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

Have a story idea?

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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Scott Dyreng is an accounting professor and Senior Associate Dean of Innovation at Duke University’s Fuqua School of Business. Over the past year, he and colleagues at Fuqua have been developing an AI-enabled classroom system with OpenAI’s API designed to help faculty and students better understand participation, teamwork, and the quality of classroom discussion. (Photo by HuthPhoto)

Background

For years, participation has been one of the hardest parts of teaching to evaluate well. Yet learning to show up, contribute, and move a discussion forward is exactly the kind of skill employers prize in business school graduates, and often report seeing in short supply.

The challenge is stubborn. A professor may teach several sections a day, sometimes with 70 students in a room, and then be expected to recall who spoke, what they said, whether each comment advanced the discussion, and how the room responded. Students, meanwhile, often receive a participation grade with little concrete sense of what they could have done differently. Student participation can’t only be a checkbox as their engagement is an important opportunity to develop career readiness. And faculty need to make sure that the current heuristic doesn’t just reward the loudest voice in the room, and students benefit from all contributions, whatever the shape, being considered. But capturing engagement and providing valuable feedback is a near impossible task in a traditional lecture hall.

When Professor Scott Dyreng became Senior Associate Dean of Innovation at Fuqua, he found faculty already trying to solve this problem. He was also wrestling with a related question of his own: if students can now use AI to produce written work in minutes, what remains valuable about coming together in person at all?

His answer was not to use AI to replace the human parts of learning, but to make them more visible: to preserve, rather than automate away, the productive friction of people learning to think together.

Scott talked through the problem with colleagues across the university, and pieces of a solution began to come together. Fuqua’s multimedia and IT teams built on lecture-recording infrastructure they had maintained for decades, adding a way to capture who was speaking and what they said. At a university gathering, Scott described the idea to Robert Olinger, an assistant dean already building an AI system to analyze recorded discussion.

The collaboration brought together classroom capture infrastructure, Olinger’s insight engine, and a shared ambition to make classroom conversation visible. The result was a way to understand who contributed, how teams worked, and where understanding broke down.

It began in one classroom, then two. This fall, Fuqua will open the tool to faculty members who want to use it, starting with first-year team formation and leadership work. The central idea is simple: if leadership education is about learning to think with other people, students need better insight into how they actually interact with them.

The Interview

What problem were you trying to solve when this started?

When I became Senior Associate Dean of Innovation, I started asking people what Fuqua was doing with AI. While some faculty were doing amazing research, and a few were experimenting with AI tools in their teaching efforts, the general consensus among faculty, students, and staff was that we needed to be doing much more.

Around that time, another faculty member came to me with an idea for an AI-enabled classroom. He was trying to automate who was participating and what they were saying. The idea had obvious benefits to long-standing issues in business education.

This classroom would solve a longstanding problem with capturing and evaluating participation. In business schools, class participation can be a meaningful part of the grade. But the way we assess it has historically been weak and highly subjective. In the best case, a teaching assistant sits in the room and marks down who said something and maybe whether it was on point. More commonly, the faculty member teaches multiple sections, goes back to the office, and tries to remember who contributed. A week later, that memory is even worse.

Then a student asks, “Why didn’t I get an A?” and the answer is basically, “Your participation was low.” But why was it low? What could they have done differently? That’s not very easy to answer without the details.

Why does participation matter so much in a business school classroom?

Information has been ubiquitously available for decades, and now more accessible than ever with AI. Students still need technical knowledge of course. They need to understand accounting, finance, marketing, operations, strategy, and the rest.

But a big part of business education is teaching students how to interact with people. Students need to learn how to give and receive feedback without coming emotionally unglued. They need to build relationships of trust. They need to persuade people. They need to take the diverse opinions of smart people and find common ground.

That is why participation matters.

How does the classroom system work?

The initial thought was to put a microphone at every desk to capture participation. Our audiovisual team pushed us toward a better approach: ceiling-based microphone arrays. These can capture sound well and determine where the sound is coming from, but preserve the natural feel of the classroom.

We didn’t want assigned seats because that changes the classroom dynamic. So our team modified an app that had been used for attendance. Instead of just checking into the room, students check into a seat. Now we know who is sitting where, we know where the sound is coming from, and we can connect that to the transcript.

Once the transcript is annotated with speakers, we use OpenAI’s API to analyze it with specific context from the faculty lecture. Did a student respond to a question? Did they build on another student’s point? Did someone else build on theirs? Did the comment invite discussion or shut it down? Was it on point, given the material for that class, or was it tangential?

That creates a much more objective and detailed view of classroom participation than the old method that relied primarily on the professor’s memory.

You mentioned allowing students to use AI freely on assignments. What did that look like?

In the fall of 2025, I gave students unfettered access to AI for creating their assignments. In many MBA classes, including mine, students work in teams of about five. They analyze a case, reach a conclusion, write a memo, and submit it as a team assignment.

I discovered that with AI, team culture deteriorated. Many students did not regularly meet with their teams. If there were 10 assignments and five people on the team, they divided them up: you do two, you do two, and so on. While the idea of divide and conquer is not new, unfettered access to AI seemed to exasperate it. Students did not seem as worried about checking each other’s work because AI could help make the final submissions remarkably polished.

But taking divide and conquer to an extreme was exactly the opposite of what we wanted. We were trying to help students learn how to work with others, and introducing access to AI enabled them to reduce their interactions with others.

How did you respond?

Over winter break, I came up with a simple idea: require students to upload an audio recording of their team meeting.

At first, the purpose was simply to make sure students actually met. If they had to submit a recording, they had to get together. But then we realized we could combine transcription and speaker-labeling tools with GPT through OpenAI’s API to analyze the meeting: who participated, how the conversation flowed, and what kinds of interaction took place.

That worked exceptionally well. We could take an hour-long team meeting and learn a lot about how students were thinking, how they were working together, and whether someone was dominating the conversation.

In one real team, there was one male student and four female students. The male student spoke for about 50% of the time. That is exactly the kind of thing a team may not notice clearly in the moment, but it matters.

What kind of feedback did the teams receive?

After each team meeting, we sent the team about three pages of feedback.

Some of it looked like meeting notes. Here’s how much each person spoke. Here were the questions you were supposed to discuss. Here’s what you concluded.

But it went deeper. The feedback analyzed how team members interacted. One person might be good at bringing together diverse perspectives. Another might keep the meeting on track. Another might raise complicated questions that helped the team understand the material more deeply.

It also provided opportunities for students to consider. If Ariel didn’t say much, the team might be encouraged to ask her more questions. If Seth spoke too much, he might be encouraged to ask someone else a question after he contributes.

What did the system make visible for faculty?

Before class, I received a report based on 15 hours of team meetings from 15 teams.The report described l what concepts students understood, what concepts they were struggling with, and what I might want to emphasize in class.

Some of the insights were general, some were individual. It might say: you should call on Luol because he understood this concept well. You should call on Zion because he understood another concept. Or you should call on team seven because they came up with a point of view that was different from the other teams.

As a faculty member, that type of information is very useful. Before we had this tool, I would read each team’s one-page assignment submissions and try to glean that information. But when you have insights from an hour of discussion from every team, you have far more understanding of what students are actually thinking.

The system also provides feedback to faculty on their teaching by analyzing the whole course. For example, I teach students a specific framework in my class. The AI system showed that students used the vocabulary of the framework, but very few of them actually understood the framework deeply. That tells me I may have been teaching buzzwords rather than real understanding.

How do you judge the quality of participation, not just the quantity?

We can create a heat map of participation across the term. Some students speak all the time. Some students never speak.

But the amount of speaking is not the same as the quality of what students have to say. A student might talk often but provide little substance. Another student might speak rarely, but when they do, their comment is highly relevant, integrative, or useful.

The system can help distinguish those patterns. It can describe a student’s role over time: whether they pressure-test logic, bring together ideas, keep the group focused, or stay peripheral. Then students can receive individualized feedback about how their ideas land and what they might experiment with next.

That accumulated feedback matters. A student may not need to hear only, “You spoke too little in this one case.” They may need to see their arc across the term.

How do you manage accuracy, privacy, and fairness?

You have to be careful. We are not simply asking one model to make a judgment and treating that as fact.

The workflow involves many prompts and checks. You don’t just ask one broad question and call it done. You tag the data, analyze it in one context, analyze it again in another context, and sometimes send it through different versions of a prompt to see whether the model agrees before sending feedback to students.

We also ground the analysis in relevant research about team dynamics and in the domain context for the class. If the class session is about taxes and climate, the model needs to know that the meeting is about taxes and climate, and needs the assignment students were working from.

On privacy, the system is designed around anonymized data handling and institutional protections. Transcripts can be anonymized before analysis, then mapped back afterward so the feedback is useful without exposing more identity than necessary.

How is AI changing assessment?

In my class, students submit team memos. In the past, there was visible variation in writing quality. Now many memos come in polished, because students can use AI to help write them.

That is not automatically bad. In the real world, students will probably use AI to help draft memos. But it changes what faculty need to assess. A polished memo no longer demonstrates that students understand the underlying material.

