.

Before creating it, ask me one question at a time about:
- Which inbox and date range to review
- What counts as actionable or urgent
- People or topics to prioritize
- The output format I want
- What actions the skill may take

Suggest sensible defaults when I’m unsure. 
Then summarize the proposed workflow for my approval before creating the reusable skill.

Open this prompt in ChatGPT

Around the World: Field Notes from Italy

Last week, OpenAI’s Education for Countries team traveled across Italy to learn from and strategize with university leaders, professors, PhD students, and local businesses to imagine and build the future of education with AI.

Each stop—Turin, Modena, and Naples—revealed the unique culture, industry, educational focus, and aspirations of each region. AI is most powerful when it amplifies the experiences, skills, and intentions of people and communities.

Read more.

Learning with OpenAI: New Skill Labs

Join us in OpenAI Academy’s newest learning series, Skill Labs, where you’ll get hands-on practice with everyday workflows you can use immediately, whether you’re faculty, staff, working in student support, or an administrator.

• 07.28 Creating high-quality presentations

• 07.30 Automating workflows

• 08.04 Planning cross-functional projects

• 08.06 Analyzing data

• 08.11 Managing daily work

• 08.13 Building sites and dashboards

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We are looking for practical examples from educators, faculty, staff, students, and education teams using OpenAI tools to save time, improve learning, or build something new. Reply with what you tried, what changed, and what another educator or team could reuse.

Thanks for reading. If this issue gave you something useful to try, share it with an educator, staff member, or team who might use it too.

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Sebastián Díaz Bolívar teaches Legaltech at Catholic University Luis Amigó.

Source note: This is an edited interview adapted from a narrated video submitted to OpenAI. Watch the associated video in OpenAI Academy.

Intro

What does it look like when a law school embraces artificial intelligence—not just as a topic, but as a practical tool for students and the communities they serve? At Universidad Católica Luis Amigó in Medellín, Colombia, law professor Sebastián Díaz Bolívar is leading a pioneering LegalTech initiative that integrates ChatGPT into the curriculum. The result: students are learning to draft legal documents, analyze cases, and deliver social impact at a pace and quality previously unimaginable. In this interview, Díaz Bolívar shares the story behind the program, why AI matters for legal education, and what’s changed since they started.

The Interview

Q: Sebastián, can you set the stage—how did your LegalTech initiative begin, and what sparked the focus on AI?

Díaz Bolívar: It started in 2023, when ChatGPT was first announced. We saw its potential and began experimenting with these tools in our law courses. Our director, Esteban Aguirre Espinoza, recognized that few lawyers in Colombia understood how to use AI in legal practice, so we decided to lead the way. We created a new course called LegalTech, designed to teach students not just about AI, but how to use it responsibly and effectively in real legal work.

Q: What does that look like in practice? How do students actually use AI in your classes?

Díaz Bolívar: First, we make sure students understand that AI is a tool, not a replacement for human judgment. For example, we take real legal documents—like contracts or petitions—created by Colombian lawyers and our own students. We upload these to the software, then guide students in using ChatGPT to generate new drafts. The key is teaching them to craft better prompts, because better prompts mean better documents. We also emphasize the importance of human supervision at every step.

Q: Can you share a concrete example of how this has changed students’ work?

Bolívar: Absolutely. In Colombia, there’s a legal process called “conciliación” that requires students to draft specific documents, like a Petition for Conciliation. Previously, it took students about 50 minutes to produce a solid draft. With AI, they can now do it in 5 to 7 minutes—ten times faster. That’s a huge leap in efficiency, and it frees up time for deeper learning and client interaction.

Q: Beyond efficiency, how are you using AI to foster critical legal skills?

Bolívar: We use AI to help students analyze Supreme Court cases. First, we teach the theory—how to read and interpret legal precedents. Then, with ChatGPT, students can process large volumes of case law and quickly identify the most relevant points. But again, the AI doesn’t work alone: students must supervise, interpret, and give feedback on the results. This combination of technology and human insight is crucial.

Q: Your program also has a social impact dimension. How does AI help you serve the wider community?

