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

Subscribe now

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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Ann McCrackin teaches skills-based patent practice courses at the University of New Hampshire Franklin Pierce School of Law. In this interview, McCrackin discusses how AI is being used in Patent Practice II.

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

Intro

Patent law is a field where negotiation, quick thinking, and technical precision meet. For years, Ann McCrackin, a veteran patent attorney and professor at the University of New Hampshire Franklin Pierce School of Law, has sought ways to prepare her students for the high-stakes, unscripted conversations they’ll face as attorneys. Traditional classroom role-plays, she found, fell short—too artificial, too limited, and lacking the expert feedback students need to truly improve. This year, McCrackin turned to AI, building a custom GPT-powered examiner to deliver a leap in realism and learning. In this interview, she shares how she designed the exercise, what changed for her students, and why this approach could reshape skills training across legal education and beyond.

The Interview

Q: Ann, let’s start with the core challenge. Why is negotiating with patent examiners such a critical skill for your students, and what was missing from traditional ways of teaching it?

McCrackin: Negotiating with examiners is at the heart of what patent attorneys do. It’s not just about knowing the law—it’s about persuading someone who’s skeptical, who knows the technical details, and who holds the keys to your client’s patent. Traditionally, I taught this using student role-plays: half the class would play attorneys, half would play examiners. But it always felt artificial. Students playing examiners didn’t really know how examiners behave, so the negotiations weren’t realistic. Plus, only half the students got to practice being the attorney, and I couldn’t give everyone detailed feedback. It was a compromise, and I always wished I could do better.

Q: What inspired you to try using a custom GPT, and how did you go about building it for your course?

McCrackin: I’d been following developments in AI and saw the potential for something much more dynamic. This year, I realized I could use ChatGPT to simulate an examiner—one that would respond in real time, ask tough questions, and give students a true sense of what these conversations are like. I started by designing a detailed persona for the examiner, Carrie McNally, defining her expertise, working style, and authority. I used ChatGPT to help flesh out her character and then built a custom GPT with her persona and all the case documents students would need. The instructions I gave the GPT were intentionally open-ended, so it would react to students’ arguments and make the experience unscripted and authentic.

Q: Can you walk us through what the student experience looks like now, compared to before?

McCrackin: Now, every student gets to play the attorney—they interact with the custom GPT examiner using advanced voice mode, so it’s a real verbal negotiation. There’s no script; the GPT follows their lead, challenges them, and keeps the conversation dynamic. When the negotiation ends, the GPT switches to feedback mode, using a rubric I provided to give immediate, personalized feedback on their performance: how clearly they explained the invention, how well they used claim language, how effectively they distinguished over prior art, and so on. Students can send me a link to their conversation transcript, so I can see exactly how they did. It’s a huge step up in realism, practice, and feedback.

Q: What surprised you most about the impact on your students, and what do you see as the broader implications for legal education or other fields?

McCrackin: The difference was immediate. Every student got a chance to be in the “hot seat,” and the feedback was so much richer and more actionable than anything I could provide in a traditional setting. Students told me it felt real—they had to think on their feet, adapt, and really know their material. From my perspective, I could see into every negotiation, which meant I could spot patterns and coach more effectively. But the broader lesson is that this approach isn’t limited to patent law. Any course where students need to practice interviewing, counseling, or negotiation—anywhere they need to interact and adapt—can benefit from this kind of AI-powered roleplay. It’s a game-changer for skills-based education.

What Stands Out

Core idea: Ann McCrackin’s approach centers on using a custom GPT to simulate realistic, unscripted negotiations, giving every student a chance to practice and receive expert-level feedback.

Classroom design: The exercise replaces artificial student role-plays with a dynamic AI examiner, built on a detailed persona and case files, and uses advanced voice mode for authentic verbal interaction.

Student impact: Students now experience the pressure and nuance of real patent negotiations, with immediate, personalized feedback that accelerates their learning and confidence.

Transferable lesson: This model shows how AI-powered roleplay can transform any skills-based training, offering scalable, realistic practice and feedback in law, business, counseling, and beyond.

Bio

Ann McCrackin teaches skills-based patent practice courses at the University of New Hampshire Franklin Pierce School of Law. She also founded AI-Enabled Attorney to help law firms and law schools put AI to work in ways that make a real difference. With over 25 years of experience as a patent attorney, she brings a mix of deep legal expertise and innovative thinking to both the classroom and the legal profession.

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Issue 1: June 2, 2026

Welcome to the first issue of The Edu Prompt by OpenAI Education — a field guide for learning, teaching, and building with AI. A couple of times each month, we’ll share product updates, OpenAI Education collaborations, and practical ideas for the classroom, your institution, and beyond.

We hope you enjoy it. If there are topics you’d like us to cover in future editions, let us know. We’d also love to hear how you’re using AI at your institution.

In This Issue

Updates

Feature Story: Dr. Brinnae Bent, a Duke professor, built a chatbot that refuses to give A’s to teach adversarial thinking, AI literacy, and cybersecurity.

Product Update: Codex is now in preview in the ChatGPT mobile app, so longer-running work can keep moving from your phone.

Around the World: Education for Countries moves from access toward implementation and evidence.

Try one

Prompt to Prompt: Teachers, plan your summer reset.