So we have to ask: What do students actually know? Can they explain it? Can they answer follow-up questions? Can they defend the reasoning behind the work?

That is why oral assessment may become more important. If AI can help assess oral exams at scale, schools may have new ways to test student comprehension..

Where is the work going next?

We began in one classroom. We’ve now raised money to upfit every classroom in the business school, so the tool will be available across Fuqua in the fall for faculty who want to use it.

One early use case is C-Lead, Fuqua’s first-year team orientation program. Students are assigned to teams, create a team charter, and then work together through required courses. We are currently working through the logistics of employing the tool from the beginning so teams can see whether they are following through on the commitments they made to one another.

The same workflow can apply in other settings. For admissions interviews, AI may help assess fit and interpersonal skills more consistently. In career services, mock interviews can be recorded and analyzed so students get better feedback on how they interview.

The transferable piece is the workflow: take an audio file, give it the right context, use structured prompts, analyze against clear standards, and provide feedback people can act on.

What’s the larger story you want people to understand?

The current narrative in education is often that AI is destroying people’s ability to think. What we are doing flips that on its head.

This is about encouraging and developing human interactions with AI instead of having AI take away human interactions. Everything I described is about helping people interact better with humans.

In a business school, students are signing up to become leaders. A leader has to lead people. If someone says, “I do not want you to analyze my team meeting,” we push back and say: you chose to get an MBA. You are here to learn how to work with people.

Every technology can be used well or badly. Our job is to figure out how to use it for good.

What Stands Out

• Core idea: OpenAI API is being used to make human interactions more visible, not to replace them.

• Classroom design: Audio capture, seat check-ins, transcripts, and structured prompts connect participation to specific moments in discussion.

• Student impact: Teams and individuals receive concrete insights on airtime, contribution quality, collaboration patterns, and opportunities to improve.

• Faculty impact: Instructors can see misconceptions as well as promising student/team insights before class begins.

• Transferable lesson: The same workflow can support classrooms, team rooms, admissions interviews, and career coaching when context and evaluation standards are clearly defined.

Closing

Dyreng’s work points toward a different way to talk about AI in education.

The question is not only whether students can use AI to finish assignments faster. It is whether educators can use AI to see learning that was previously difficult to observe: the half-formed idea in a team meeting, the student who rarely speaks but changes the direction of a discussion, the course concept that sounds familiar but has not actually landed.

For institutions trying to prepare students for work and leadership, that visibility matters. It gives faculty better information. It gives students better feedback. And it reframes AI as a tool for strengthening the human parts of education.

Technical Notes

The system runs on OpenAI’s GPT-5 family at high reasoning effort, using GPT-5.4 as the default analysis model and GPT-5.5 selectively for stages where we’ve validated that it adds value. For each lecture or team meeting, a series of calls maps both the intellectual structure of the discussion and the nature of each participant’s contributions. The participation assessment runs as an ensemble of calls that vote, so the judgment reflects stability rather than a single model pass. This enriched analysis is then fed to a final model that drafts the narratives shown in the screenshots. Because the work scales with the content of the session, a single lecture can consume hundreds of individual model calls. Our analysis models are all from OpenAI, and the system upgrades to newer models as they prove out. We began on GPT-5.2 and now run GPT-5.4 and GPT-5.5.

Prof. Diverís Vega López is a professor and researcher from Puerto Rico whose work bridges language education, instructional design, and the ethical integration of artificial intelligence in learning. In this interview, López discusses how AI is being used her undergraduate course English and Writing for the Professional World.

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

Intro

What if learning English as a second language could feel less intimidating, more personal, and deeply empowering? For Prof. Vega, a professor at Universidad del Sagrado Corazón in Puerto Rico, this question is at the heart of her teaching. Through innovative use of ChatGPT and a custom-built GPT called “Hello Inglés,” she’s reimagining how undergraduates—many of whom bring a wide range of language backgrounds—find their voice in English. In this interview, Prof. Vega shares her approach, practical classroom strategies, and the impact these tools are having on her students’ confidence and communication skills.

The Interview

Q: You teach first- and second-year undergraduates in Puerto Rico, many of whom have very different experiences with English. What challenges do you see in supporting such a diverse group?

López: The range is significant. Some students are almost bilingual, often because of family, school, or media, while others have very limited exposure to English in daily life. These differences often reflect broader socioeconomic and educational backgrounds. So, my instruction needs to be flexible and equitable, and most importantly, it needs to build the confidence of my students. The goal is always to help students find their own voice in English, not just to meet a standard.

Q: How do you use generative AI, specifically ChatGPT, to support that goal?

López: I use two main approaches and one custom tool. First, there’s a feedback loop for writing assignments. Students draft materials like cover letters or resumes, then use ChatGPT to get targeted feedback using a prompt I provide. We review the AI’s notes together in class, discuss what’s useful and why, and then students revise their work. They can also ask ChatGPT to generate an improved version based on the feedback, and we compare the original with the AI’s revision. This helps students identify what sounds generic versus what really reflects their intent and personality.

Q: Can you share a concrete example of a prompt or exercise you use?

López: Absolutely. For example, I’ll have students ask ChatGPT to “act as a hiring manager” and evaluate their cover letter or resume for clarity, tone, and relevance. ChatGPT gives detailed feedback—five strengths, five specific improvements, with examples.

We then discuss as a group: Did they agree with the feedback? What surprised them? After that, I’ll have them request a revised version from ChatGPT, but with a one-sentence rationale per paragraph. We compare, discuss what feels human or generic, and students add personal anecdotes or details only they can provide. The process keeps their voice at the center.

Q: How do you approach speaking and real-time communication practice?

López: We use ChatGPT with the voice feature enabled, so students can practice live conversation. They ask questions, respond to follow-ups, and practice repair strategies in real time. For students who are shy about speaking English, I encourage them to ask questions in Spanish and have ChatGPT reply in English. This helps them process and interpret answers in a way that feels safe and manageable. I also provide prompts like, “Be my conversation partner on the topic of internships. Ask one question at a time, and if my answer is short, politely push me to elaborate.” At the end, ChatGPT gives pronunciation tips and useful expressions. This makes practice accessible and low-pressure.

Q: You’ve also built a custom GPT, Hello Inglés. What inspired you to create it, and how does it work?

López: When OpenAI released the ability to create custom GPTs, I saw an opportunity to simplify and personalize support for my students. I built Hello Inglés as a friendly practice tutor that can hold voice conversations, break down grammar with simple examples, clarify literature passages, and guide students through the writing process—without ever ghostwriting. If a student asks for a full essay, Hello Inglés redirects them to create an outline, asks for personal experiences, and provides drafting checkpoints. It’s designed to coach, not replace, and to always protect the student’s unique voice. I embedded it in our learning management system so students can access it alongside rubrics and assignments.

Q: What impact have you seen on students since integrating these AI tools?

López: Students report feeling less shy about speaking English, and many say the voice demo is a “game changer.” Having a tool they can use on their own builds confidence and independence. In class, these students participate more and express how much they enjoy the dynamic. Many also tell me that grammar finally “clicks” when ChatGPT explains it step by step. Most importantly, they can see the difference between AI-generated text and writing that carries their personal story—and they choose the latter. Little by little, their language learning world expands with the help of AI.

Q: What advice would you give to other educators who want to try this?

López: Start small, with manageable tasks you can demonstrate. Try one assignment using ChatGPT just for feedback, then have a class discussion and a short reflection. Always keep students’ voices at the center, and set clear rules: disclose AI use, verify facts, respect privacy, and never submit AI-generated work as your own. Reflection is non-negotiable—it’s where real learning happens. With thoughtful design, ChatGPT can become a bridge to learning, not a shortcut. Students in Prof. Vega’s class report greater confidence and engagement after using AI tools for language practice.

What Stands Out

Core idea: Vega’s approach centers on using AI not as a replacement for student effort, but as a scaffold for developing authentic communication skills and personal voice in English.

Classroom design: Her feedback loop—combining AI-generated critique, group discussion, and iterative revision—ensures students remain active participants, learning to distinguish between generic output and their own unique contributions.

Student impact: Students report increased confidence, reduced anxiety, and deeper engagement. The ability to practice independently and receive tailored support, especially through voice features, is transformative for many.

Transferable lesson: Thoughtful integration of AI, with clear boundaries and a focus on reflection, can bridge gaps in language learning and empower students to take ownership of their growth.

Bio

Diverís Vega López is a professor and researcher from Puerto Rico whose work bridges language education, instructional design, and the ethical integration of artificial intelligence in learning. With experience teaching from middle school to higher education, her research explores how AI is transforming language, literacy, and communication in academic contexts.

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Dr. Jalal Sarabadani is an educator and curriculum designer passionate about blending technology, creativity, and real-world learning in the classroom. In this interview, Sarabadani discusses how AI is being used in Systems Analysis and Design.