Bolívar: We run a legal clinic where people with limited resources come for assistance. We’ve built a custom GPT to help students create better legal documents for these clients. This not only improves the quality and speed of our services, but also teaches students to use technology ethically and for the public good. Some of our graduates have even been hired by companies specifically to improve their legal operations using these skills.

Q: What’s been the most rewarding outcome so far?

Bolívar: Seeing our students succeed—both in class and in their careers. Graduates have told us that their AI skills set them apart in the job market. And as a faculty, we’re proud to be recognized as leaders in LegalTech education in Colombia. We’re excited for what’s next as AI tools continue to evolve.

What Stands Out

Core idea: Universidad Católica Luis Amigó integrates ChatGPT into legal education to prepare students for modern legal practice.

Classroom design: Students learn to use AI responsibly by drafting real legal documents, analyzing case law, and refining prompts under human supervision.

Student impact: AI enables students to complete complex legal drafts in minutes, enhancing efficiency and freeing time for deeper learning and client service.

Transferable lesson: Combining technology with ethical oversight and social responsibility can transform professional education and community engagement.

Bio

Sebastián Díaz Bolívar Sebastián Díaz is a law professor at Universidad Católica Luis Amigó in Colombia. He specializes in LegalTech and artificial intelligence applied to law. Sebastián leads innovative projects that explore how AI can transform legal education and practice.

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Faculty and staff around the world already use ChatGPT to brainstorm, draft, analyze, and solve problems. ChatGPT Work, powered by our latest frontier model, GPT-5.6, extends those familiar workflows into projects that involve multiple steps, sources, tools, and deliverables.

Give ChatGPT Work a goal, and it can propose a plan, gather context from the sources available to it, carry out a sequence of steps, create connected outputs, and pause when it needs guidance or approval. You can follow its progress, answer questions, redirect the work, and review the results before they are used or shared.

Download the ChatGPT desktop app

From a prompt to a completed workflow

Chat GPT Work is designed for tasks that unfold over time. A typical workflow may look like this:

1. Define your goal. Describe the outcome, audience, constraints, and materials that ChatGPT Work should use.

2. Review the plan. ChatGPT Work breaks the project into steps, identifies missing context, and asks questions before proceeding.

3. Let the work progress. ChatGPT Work uses the files, web context, and connected tools available in that environment to complete the approved steps.

4. Stay involved. You can answer questions, redirect the task, review sources, and approve important actions along the way.

5. Review outputs. ChatGPT Work returns the connected outputs, open questions, and decisions that still require a person.

How Chat, Work, and Codex fit together

The updated ChatGPT experience brings Chat, Work, and Codex into one place. Each supports a different kind of task.

Most teachers, faculty, and staff should start with ChatGPT Work when they need help with a longer or more complex task. Work can bring together files, research, and connected tools to complete a coordinated set of steps. Technology teams and advanced builders should use Codex for tasks that involve code, repositories, or software development.

The tools available in ChatGPT Work depend on how you access it. On the web, ChatGPT Work can use uploaded files, web information, and connected tools approved by your institution. In the desktop app, it may also work with approved files, applications, browsers, and tools on your computer.

Start with work you already know

Choose a first task that you understand well enough to evaluate. Define the goal, provide the relevant context, describe the expected outputs, and identify where ChatGPT Work should pause for review, decisions, or approval.

For education, ChatGPT Work can help you:

• Refresh a course or learning experience. Provide the current course materials and the learning goals they are intended to support. ChatGPT Work can compare the materials with accessibility guidance and aggregated learner feedback, then identify content that may be outdated, inconsistent, or misaligned. After you approve a revision plan, it can draft the updates and track any decisions that still require faculty judgment. The final outputs might include an updated syllabus, revised assignments, and a summary of what changed.

• Prepare a program review or accreditation packet. Provide the review requirements and the evidence your team has already collected. ChatGPT Work can organize the evidence against each requirement, identify gaps, and flag areas that need follow-up. It can also help assign owners, track outstanding items, and assemble a draft packet. Program leaders can then review the supporting evidence and approve the final version.