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

Intro

Dr. Jalal Sarabadani is passionate about blending technology, creativity, and real-world learning. At San Jose State University, he teaches Systems Analysis and Design, a course where students must master the abstract yet practical skills of modeling information systems. In this interview, Dr. Sarabadani explains how he uses ChatGPT to scaffold group exercises, accelerate feedback, and foster a more inclusive, iterative, and collaborative classroom environment. His approach demonstrates not only how AI can save instructors time but also how it can empower students from diverse backgrounds to engage more deeply with complex material.

The Interview

Q: Dr. Sarabadani, can you walk us through how you use ChatGPT in your Systems Analysis and Design class?

Sarabadani: Absolutely. One of the core exercises in my class is having students draw a logical version of an information system—specifically, context diagrams. These diagrams help students visualize how entities, such as users or administrators, interact with a system and what data flows between them. To make this process more approachable, I use ChatGPT as both a brainstorming assistant and a constructive reviewer.

Q: What does the exercise look like in practice?

Sarabadani: I start by preparing a simple prompt for ChatGPT. For example, I’ll ask it to act as a professional analyst and provide step-by-step guidance for drawing a context diagram for a job portal system. I distribute this prompt to students, who are grouped in teams of five. Their first task is to use ChatGPT to brainstorm and collaboratively create their initial diagram draft, all within about 10–15 minutes.

Q: How do students interact with ChatGPT during this phase?

Sarabadani: Each group engages with ChatGPT on their laptops or phones, inputting the prompt and following the AI’s step-by-step suggestions. ChatGPT is particularly helpful at breaking down the task—defining the system, identifying external entities, and labeling data flows. It also suggests clear, professional labels, which is critical since many students are still building their technical vocabulary.

Q: What happens after the first draft is complete?

Sarabadani: After about 15 minutes, I ask students to upload a photo of their drawing to ChatGPT and prompt it to act as a constructive reviewer. They request feedback on their diagram—what’s working, what could be improved, especially around labeling. ChatGPT’s image processing is surprisingly good, even with handwritten diagrams. It can spot issues like improper labeling, such as using verb phrases where only nouns should be used, and it gives actionable feedback.

Q: How do students respond to this feedback loop?

Sarabadani: They revise their diagrams based on ChatGPT’s suggestions, then sometimes repeat the process for further refinement. This iterative feedback cycle is powerful. Before using ChatGPT, I could only give one round of feedback per class session. Now, every group gets personalized, immediate feedback, which dramatically increases the number of iterations and the depth of learning.

Q: What impact have you noticed on students, especially those from diverse backgrounds?

Sarabadani: ChatGPT helps level the playing field. Many of my students are multilingual, and English labeling can be a barrier. With ChatGPT, they get clear, accurate language support, which boosts their confidence and participation. The AI also encourages more collaboration—students discuss and debate ChatGPT’s suggestions, cross-checking them with their own understanding. It’s a human-in-the-loop process that strengthens both technical and teamwork skills.

Q: Do you use ChatGPT for anything beyond diagramming and feedback?

Sarabadani: Yes, I encourage students to ask ChatGPT to generate a summary or a study guide based on their chat history. ChatGPT can create a downloadable PDF that students can save and review later. This makes the learning process more sustainable and gives them a personalized resource for future reference.

Q: How has this changed your own teaching workflow?

Sarabadani: It’s made assignment creation and feedback much faster for me. ChatGPT acts like a teaching assistant—I can focus more on facilitating learning and less on repetitive tasks. The classroom feels more dynamic and inclusive, and students are more engaged.

What Stands Out

Core idea: Dr. Sarabadani’s approach demonstrates how AI can act as both a brainstorming partner and a constructive reviewer, accelerating student learning and enabling more feedback cycles than traditional methods.

Classroom design: By structuring group exercises around ChatGPT prompts and feedback, he creates a collaborative, iterative environment where students learn both technical skills and teamwork.

Student impact: The use of ChatGPT lowers language barriers, builds confidence, and encourages students from diverse backgrounds to participate more actively and thoughtfully in complex assignments.

Transferable lesson: Integrating AI as a co-pilot in classroom exercises can save instructors time, deepen student engagement, and make advanced concepts more accessible to all learners.

Bio

Dr. Jalal Sarabadani is an educator and curriculum designer passionate about blending technology, creativity, and real-world learning in the classroom. He focuses on helping students build critical thinking and entrepreneurial skills through engaging, project-based experiences. He is especially interested in how AI can support authentic learning and empower both students and teachers.

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Dr. Mahipal Jadeja is an Assistant Professor at Malaviya National Institute of Technology Jaipur (MNIT Jaipur) and an advocate of AI-powered education. In this interview, Jadeja discusses how AI is being used in his Programming with Python course.

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

Intro

Teaching programming to first-year undergraduates in India brings unique challenges: language barriers, the need for personalized learning, and the struggle to make abstract concepts relatable. Dr. Mahipal Jadeja, Assistant Professor at Malaviya National Institute of Technology Jaipur, an Institute of National Importance in India, has tackled these challenges head on by developing Python Ki Pathshala, a custom GPT designed to meet his students where they are. In this interview, Dr. Jadeja shares the motivations, design, and impact of his AI-powered tutor, offering a glimpse into the future of inclusive, story-driven coding education.

The Interview

Q: Dr. Jadeja, you’ve spoken about three core challenges in education: language, personalization, and the lack of narrative. Why do these matter so much in your teaching context?

Jadeja: Teaching isn’t just about delivering content, it’s about connecting with learners. In India, language is often an invisible barrier. While higher education is mostly in English, many of my first-year students come from Hindi-medium backgrounds. If we explain concepts in a language they’re truly comfortable with, learning becomes more enjoyable and meaningful. Personalization is equally important. We can’t teach students well if we don’t know them well, yet most materials ignore students’ backgrounds, interests, or learning pace. Finally, narrative is often missing. Facts and theorems are necessary, but stories make concepts live in students’ hearts. That’s why I wanted to address all three challenges with Python Ki Pathshala.

Q: How does your custom GPT, Python Ki Pathshala, address these specific barriers?

Jadeja: The GPT’s default response language is Hindi, which aligns with the linguistic reality of my students. But it’s flexible: students can switch to English or Hinglish with simple commands. Personalization comes through features like metaphors and stories tailored to individual interests. For example, if a student loves cricket or Instagram, the GPT will use those as metaphors to explain programming concepts. The story feature is especially powerful: students can enter their name and hobby, and the GPT generates a story mapping their passion, like chess or dance, to the coding topic at hand. This makes abstract concepts concrete and memorable.

Q: Can you walk us through a typical student interaction with Python Ki Pathshala? What does the workflow look like?

Jadeja: Absolutely. A student might start by typing a Python topic, say “for loops.” The GPT responds with a mini-lesson: it starts by explaining why the concept matters, then uses metaphors aligned with student interests, followed by syntax, examples, a quiz, and recommended videos. If a student wants more relatable metaphors, they type “M” for more metaphors. For real-world applications, “R” generates practical use cases. The story feature, “S,” lets students enter their name and hobby, and the GPT crafts a story connecting their interest, like chess or a favorite web show, to the programming concept. There’s also a practice feature, “P,” for partially filled code, and language modes can be toggled for comfort. The system is trained on my own teaching materials, so it reflects my style and methodology.

Q: What kind of feedback have you received from students? Has it changed the classroom experience?

Jadeja: The feedback has been overwhelmingly positive. Students appreciate the revision support, video links, practice problems, interactive interface, and most of all the language and metaphor support. Many have said it’s the best resource they’ve found, especially because it relates concepts to real life. One student said, “It’s like it reads my mind.” That’s the ideal as a teacher: to bridge the knowledge gap by truly understanding what students need.

Our data shows a 4.58 out of 5 overall rating based on feedback from 73 students, with strong use of both English and Hinglish modes. Python Ki Pathshala has also supported more than 1,000 student learning conversations so far. The most valued features are real-world use cases and storytelling. It’s not just students, faculty are also using the GPT to bring personalized stories and applications into their own teaching.

Q: What do you see as the broader impact or potential of this approach? Could it work beyond Python or your own classroom?

Jadeja: The biggest advantage is limitless personalization. As a human teacher, I can only cover common interests in class. But the GPT can generate millions of personalized examples and stories, making learning deeply relevant for every student. While my GPT is designed for Python, the core ideas are universal. Any educator can use prompts or custom GPTs to generate personalized, story-based explanations in any subject. I’ve even seen students use it for physics concepts. This approach can transform how we teach, making education more inclusive, engaging, and effective.

What Stands Out

Core idea: Custom GPT Python Ki Pathshala demonstrates how AI tutors can break down language barriers, personalize learning, and make abstract concepts relatable through stories and metaphors.

Classroom design: The tool integrates language flexibility, topic-based mini-lessons, and story-driven examples, all modeled after the instructor’s own teaching style, to create a highly interactive and student-centered learning environment.

Student impact: Students report increased motivation, deeper understanding, and a sense of being truly “seen” by the tool, reflected in a 4.58 out of 5 rating from 73 students and more than 1,000 student learning conversations to date.

Transferable lesson: Personalization and narrative are not just nice to have, they are transformative. Any educator, in any subject, can leverage AI to create more inclusive and engaging learning experiences.

Bio

Dr. Mahipal Jadeja is an Assistant Professor at Malaviya National Institute of Technology Jaipur and an advocate of AI-powered education. He develops custom AI tutors and conducts workshops on coding, AI literacy, and prompt engineering.

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Issue 2: June 18, 2026

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

In This Issue

• Feature Story: Rebooting the Writing Classroom

• Product Corner: Build your own Agent from a template

• Prompt to Prompt: Turn a concept into an infographic

• Codex Summer Challenge: Create a lesson package

• Inside Track: Make progress on a task

• Around the World: Armenia joins OpenAI Education for Countries

• Learning with OpenAI: OpenAI Academy launches three new AI courses

• Quick Links

Feature Story

Rebooting the Writing Classroom

Dr. Jeanne Beatrix Law is pioneering new territory in writing instruction at Kennesaw State University. As both a researcher and educator, she’s been integrating generative AI into undergraduate and graduate writing courses. Her approach? Treat AI not as a shortcut, but as a thought partner—one that students can collaborate with, question, and learn from, all while maintaining their own voice and agency. In this conversation, Dr. Law shares how she and her students have developed custom GPTs to engage with primary sources and open educational resources, the frameworks that ensure ethical and effective use, and the real-world impact of these tools in the classroom.

Read the full interview.

Product Corner

Create an Agent from a template

Create your own Chief of Staff agent right in ChatGPT with templates. Just choose Agents on the left of your ChatGPT screen, browse, and explore some of the options. Let us know what you’re building!

Prompt to Prompt

Turn a concept into an infographic

Use ChatGPT to turn a concept for your class into a full-blown visual. Swap in your own concept, or try the photosynthesis example below.

Prompt to try in ChatGPT:

Create a professional educational infographic explaining photosynthesis for first-year college students. 
Include labeled diagrams, key vocabulary, visual callouts, a clean academic design, and a color palette appropriate for science education. 
Make it suitable for a lecture slide.

Open this prompt in ChatGPT

Codex Summer Challenge

Create a lesson package

Here’s a Codex idea for you to try before the next school year: build a customized lesson package for a specific grade level, subject, and topic, tailored for your class.

You can copy the whole prompt or start with a simpler one. Either way, Codex will walk you through step by step, and you can shape it after the first output by giving more instructions.

Prompt to try in Codex:

Hi Codex. I’m a teacher trying Codex for the first time. Help me create a complete lesson package.
Ask me one question at a time, and wait for my answer before asking the next one. Ask no more than 8 questions unless truly needed. Ask only what you need: what my students call me, grade/course, topic, class length, objective, student context, materials, differentiation needs, and visual style.
For visual style, offer a few options for the HTML page and optional LinkedIn card, then let me answer in my own words. Example styles: clean classroom, classic academic, hip modern, steampunk, subject-themed, or something else I describe.
One question should ask whether I want to use any external docs, files, or connectors, such as standards, slides, Google Drive, or an existing worksheet. Do not require them; continue without them if I say no.

Then create two files:
- `lesson-package/index.html`: a beautiful browser page with copy buttons
- `lesson-package/lesson-package-google-doc.docx`: a real Google Docs-ready DOCX file I can upload/open directly, without a connector

Both files should include:
- A teacher lesson plan with timing
- A lecture hook
- Entry and exit tickets
- One interactive activity
- A student handout
- Homework
- An advanced extension
- A LinkedIn-ready teacher reflection with 3 hashtags

Before finishing, check timing, alignment, accessibility, and whether the exit ticket measures the objective. Give me both local paths and open the HTML if possible.
After both files are complete, ask whether I want a unique 16:9 LinkedIn image. If yes, create `lesson-package/linkedin-summary-card.png` with specific details from my lesson package and the hashtag `#createdwithcodex`.

Open the prompt in Codex

Inside Track

Make progress on a task you’re stuck on

Having trouble starting something that’s hard or that you’ve been putting off a little too long? Let ChatGPT help you make some quick progress.

Prompt to try in ChatGPT:

I have 20 minutes. Help me make progress on [task]. 
Give me the smallest useful next step and a quick draft I can improve.

Open the prompt in ChatGPT

Around the World

Armenia joins OpenAI Education for Countries

Armenia took a major step toward AI-native education and innovation. OpenAI Education is supporting a new collaboration with Armenia’s Ministry of Education, Science, Culture and Sport and Firebird to expand access to ChatGPT Edu and Codex for 50,000 students, teachers, and researchers, alongside faculty training programs and research.

The work is designed to help Armenia’s universities, educators, founders, engineers, and researchers build the skills and practical experience needed to shape what comes next. It also reflects the next phase of AI in education: pairing tools with training, research, local priorities, and responsible deployment so more people can learn, build, and compete globally.

Read more.

Learning with OpenAI

OpenAI Academy launches three new courses

OpenAI Academy just launched three new courses to help learners build practical AI skills. It’s a useful resource for educators, students, and campus teams looking for structured learning they can try at their own pace.

• AI Foundations

• Applied AI Foundations

• Agents and Workflows

Explore the new courses on OpenAI Academy

Have a story idea?

We’d love to hear how you’re using AI in your classroom, campus, research, or learning community. Send us the story, project, prompt, or question you think other educators should see.

Quick links

• OpenAI for Higher Education

• OpenAI for K12

• OpenAI Academy

• OpenAI Forum

• ChatGPT for Education on LinkedIn

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Nicos Savva teaches analytics and data science to MBA and leadership audiences at London Business School.

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

Intro

For nearly 20 years, Nicos Savva has taught analytics and data science to MBA students and executive audiences. His students are often experienced leaders with deep domain expertise, but not necessarily coding backgrounds or advanced mathematical training.

That created a familiar teaching problem: the people closest to business judgment were often separated from data analysis by technical tools.

Generative AI changed the shape of that problem. In his online course, Business Analytics in the Age of Generative AI, Savva explores what happens when executives can ask for a histogram, regression, confidence interval, or predictive model in plain language.

The Interview

Q: What problem were you trying to solve when you created this course?

Savva: For most of my career, when I was teaching analytics, I ran into the same wall. My audience was brilliant, but most of them were not coders and did not have advanced mathematical training.

That meant I was limited in what I could practically teach. I would spend part of my time explaining the intuition behind methods like data visualization, linear regression, k-means clustering, and their business impact. The other part was spent wrestling with no-code tools and workarounds.

Then tools like ChatGPT made analytics doable in plain language. Suddenly, my audience could ask for a histogram, a confidence interval, a logistic regression, or a power analysis without programming and without touching Python or R. My classroom shifted from the mechanics of how to do analytics to the more important questions: why we do it, what can go wrong, and what decision it supports.

Q: What did that change make possible for executives?

Savva: It changed access. In the course, we give executives real challenges and ask them to do the work. For example, we give them a London housing dataset and ask what happened to prices around Brexit and COVID.

In seconds, they can produce analysis that might previously have taken a data team hours. But the real transformation is not just speed. It is that an executive with 20 years of property investment experience can test their market intuition directly against data.

They can ask follow-up questions, slice the data by neighborhood, compare metrics, and do it in real time. The barrier between having a hypothesis, testing it, generating insight, and telling a data-driven story has collapsed.

The course asks executives to use generative AI to analyze business datasets and move quickly from question to evidence.

Q: You describe this as more than a technical upgrade. What’s the deeper shift?

Savva: The deeper shift is that analytics becomes a strategic thinking tool that leaders can use directly. Predictive models like logistic regression are useful for customer churn, loan default, drug effectiveness, or employee attrition. These models create enormous business value, but teaching busy executives how to run them and interpret the findings used to feel like teaching ancient Greek.

Now an executive can ask, in plain English, which loans are likely to default based on borrower characteristics and receive a working model with performance metrics. The ancient Greek has become plain English.

That does not make domain expertise less important. It makes it more important. The skill is not generating the histogram or model. The skill is knowing which questions to ask and how to turn insights into decisions.

Q: Where does responsibility come in?

Savva: With great power comes great responsibility. When anyone can build a predictive model in minutes, people need to recognize when a model is heading down the wrong path or when a result might be a hallucination.

It is not enough to know what questions to ask. You also need to know what a good answer looks like. Part of the course focuses on analytic intuition: when to be suspicious, how to spot leakage, and when sample size should make you nervous.

This is not about turning executives into statisticians. It is about helping them catch dangerous mistakes before those mistakes become business disasters.

Savva emphasizes that the goal is not just faster analysis, but better judgment about models, evidence, and decisions.

Q: Does this replace data teams?

Savva: No. The point is not to replace data teams with large language models or artificial intelligence. The point is to make senior leaders better partners.

They can prototype quickly. They can ask sharper questions. They can interpret results more responsibly. Analytics moves from a specialized function that leaders consume to a strategic thinking tool they can use directly.

Q: What has this changed in your teaching?

Savva: My teaching has not become about AI. It is still about analytics and what humans can do with evidence. AI removes the mechanical barriers so non-technical leaders can access data, think better, reduce uncertainty, and do it responsibly.

That is the real upgrade.