• Build a source-backed research or planning brief. Start with the question you need to answer, then add your existing notes and approved sources. ChatGPT Work can compare the evidence, highlight areas where sources disagree, and draft a brief with citations. It can also track unresolved questions so the team knows where additional research or judgment is needed. This can support curriculum planning, grant development, or leadership decisions that require a clear view of the available evidence.

• Coordinate operational work across teams. ChatGPT Work can bring together information from project systems, policies, and team notes. It can clarify who owns each next step, draft project updates, and keep the implementation plan current. Project leads gain a clearer view of what has changed, what is blocked, and which decisions still require approval.

Where ChatGPT Work can help across an institution

Faculty. Help faculty update course materials while keeping learning goals and evaluation criteria in view.

Academic operations. Keep program reviews and accreditation work moving by tracking evidence, open questions, and follow-ups.

Technology teams. Pull together ticket history and system context, then draft a response or implementation plan for review.

Finance and administration. Explain what changed in a budget or report and prepare the materials needed for the next review.

Enrollment and marketing. Turn program information and audience feedback into a campaign brief, then adapt the materials for different audiences.

Institutional research and analytics. Place approved data in context, explain the changes that matter, and prepare materials that support a decision.

Keep recurring work moving with Scheduled Tasks

Scheduled Tasks can run recurring work at the times you choose. A task can review new information, update an existing output, and surface the changes that need attention. For example, Work can:

• Review new messages from approved channels each week and refresh a meeting agenda.

• Check an approved dashboard each morning and summarize what changed, including the underlying source or calculation used.

• Review questions from an institution-approved course Q&A channel and summarize recurring themes for the instructor before class.

• Refresh a project tracker each Friday, flag overdue items, and prepare a status update for the project lead to review.

Bring context together through connected tools

Where supported and enabled by an institution, connected tools can help Work bring relevant context together across systems such as Google Drive, Microsoft 365, Slack, Teams, email, calendars, LMSs, and project trackers. Work can use that context to create and refine documents, presentations, analyses, trackers, and Sites.

Admin Controls

You remain involved throughout the project. You can inspect the plan, see which sources and tools are being used, answer questions, redirect the work, and approve important actions before they happen.

For ChatGPT Edu and ChatGPT for Teachers, workspace admins can determine who has access and what information ChatGPT Work can use. They can also control which tools are available and place additional restrictions on certain actions. On the web, these settings govern connected tools, browser use, cloud network access, and sensitive actions. In the desktop app, the same governance model also applies to approved local files and applications.

Turn your results into a shareable Site

Sites, now in public beta, lets you turn the result of a ChatGPT Work project into an interactive site or web app. A Site might be a course resource hub, an internal project tracker, or a dashboard. You can test it inside ChatGPT and share it with a team. Depending on institutional policy, you can also publish it with a URL. When the source material changes, you can ask Work to refresh the Site.

For example, a program review could end with a source-backed briefing document and an internal Site. The site could display organized evidence, show who owns each open question, and keep upcoming deadlines visible.

Availability

ChatGPT Work is available to all plans on macOS desktop today, and rolling out to Windows desktop, and Plus, Pro, Business, Enterprise, and Edu on web and mobile over the next few days. The updated ChatGPT desktop app brings Chat, Work, and Codex together on Mac and Windows. Capabilities, supported integrations, and controls may vary by plan, platform, and administrator configuration.

For current product details, availability, pricing, supported integrations, and security controls, see the main ChatGPT Work announcement.

Anupam Sobti teaches Web Development using AI at Plaksha University.

Source note: This is an edited interview adapted from a narrated video submitted to OpenAI. Watch the associated video in OpenAI Academy.

Intro

What happens when you hand a cohort of motivated undergraduates the fundamentals of modern web development—and invite them to infuse every step with AI? Dr. Anupam Sobti, assistant professor at Plaksha University, set out to answer this question with his course “Web Development Using AI” (now called AI Product Design). In this conversation, Dr. Sobti shares how he reimagined the undergraduate learning experience, why he believes AI is a capability (not just a skill), and what happens when students are encouraged to build complete systems from early years rather than wait to learn all the fundamentals before ever building a complete product.

The Interview

Q: Dr. Sobti, what inspired this course?

Sobti: The core motivation was to bridge the “relevance gap” I noticed in undergraduate engineering. Students learn the fundamentals—data structures, programming, linear algebra—but often struggle to see how these connect to building useful software. With the rise of large language models and their utility in software workflows, I saw an opportunity to bring AI directly into the web development process. My goal was to move from a bottoms-up, theory-first approach to a journey where students go from idea to prototype, experiencing the full stack and the power of AI along the way.

Q: Can you describe at what stage of their undergraduate degrees did students take the course?

Sobti: We had a selective process—about 25% of applicants were accepted, from 2nd and 3rd year undergraduates who showed motivation and a clear vision for what they wanted to build. The only formal prerequisite was Python programming, which they had covered in their first semester. From there, we built up: students learned everything from HTML, CSS, and JavaScript to backend APIs, databases, and security best practices.

Q: Can you walk us through the workflow students followed, especially in terms of integrating AI?

Sobti: Every student started by proposing a project idea, which needed to include basic web development, LLM integration, and some form of Retrieval-Augmented Generation (RAG) or agentic pipeline. We taught them to use FastAPI for backend endpoints, and to prompt LLMs for generating everything from HTML and CSS to backend logic. Tools like Cursor, Lovable, and Bolt helped with rapid prototyping, while API endpoints provided more control and reproducibility—especially for database interactions. Later, we introduced agentic workflows and custom tool development, including crew AI for orchestrating multiple agents.

Q: What are some standout student projects that emerged from the course?

Sobti: The creativity was remarkable. One project, Bugzer, uses an LLM to automate bug-finding in apps—saving developers from manually checking dozens of pages after each update. Another, Luma, was inspired by a student’s experience with their father recommending books. Luma connects local libraries and readers, suggesting books based on mood and interest via an AI chatbot. Then there’s CourseVibe, an AI-enhanced course review and recommendation platform for Indian college students. It uses validation questions and reasoning models to ensure reviews are genuine, filtering out unreliable feedback before it’s published.

Q: How did students experience the learning process, especially when working with AI tools?

Sobti: Many students described “aha moments” when they realized AI wasn’t just an assistant but a capability that could be woven into any project. For example, one student unlocked new project functionality by enabling memory in their agent, allowing it to remember past interactions. Another shifted from using GPT in the browser to hitting the API directly, providing full codebase context and targeted fixes—this was a breakthrough in understanding the potential of AI. Across the board, students found that AI sped up learning and iteration, making it possible to move from concept to working prototype much faster than traditional methods. I also found students more interested in the core areas like data structures, databases, etc. once they were able to witness how these technologies made a significant difference in their products.

Q: What’s the broader impact you hope to see from this approach?

Sobti: I want students to realize that software in a human-enabler and with AI, we can broaden how much can be enabled for humanity. I want the students to be able to witness product journeys and build curiosity about fundamental computer science as well as advanced AI capabilities eventually integrating it into their workflows. I hope they see AI not as a niche skill, but as a capability that can be integrated into any domain. The speed of iteration and the ability to test, refine, and deploy ideas rapidly is transformative. Ultimately, I hope this approach encourages students to see connections across subjects and to approach problems with creativity and confidence.

What Stands Out

Core idea: AI is framed as a capability to be integrated throughout the software development process rather than a standalone skill.

Classroom design: The course centers on rapid prototyping from idea to working web application, combining frontend, backend, and AI workflows. The course helps students take a top-down approach to learning how systems work.

Student impact: Learners created projects addressing real-world problems, such as automated bug detection and personalized recommendations, while experiencing accelerated learning.

Transferable lesson: Embedding AI tools in project-based learning can deepen understanding and foster creativity by enabling faster iteration and practical application.

Bio

Dr. Anupam Sobti is an assistant professor at Plaksha University. He completed his postdoctoral research at Microsoft Research India and earned his PhD at IIT Delhi with the assistive technology group. His current research focuses on geospatial and embedded perception for agri-urban sensing.

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