Bio

Nicos Savva is Professor of Management Science and Operations at London Business School, where he researches and teaches data science, operations, and healthcare management. His work has been published in leading academic journals and has influenced practice in industries including healthcare, retail, and technology. He is passionate about equipping leaders to harness analytics and AI to make better, evidence-based decisions.

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Adam B. Lockwood teaches graduate courses in school psychology at Kent State University, including internship seminar. In this interview, Lockwood discusses how AI is being used in School Psychology Internship.

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 take to prepare graduate students for the real-world complexities of school psychology? For Dr. Adam B. Lockwood, Associate Professor at Kent State University, the answer is a blend of hands-on practice, ethical rigor, and creative use of AI. With a background as both a practitioner and educator, Dr. Lockwood has become an active voice in the responsible integration of AI into school psychology training—making learning more engaging, practical, and safe. In this interview, he shares how AI-generated data, interactive tools, and games are transforming the field, and why transparency and ethics are at the heart of his approach.

The Interview

Q: Dr. Lockwood, you describe yourself more as a trainer than a traditional professor. How does that shape your approach to preparing school psychologists?

Lockwood: My students are on the cusp of becoming practicing school psychologists, so I focus on applied, hands-on activities. The internship year is where theory meets reality, and I want them to have as many opportunities as possible to practice in a safe, low-stakes environment before working with real students. That’s where technology—and especially AI—has become transformative.

Q: What was your first step in bringing AI into your training?

Lockwood: One of the earliest things I did was generate mock student data using ChatGPT. Traditionally, students would practice writing reports or interventions using real cases, which is high stakes—mistakes can have real consequences. By creating virtual practice scenarios, students can hone their skills, get feedback, and build confidence without risk. I make all these resources freely available on OSF (Open Science Framework) to support transparency and sharing.

Q: What impact have you seen from these AI-generated practice opportunities?

Lockwood: The difference is clear: students gain confidence, work more efficiently, and their skills become more polished. In fact, I conducted a study with about 250 licensed psychologists comparing reports written by humans and by ChatGPT. Surprisingly, there was no significant difference in preference—despite using a single, non-iterative AI prompt. It shows the huge potential for AI to support both learning and administrative tasks in psychology.

Q: Beyond data sandboxes, you’ve created interactive games for your students. Can you share an example?

Lockwood: Absolutely. I developed a game inspired by the old Oregon Trail, but tailored for school psychology. Using ChatGPT, I brainstormed scenarios that mimic real-world challenges practitioners face—like tricky interpersonal dynamics or ethical dilemmas. I coded the game with HTML, CSS, and JavaScript. The game gives students a playful but realistic way to experience consequences of their choices, something lectures alone can’t provide.

Q: How do you use AI to support more traditional, didactic courses?

Lockwood: When I teach behavior assessment, for example, I use AI to generate matching games and interactive activities. ChatGPT helps me brainstorm scenarios and language, and then I use other tools for coding. These games provide instant feedback—so students learn actively rather than just passively listening or waiting for grades. Three years ago, this would have taken me days or weeks. Now, I can build an engaging activity in half an hour.

Q: Ethics and governance are big themes in your work. How do you help students and practitioners navigate the risks of using AI tools?

Lockwood: Choosing the right AI tools is critical—especially when handling sensitive student or patient data. I’ve built interactive checklists and logic tools, based on APA guidelines, to walk users through the decision-making process. These tools, developed with ChatGPT and others, give real-time feedback and help ensure compliance with ethical standards. It’s a way to make complex, high-stakes decisions more accessible and transparent.

Q: What’s the feedback been like from your students and colleagues?

Lockwood: Overwhelmingly positive. Students appreciate the instant feedback and interactive nature of the activities. Practitioners find the tools practical and time-saving. The biggest shift is moving from traditional assignments—papers and delayed feedback—to dynamic, engaging practice that’s both fun and deeply educational.

What Stands Out

Core idea: AI-generated mock data and interactive games provide low-stakes, hands-on practice for school psychology students.

Classroom design: The integration of AI tools enables rapid creation of engaging, feedback-rich activities that replace traditional assignments.

Student impact: Students gain confidence, work more efficiently, and develop polished skills through safe, realistic simulations.

Transferable lesson: Combining ethical frameworks with AI-driven tools supports transparent, practical training adaptable across disciplines.

Bio

Dr. Adam B. Lockwood Adam B. Lockwood, Ph.D., NCSP, is an Associate Professor of School Psychology at Kent State University. His work focuses on responsible AI use in psychology and education, including AI-assisted psychological report writing, AI governance and ethics, and AI-supported research workflows. He chairs the National Register of Health Service Psychologists’ AI and Emerging Technologies Committee and has served on AI task forces for the National Association of School Psychologists and the Ohio School Health Services Association. His AI-focused scholarship has been recognized by Contemporary School Psychology, which named his article on GPT-4 and school psychology writing its 2025 Article of the Year.

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On Friday, we welcomed the inaugural ChatGPT Futures Class of 2026 to our San Francisco headquarters to celebrate what they’ve made possible by putting frontier AI to work.

On behalf of the OpenAI team, I had the privilege of delivering a commencement speech I wanted to share with you. My hope, in the words of Nolan Windam, is that this generation will recognize their place as teachers for a society looking to learn to use the technology of the future.

Congrats to the Class of 2026.

Welcome, ChatGPT Futures Class of 2026.

Over the last year, I’ve met students around the world using AI in remarkable ways.

What struck me wasn’t the technology, but what people like you chose to do with it.

While much of the world was debating what AI might mean someday, you were already putting it into practice.

And that’s why we’re here today.

The Class of 2026 is unique—it is the first graduating class to go through four years of college with ChatGPT.

Commencement speeches will say some version of the same thing: follow your dreams, work hard, be kind, do not fear failure.

All good advice. But, not quite enough for you.

Because you are not graduating into the world you were prepared for. You are graduating into a world where intelligence is suddenly everywhere—answers, drafts, ideas—all on demand.

Many students and recent grads ask me—how can we prepare to be successful in this type of world?

The better question is: what kind of person becomes more valuable when everyone has access to intelligence?

I think the answer is: a person with agency, just like the people sitting in this room.

You all know what it means to have agency because you’ve already demonstrated it.

You did not wait until someone gave you permission, a title, a perfect plan, or a guaranteed path. you just got started.

What’s even more special is the problems you chose to take on in the world:

Rerouting food waste away from landfills.

Helping your peers find scholarships.

Driving access to education.

—the human stuff.

And that’s why I’m optimistic.

Because when I look at this room, I don’t just see what AI can do. I see what people can do when they have access to tools that expand their ability to drive impact.

We brought you together today not just in celebration, but because we believe something bigger is possible when people like you find one another.

Last night at dinner I asked the honorees: What would it take to build a movement of high-agency young people who feel as empowered by this technology?

I’ve been thinking about that question a lot lately because I want my own children to feel the way many of you do.

In fact, my son is here with us today.

He sees the same headlines everyone else sees. He hears the same fears and uncertainty about what AI might mean for his future.

And when I look at him, I find myself wondering: How do we help more young people move from fear to possibility? From consuming technology to creating with it? From watching change happen to helping shape it?

I don’t know the full answer yet.

Movements are these mystical creatures but we do know that they spread.

They spread when someone sits down with a friend, a classmate, a colleague, or a family member and shows them what’s possible.

They spread when we share what we’ve learned.

They spread when we help someone take their first step.

So my ask of you is simple: Keep building. But don’t stop there.

Tell your stories. Share your experiments. Show people what’s possible.

Help others discover the same sense of agency that brought you here.

Because the future of AI won’t just be shaped by the people who build the technology.

It will be shaped by those who use it to solve real problems and inspire others to do the same.

Congratulations ChatGPT Futures Class of 2026.

Anna Marbut teaches Applied Artificial Intelligence and Applied Data Science at University of San Diego.

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 bring AI-powered teaching assistants into a fully online, asynchronous graduate program? Anna Marbut, Professor of Practice at the University of San Diego, has spent the past two years answering that question. In this conversation, she shares the story behind her team’s integration of OpenAI’s Assistant function into their Applied Artificial Intelligence and Data Science masters programs, how the system works, what the impact has been, and where she sees the greatest potential for students and faculty alike.

The Interview

Q: Anna, let’s start with the basics. What motivated you and your team to bring AI teaching assistants into your online programs?

Marbut: Our programs are fully online and asynchronous, serving mostly working professionals—people advancing in their careers or changing industries. About two years ago, we realized that the lack of real-time interaction was a challenge. Students needed more immediate support, especially outside of traditional office hours. That’s what led us to experiment with AI teaching assistants, accessible through our Slack channels.

Q: How did you go about building these AI TAs? What does the setup look like behind the scenes?

Marbut: We used OpenAI’s Assistant function. In our OpenAI API account, we set up assistants for each course, plus one for administrative questions—things like enrollment, payments, and deadlines. Each assistant is configured with system instructions: what the course covers, how to respond, and, importantly, what not to do. For example, we tell it not to give out direct answers to quizzes or assignments. We also uploaded a full dump of our Canvas course materials—discussion prompts, quizzes, assignments—so the assistant can use retrieval-augmented generation (RAG) to answer questions based on actual course content. Since our students often have coding questions, we enable the code interpreter as well.

Q: How do students actually interact with the AI assistants?

Marbut: We integrated the assistants into Slack using a third-party connector. Students can message the TA privately or tag it in any course channel. For example, if a student is stuck on a homework assignment, they can ask for tips and get a response directly in the thread. The assistant references the relevant course material and provides guidance—sometimes even example code—but always avoids giving away answers outright. If a student tries to get the quiz answers, the assistant politely refuses and offers study tips instead.

Q: What kind of impact have you seen so far—on students, faculty, and administrators?

Marbut: Since launching, our AI TAs have answered about 200 questions in eight months. That’s 200 questions our professors and administrators didn’t have to handle directly. Faculty have noticed a shift: the questions that come up in office hours or on Slack are deeper and more thoughtful—students seem to be doing more troubleshooting on their own before reaching out. Administrative staff are also getting fewer routine questions about enrollment or deadlines, since the bot handles those. Of course, we’d like to see even more usage, so we’re working on messaging and fine-tuning the bots based on student feedback.

Q: What are some of the challenges or next steps?

Marbut: The biggest challenge is awareness—making sure students know these tools are available and feel comfortable using them. We’re also continually improving the assistants, both in terms of accuracy and helpfulness. In a fully online, asynchronous program, having a 24/7 TA is a game-changer, especially for working professionals who study after hours or on weekends. But we’re still learning how to maximize their impact.

What Stands Out

Core idea: AI teaching assistants provide immediate, 24/7 support in fully online, asynchronous graduate programs serving working professionals.

Classroom design: The AI TAs are integrated with Slack and Canvas, configured to guide students using course materials without giving direct answers to assessments.

Student impact: Students receive timely help that encourages independent troubleshooting, while faculty handle fewer routine questions and focus on deeper interactions.

Transferable lesson: Thoughtful integration and clear boundaries for AI assistants, combined with ongoing feedback and communication, enhance support in online education.

Bio

Anna Marbut is a Professor of Practice in the Master’s in Applied Artificial Intelligence and Master’s in Applied Data Science programs at the University of San Diego.

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Dr. Michael Atkinson, educator and consultant, shares how he used GPT to transform his Management Communication course at BYU.

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

Intro

When Dr. Michael Atkinson took over a Management Communication course at Brigham Young University mid-semester, he faced a challenge familiar to many educators: disengaged students and a classroom that felt unusually quiet. Drawing on his expertise in instructional psychology and technology, Dr. Atkinson turned to an unconventional collaborator—ChatGPT—not just as a tool, but as a tutor and thought partner. What followed was a series of experiments that led to a remarkable turnaround in student connection, engagement, and learning outcomes. In this interview, Dr. Atkinson shares the story behind his approach, the theory and practice that guided it, and the impact he observed.

The Interview

Q: You walked into a class mid-semester and immediately sensed something was off. Can you describe that moment and what you noticed?

Atkinson: Absolutely. I love teaching, and I usually feel a strong connection with my students. But this time, the classroom was unusually quiet—almost like I was talking into a void. Even after introducing myself via video, the analytics showed only two students had watched. The instructional design was solid, but engagement was missing, and with midterms looming, I didn’t have time for a deep diagnostic.

Q: How did you decide to bring a custom GPT into the process, and what was your mindset?

Atkinson: I did what I encourage my students to do: I treated ChatGPT not just as a tool, but as a tutor and a thought partner. I laid out the problem as I would to a trusted colleague, even uploading the syllabus and describing the class dynamics. The key was to approach the conversation with ChatGPT as a dialogue, not just a search for quick answers.

Q: What did that back-and-forth with ChatGPT look like? What did you learn from it?

Atkinson: It was surprisingly rich. ChatGPT didn’t just give me static information—it asked me questions, pushed me to clarify, and brought in relevant research about discussion boards and student engagement. Some of my best ideas came from this iterative exchange. For example, ChatGPT helped me realize that the core issue was a lack of connection—between students and with me.

Q: Can you share a concrete example of how you used a custom GPT to personalize learning for your students?

Atkinson: One of the most impactful changes was introducing weekly check-ins where students identified a communication skill they wanted to improve. I’d paste their responses into a custom GPT I’d designed, which would generate a set of tailored “rules” or strategies for that student. For instance, a student wanting to work on listening skills would get specific, actionable advice—sometimes even contextualized to their major, like audiology. I’d review and personalize these further, adding my own voice and stories, and then send them back. It was individualized learning at scale.

Q: Why do you think this approach worked so well? What’s the theory behind it?

Atkinson: Personalization is powerful. When students feel seen and receive feedback that’s uniquely relevant to them, their motivation and engagement increase dramatically. Theoretically, it’s about moving from transactional to relational learning. GPT enabled me to do this at a level I couldn’t achieve alone—even as an expert in online learning. The AI acted as both a tutor, surfacing research and best practices, and a thought partner, helping me iterate and refine ideas.

Q: What was the impact on your students—and on you as an instructor?

Atkinson: The results were remarkable. My end-of-semester surveys showed a 12-20% increase in positive feedback compared to previous terms. More importantly, students reported real growth. They referenced specific “rules” they’d practiced and described tangible improvements in their communication. For me, it was deeply rewarding to see students so engaged and to witness their progress week after week.

Q: What advice would you give to other educators considering AI in their teaching?

Atkinson: Don’t limit yourself to seeing AI as just a tool. Treat it as a tutor and a thought partner. Imagine what’s possible if you approach it like a conversation with a human collaborator. Every subject and context is different, but with a bit of creativity, you can use AI to personalize learning and foster deeper engagement. We’re only limited by our imagination.

What Stands Out

Core idea: Dr. Atkinson transformed student engagement by treating ChatGPT as a tutor and thought partner rather than just a tool.

Classroom design: He used a custom GPT to generate personalized communication strategies for each student based on their individual goals.

Student impact: Students reported increased motivation, engagement, and tangible improvements in their communication skills.

Transferable lesson: Approaching AI as a collaborative dialogue enables educators to personalize learning and foster deeper connections at scale.

Bio

Michael Atkinson Dr. Michael Atkinson is an educator, advisor, and consultant focused on leadership development, learning design, and AI training. He is adjunct professor in the Marriott School of Business at Brigham Young University.

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Dr. Nydia Cappas teaches Psy 6850 Conceptualization and Treatment Planning at Ponce Health Sciences University. In this interview, Cappas discusses how AI is being used in Psy 6850 Conceptualization and Treatment Planning.

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 take to prepare the next generation of clinical psychologists in an era where AI can write papers, draft case conceptualizations, and even simulate patients? Dr. Nydia Cappas, a clinical psychology professor at Ponce Health Sciences University in Puerto Rico, has spent nearly two decades teaching—and in her words, the last few years have been the most creative and innovative of her career. In this conversation, Dr. Cappas shares how she’s leveraging AI to create authentic learning experiences, build student confidence, and foster a collaborative classroom culture.

The Interview

Q: Dr. Cappas, you’ve described the last few years as your most creative and innovative in teaching. What sparked this transformation?

Cappas: How do I ensure my students are truly learning clinical skills and competencies in an age when AI can easily generate essays or conceptualizations? I realized that traditional assessments were becoming obsolete. So, I started identifying the non-negotiable skills my students must develop—sometimes related to writing, but often more practical. From there, I brainstormed ways to assess and practice those skills, both with and without AI. The process became a journey I share openly with my students, experimenting together and learning as we go.

Q: Can you give us a concrete example of how you’re using AI in your teaching?

Cappas: Absolutely. One of my favorite innovations is a custom GPT patient simulator for teaching motivational interviewing. Motivational interviewing is a therapeutic approach that helps people make positive changes—even when they’re not ready to change. I built a GPT-based patient in the “pre-contemplation” stage, where the person doesn’t yet recognize the need for change. My students interact with this simulated patient, practicing techniques and questions in a safe environment. What’s great is that students can use this tool even with the free version of ChatGPT, so access isn’t a barrier.

Q: How do you integrate this tool into your courses, and what impact have you seen?

Cappas: I introduce the GPT patient in class so everyone understands the task and feels comfortable. Students practice live, ask precise questions, and get immediate feedback. I also use the chat logs or PDFs of their conversations as assessments, which lets me pinpoint exactly where students need more support. The feedback has been phenomenal—students are more engaged, and classes are more fun. Plus, my openness to AI has encouraged students to share their own creative approaches, like the student who wrote a salsa song about anxiety disorders!

Q: What’s your process for building these AI tools? Any advice for other educators?

Cappas: It’s simpler than people think. I used ChatGPT itself to help me build the prompt for the patient simulator. I described what I wanted, refined it through a few iterations, and tested it with students. The key is to be open, experiment, and treat it as a collaborative journey with your students. Also, think carefully about which skills should and shouldn’t be delegated to AI. We need to be at the table where these decisions are made, especially those who are concerned about AI’s impact on learning.

Q: You mentioned time management. How has AI changed your workflow as an educator?

Cappas: AI has been a game-changer for time management. I use voice mode during my commute to brainstorm and plan. It’s made my workflow more efficient and enjoyable. Sharing these tools with colleagues has also sparked a culture of innovation—professors exchange ideas and learn from each other, which benefits everyone.

Q: What’s your big-picture takeaway about AI in education?

Cappas: AI can be a powerful tool for teaching and learning, but only if we approach it thoughtfully. It can also hinder learning if misused. That’s why it’s crucial for professors to engage deeply with AI, understand its strengths and limitations, and help shape how it’s used in education. We need more educators at the decision-making table. That’s my motivation for sharing my experiences.

What Stands Out

Core idea: Dr. Cappas focuses on teaching clinical skills that cannot be replaced by AI, ensuring authentic student learning.

Classroom design: She integrates custom GPT patient simulators to provide students with safe, hands-on practice in therapeutic techniques.

Student impact: Students are more engaged, receive immediate feedback, and feel encouraged to share creative approaches in a collaborative environment.

Transferable lesson: Educators should experiment openly with AI tools, involve students in the process, and actively participate in shaping AI’s role in education.

Bio

Dr. Nydia M. Cappas is a clinical psychologist, educator, and academic leader dedicated to integrating mental and physical health in doctoral training. She blends science, innovation, and real-world application to transform how psychology is taught and practiced.

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Dr. Arka Sarkar is an Assistant Professor of Marketing at Suffolk University in Boston. In this interview, Dr. Sarkar discusses how AI is being used in SBS 298 and MKT 898.

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

Intro

Dr. Arkapravo (”Arka”) Sarkar is pioneering the integration of generative AI into business education at Suffolk University Boston. In this conversation, he shares how he leverages ChatGPT—through custom GPTs, interactive simulations, and live client projects—to create engaging, real-world learning experiences for both undergraduate and graduate students. From demystifying statistics to simulating complex systems, Dr. Sarkar’s work exemplifies the next frontier in teaching with AI.

The Interview

Q: Dr. Sarkar, you’re known for your innovative use of ChatGPT in your courses. Can you walk us through some of the ways you’re using it in your teaching?

Sarkar: Absolutely. I’ve been experimenting with ChatGPT across both my undergraduate and graduate courses. Let me highlight four key use cases. First, I created a custom GPT as a course companion for my undergraduate class, “SBS 298: Tackling Global Wicked Problems.” This GPT acts almost like a supercharged teaching assistant—available 24/7—helping students navigate course content, deadlines, and expectations. For example, students can ask, “What does Module II look like?” and instantly get a week-by-week breakdown, key highlights, and resources. It’s a way for students to stay engaged and supported outside the classroom.

Q: That’s a powerful support tool. How do you use ChatGPT to bring abstract concepts to life, especially in areas like statistics?

Sarkar: Glad you asked. Statistics can be dry, so I often try to make it more interactive with GenAI. For example, using ChatGPT’s canvas mode, I built a simulation to demonstrate the Central Limit Theorem. With that tool, students can manipulate different distributions—Uniform, Exponential, Bernoulli, Normal, etc.—adjust sample sizes, and watch how the resulting graphs change in real time. For instance, increasing the sample size smooths out the bell curve, making the concept visually intuitive. Such hands-on, visual approach helps students internalize statistical ideas that are often hard to grasp through lectures alone.

Q: You also mentioned systems thinking and stakeholder simulations. How does that work in your classroom?

Sarkar: Systems thinking is the backbone of my “Tackling Global Wicked Problems” course. Last year, we focused on food insecurity in Massachusetts as our wicked problem. I built a 12-month simulation where students could adjust variables for stakeholders like local farmers, government, nonprofits, and consumers. They’d see in real time how changes—like farmers raising prices or governments increasing support—affected food access, farmer viability, and system sustainability. I even programmed in random surprise events, like severe weather or budget cuts, to mimic real-world unpredictability. Students not only interacted with these simulations but also learned to build their own dashboards for their final projects, presenting to external guest judges.

Q: What’s next for you and your students as these AI tools keep evolving?

Sarkar: I’m excited about new features like ChatGPT’s study and learn mode, the capabilities of GPT-5, and exploring Codex for data analysis as I prepare to teach a new course, MKT 320: Marketing Research with GenAI in Fall of 2026. I’m exploring how these can further personalize learning and deepen engagement. My goal is to keep pushing the boundaries of what’s possible—helping students not just learn about AI, but use it to solve real problems and create value in the world.

What Stands Out

Core idea: Dr. Sarkar’s approach centers on making AI a hands-on partner in learning, not just a topic of study. By embedding ChatGPT into course structures, he creates environments where students can experiment, iterate, and learn by doing.

Classroom design: The classroom becomes a living lab, with custom GPTs, interactive simulations, and real-world projects. Students don’t just consume information—they build, test, and present their own solutions, often to external audiences.

Student impact: Students report greater engagement, deeper understanding of complex concepts, and enhanced confidence in using AI tools. They leave with practical skills and the experience of delivering work to real clients.

Transferable lesson: Any educator can adapt these methods—using AI to scaffold learning, visualize abstract ideas, and connect coursework to authentic challenges. The key is to start small, iterate, and invite students to co-create the journey.

Bio

Arkapravo Sarkar is an Assistant Professor of Marketing at Suffolk University in Boston. He specializes in integrating generative AI tools like ChatGPT into business education to foster innovation, systems thinking, and hands-on problem-solving. He is a recipient of Suffolk’s 2025 Innovative Teaching Award, a winner of the university’s Faculty AI Hackathon, and a recipient of the 2026 Dean’s Excellence Award in Research at the Sawyer Business School.

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Joaquín Fernández Sande, MD, is a physician, educator, prompt engineer, and AI innovator specializing in medicine and large language models. In this interview, Fernández Sande discusses how generative AI and LLMs can help doctors reduce administrative burden, improve patient care, and become builders of the tools they use.

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

Intro

Modern medicine is at a crossroads: while technology has delivered remarkable advances, it has also burdened doctors with overwhelming bureaucracy and data overload. Dr. Joaquín Fernández Sande, a physician, educator, and AI innovator at the National Academy of Medicine of Buenos Aires, is pioneering a new approach: LLM-native healthcare.

His work centers on a simple but powerful idea: doctors should not only use AI tools, they should learn to shape them. In his words, doctors should become “vibe coders,” capable of translating clinical needs into working AI-assisted workflows.

Fernández Sande also works as a prompt engineer at Quadrivia, a startup building AI-powered workflow tools designed to reduce administrative burden in healthcare. Across his teaching, research, and applied work, his mission is to help clinicians use AI safely, ethically, and creatively while restoring attention to the patient-doctor relationship.

The Interview

Q: Dr. Fernández Sande, you’ve described the current state of healthcare as “information-centered” rather than “patient-centered.” What’s driving this shift, and what are the consequences for doctors and patients?

Fernández Sande: Over the years, our healthcare system has become dominated by data and administrative demands. Doctors now spend about half their working time not with patients, but in front of computers: writing medical records, reviewing information, handling bureaucracy. This has led to a burnout rate of over 43% among physicians. The real tragedy is that, while we’re drowning in data, we’re losing focus on the human beings in front of us. Medical errors, often caused by information overload and fatigue, are now estimated to be the third leading cause of death worldwide. We’ve lost sight of the core mission of medicine: caring for patients.

Q: How does your approach, what you call “LLM-native healthcare,” address these challenges?

Fernández Sande: LLM-native healthcare is about fundamentally redesigning how we practice medicine. Instead of forcing doctors to adapt to technology, we teach them to use large language models as clinical tools that can handle repetitive, error-prone, and bureaucratic tasks. This lets doctors refocus on patient care. It’s not just about using AI, but about mastering it: becoming fluent in prompt engineering and understanding how to deploy LLMs safely and responsibly within clinical workflows. We emphasize ethics, patient safety, and professional trust at every step.

Q: You’ve also been exploring the idea that “doctors should be vibe coders.” What does that mean in a medical context?

Fernández Sande: The idea is that doctors should be able to participate directly in creating the tools they need. They don’t necessarily need to become traditional software engineers, but they should understand how to work with AI systems, prompt them effectively, prototype workflows, and translate clinical expertise into useful applications. Doctors understand the real problems in healthcare because they face them every day. If they can use AI to build and adapt tools around those problems, they become active creators rather than passive users. That is where the concept of doctors as vibe coders becomes powerful.

Q: What does this look like in practice? Can you share some examples from your teaching?

Fernández Sande: Absolutely. One of our key goals is to turn doctors into tool makers. We teach them core programming concepts, without the burden of complex syntax, so they can build their own AI applications. For example, Dr. Enrique Diaz-Canton, our course co-director and a leading oncologist, created a custom GPT for oncology in Argentina. It acts as a clinical co-pilot, integrating the latest oncology databases and guidelines. This tool has reached over 3 million user interactions, with a 4.8 user rating, and ranks in the top 5% globally on OpenAI’s custom GPT platform. Another example is our synthetic data case generator. We built a custom GPT that creates realistic, privacy-compliant patient cases for students to practice their clinical skills before exams. It adapts to different specialties, like cardiology or pediatrics, and aligns with each university’s curriculum. This lets students safely practice diagnosis and treatment without using real patient data.

Q: What’s the impact of giving doctors these AI-building skills, rather than just teaching them to use off-the-shelf tools?

Fernández Sande: It’s transformative. When doctors can create and customize their own tools, AI becomes a natural extension of their expertise. They’re not just passive users. They’re innovators, able to address their unique challenges. This also means AI solutions are better tailored to real clinical needs, leading to safer, faster, and more humane care. Importantly, we’re seeing a rapid rise in adoption: two out of three physicians now use AI in their practice, a 78% increase from just a year ago. Our mission is to ensure they do so safely, ethically, and creatively.

Q: How do you balance innovation with the ethical and safety considerations that are so vital in medicine?

Fernández Sande: We never lose sight of the doctor’s responsibility to provide safe care. Ethics, patient safety, and trust are foundational. Every tool we build is designed with these principles in mind. We teach doctors not just how to use AI, but how to critically assess its output, understand its limitations, and ensure it serves the patient’s best interests. AI should fit the problem. It should never dictate the practice of medicine.

What Stands Out

Core idea: Dr. Fernández Sande’s approach re-centers medicine on the patient by teaching doctors to delegate bureaucracy and repetitive tasks to AI, freeing up time and attention for care.

Classroom design: His courses blend prompt engineering, core programming concepts, and ethical foundations, enabling clinicians to build and deploy custom AI tools tailored to their specialties.

Student impact: Doctors and students gain the skills to innovate, creating tools like clinical co-pilots and synthetic data simulators that support safer, more efficient, and more engaging medical practice.

Transferable lesson: Empowering professionals to become tool makers, not just tool users, unlocks creativity and ensures technology serves real-world needs, an approach applicable far beyond healthcare.

Bio

Joaquín Fernández Sande, MD, is a physician, educator, prompt engineer, and AI innovator specializing in medicine and large language models. He co-directs the Postgraduate Course in Artificial Intelligence for Medicine at the National Academy of Medicine in Buenos Aires. He is also a Computer Science Master at IE School of Science and Technology and works as a prompt engineer at Quadrivia, a startup developing AI-powered workflows to reduce administrative burden in healthcare.

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Dr. Robert Voss is an Associate Professor of History and Social Science Education Coordinator at Northwest Missouri State University, where he integrates artificial intelligence into teaching, research, grant writing, and faculty development. In this interview, Voss discusses how AI is being used in U.S. History since 1877, Digital Humanities, and educator professional development.

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

Intro

Dr. Robert Voss’s journey from early adopter of classroom tech to pioneering AI-first pedagogy is a story of curiosity, adaptation, and a deep commitment to student and faculty empowerment. At Northwest Missouri State University, a medium-sized, rural public institution, he’s not only reimagined how history is taught, but also how AI can democratize high-level research, planning, and professional development.

In this interview, Dr. Voss shares how AI became his teaching superpower, the practical ways it’s reshaping his courses, and why the humanities are essential to the future of AI in education. He also explains how custom GPTs have helped him support faculty compensation work, accelerate a major federal grant application, and train more than 500 educators across K-12 and higher education.

The Interview

Q: Dr. Voss, your background shows a long-standing passion for integrating technology into teaching. How did your early experiences shape your current approach to AI in the classroom?

Voss: Technology has always been more than a tool for me. It’s been a way to expand what’s possible. I remember the screech of a 2800 baud modem as the sound of opportunity, not just noise. As a high school teacher in the late ‘90s, I was building websites with second graders and experimenting with PalmPilots before smartphones existed.

That mindset, seeing technology as a means to empower students, laid the groundwork for how I approach AI now. When I first tried ChatGPT in 2022 and saw it generate a lesson plan for second graders on the first prompt, I realized AI wasn’t just another tool. It was a superpower that could fundamentally change how we teach and learn.

Q: You’ve described shifting from “technology-first” to “AI-first.” What does that look like in practice, especially for you and your students?

Voss: For me, AI isn’t just an add-on; it’s woven into everything I do. I have ADHD, and AI helps me organize my thinking. Conversations are saved, threads can be revisited, and ideas don’t get lost. That’s transformative for me and for neurodivergent students.

In class, I treat AI as a collaborator. I build custom GPTs to serve as research assistants, guiding students through primary source analysis, helping them locate archives, or even simulating interviews with historical figures. Assignments might involve translating handwritten Civil War letters or Swedish immigration records, then interpreting them in students’ own words.

The question I always ask is: What can AI do here that frees us to focus on higher-order thinking?

Q: Can you share some concrete examples of how AI changes the classroom experience for history students?

Voss: Absolutely. Take primary source analysis. AI can quickly summarize a dense academic article, so instead of spending class time just processing information, we can debate its implications. Or AI can generate a rough historical timeline, and students then verify details and interpret context.

In my digital humanities courses, students don’t just use AI. They learn to judge its accuracy, spot gaps, and recognize bias. In survey courses, they create comparative timelines, explore historical scenarios, and generate discussion prompts from complex readings. Even in our applied AI minor, AI is foundational from day one.

Custom AI workflows enable students to analyze sources, build timelines, and simulate historical debates in Dr. Voss’s courses.

Q: Many people don’t associate AI with the humanities. How do you see AI’s role in fields like history?

Voss: That’s a common misconception. People think AI is just for coding or data science, but digital history has always been about connecting traditional research with technology. AI supercharges that connection.

As historians, we’re already comparing perspectives, analyzing patterns, and building interpretations. AI just accelerates those steps. But it still requires human expertise to decide what’s accurate and meaningful. AI lets us do what we could already do, only faster and at a scale that was previously impossible for a small university like ours.

Q: What’s been the impact on your students and your institution, especially given your rural, resource-limited context?

Voss: The impact has been huge. We don’t have graduate assistants or fancy labs, but AI allows our undergraduates to do work that used to require a research team at a major institution. They’re running AI-assisted searches across digitized archives, translating sources, generating maps and timelines, and simulating debates.

Engagement is up because the work feels real and relevant. Class discussions are deeper, and everyone comes prepared with meaningful contributions.

And it’s not just students. Faculty across disciplines are adopting AI, from biology to art. I’ve led workshops, collaborated on state standards, and helped train more than 500 educators in AI across K-12 and higher education. Professional development is essential for educators, especially in a world where AI is changing so quickly.

AI has also helped me work on major institutional priorities. I used ChatGPT in a custom GPT to help negotiate a new compensation package, the first one our university had seen in over 20 years. It was absolutely successful for the faculty.

I also used a custom GPT in November and December of last year to create an application for a federal grant requesting $2.61 million for Rural AI and K-12 education. What would normally have taken multiple months and a large team took five days to author, coordinate, and submit, including the signatories. While the FISPE grant system has since been shelved, ChatGPT opened many new opportunities.

For us, AI isn’t just an upgrade. It’s a game-changer.

AI-powered projects have enabled students at Northwest Missouri State University to create interactive digital exhibits, conduct advanced research, and support larger institutional initiatives.

Q: Looking ahead, what’s your vision for AI literacy at your university and beyond?

Voss: My goal is to make AI literacy as fundamental as internet literacy. I want every graduate, in every major, to use AI effectively and responsibly. And I want faculty to feel confident integrating it into their teaching, research, professional development, and administrative work.

Because of my work using AI in education, I’ve been approached to help others with AI adoption. That need is only growing. AI has made my classes more engaging, my students more capable, and my own thinking sharper. If we can achieve this kind of transformation in a small town in Missouri, it can happen anywhere.

What Stands Out

Core idea: Dr. Voss’s shift from technology-first to AI-first teaching is rooted in the belief that AI can empower students, faculty, and institutions to achieve more with less.

Classroom design: By treating AI as a collaborator, building custom GPTs, designing AI-powered assignments, and integrating critical evaluation, Voss creates classrooms where students engage deeply with history.

Institutional impact: Voss has used custom GPTs beyond the classroom, including faculty compensation work and a $2.61 million Rural AI and K-12 education grant application.

Educator training: His AI adoption work now extends across K-12 and higher education, with more than 500 educators trained in AI.

Student impact: Students gain hands-on experience with advanced research methods, from AI-assisted primary source analysis to creating interactive exhibits, leading to higher engagement and more meaningful discussions.

Transferable lesson: The success at Northwest Missouri State University demonstrates that AI can democratize access to top-tier educational experiences and institutional capacity, no matter an institution’s size or budget.

Bio

Dr. Robert Voss is an Associate Professor of History and Social Science Education Coordinator at Northwest Missouri State University, where he integrates artificial intelligence into teaching, research, grant writing, faculty development, and educator training. A former high school social studies teacher, he has been an early adopter of educational technology for over two decades, from building websites with second graders in 1998 to leading AI adoption work across K-12 and higher education. He has helped train more than 500 educators in AI and has used custom GPTs to support faculty compensation work and major grant development. His work focuses on making AI literacy accessible across disciplines, empowering students, faculty, and institutions to achieve big results with limited resources.

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