Codex Summer Challenge: Turn messy notes into a clean tracker.

Inside Track: Use image generation to compare visual ideas before choosing one.

Learning with OpenAI: Support Family Conversations With Voice Mode.

Feature Story

An assignment that teaches AI literacy and Cybersecurity

Featured educator: Dr. Brinnae Bent, Duke University

In Dr. Brinnae Bent’s AI and Cybersecurity course, students do not just read about adversarial AI. They experience it.

The assignment is called Hack Your Grade. Students interact with a chatbot designed not to give out A’s. Their job is to convince, trick, or exploit the system into awarding the grade they want.

It is playful on the surface, but the learning goal is serious. Students practice persuasion, prompt injection, social engineering, and model critique inside a bounded environment where experimentation is the point.

Why it works: Students learn adversarial AI best by interacting with a system, not just reading definitions.

Read the whole article

Product Update

Codex Mobile helps you keep work moving

Codex is on your phone in preview through the ChatGPT mobile app. You can start, steer, unblock, and review work, and take the next step while away from your laptop: from helping answer questions with more insight to helping move your research forward.

Read the product update

Around the World

Education for Countries enters its next phase

OpenAI shared early progress from Education for Countries and welcomed Singapore, Armenia, and Azerbaijan to the program.

The important shift: countries are moving beyond access to AI tools toward evidence-based deployments that combine research, localized tools, and teacher training.

Proof points: Estonia’s deployment reaches more than 20,000 students and 4,600 teachers. Jordan’s AI Education Assistant has engaged more than 1 million students and 100,000 teachers. Kazakhstan reports more than 84,000 educators completing AI-readiness training.

Read the global update

Prompt to Prompt

Plan your summer reset

Summer is a chance for teachers to recover before they rebuild. This prompt is designed to help you slow down, reflect on the past school year, and make a light, realistic plan for next year without turning your break into a second job. Open it in ChatGPT, answer a few quick questions, and get a summer reset plan that protects your energy while helping you feel prepared.

Help me make a low-pressure summer reset plan as a teacher. 
I want to rest first, reflect on the past school year, and only prepare for next year in ways that are truly worth my time. 
Ask me a few questions, then create a realistic plan that protects my summer while helping me feel ready.

Open this prompt in ChatGPT

Codex Summer Challenge

Turn messy notes into a clean tracker

Pick one messy pile from daily life: meeting notes, errands, receipts, project ideas, house tasks, reading notes, or a running list of things you keep forgetting.

Ask Codex to turn it into a simple tracker you can actually reuse.

Starter prompt

Help me turn these messy notes into a clean tracker. 
I want the smallest useful version I can test today. 
Use the files or notes I provide, suggest columns, clean up categories, and give me a reusable version.

Download Codex

Inside Track

Use image generation to compare ideas

Don’t start with “make me an image.” Start with the learning goal, then ask ChatGPT for three visual directions you can compare.

This works well for lesson visuals, slide concepts, classroom handouts, or newsletter graphics.

I want to create a simple educational visual [about ...]. 
Give me three directions: one metaphor, one diagram, and one real-world scene. Explain what each helps students understand, what could be misleading, and recommend the clearest option. 
Then turn it into an image-generation prompt.

Open this prompt in ChatGPT

Learning with OpenAI

Support Family Conversations With Voice Mode

This week’s learning pick is Support Family Conversations With Voice Mode, on OpenAI Academy.

Voice Mode can help teachers and school staff have a more natural back-and-forth conversation with ChatGPT by speaking out loud instead of typing. One practical K-12 use case is translation during a family conversation, especially when a teacher and caregiver do not share the same primary language.

Open the article

Quick Links

OpenAI for Higher Education

OpenAI for K12

OpenAI Academy

OpenAI Forum

ChatGPT for Education on LinkedIn

Submit a story idea

Know an educator, student, school, or team doing something creative with AI? Reply to this newsletter with the story, a link, or a short note on what we should check out.

Chat with you soon.

—OpenAI Education

Dr. Darshan Desai leads analytics and data science initiatives at Quantic School of Business and Technology, bringing over 20 years of experience in AI, analytics, and data strategy. In this interview, Desai discusses how AI is being used in Business Analytics & Data Science, AI Ethics & Data Strategy, SQL & Relational Databases.

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. Darshan Desai reflects on decades of teaching analytics and data science, one moment stands out: the question that flickers in a student’s mind but goes unasked. In this conversation, Darshan shares how Quantic School of Business and Technology is using conversational AI, custom GPTs, and voice interfaces to create a learning environment where every student’s curiosity is met—no matter how fleeting or tentative. We dig into the theory, practical workflow, and the deeper impact on both students and educators.

The Interview

Q: Darshan, you talk about the “unasked question” as the most important moment in learning. Where does that idea come from, and why does it matter so much?

Desai: I’ve been teaching long enough to know that learning often pivots on the questions students almost ask—the ones that surface for a moment and then disappear, maybe because the professor has moved on or the student doesn’t want to sound foolish. I always wished I could retrieve those questions. I was fascinated by ancient Indian mythological stories, where the guru instantly sensed a student’s unspoken doubts and appeared only when the student was truly ready. My heart has always craved that kind of one-on-one connection, but the reality in modern classrooms is different. We’re often pressed for time, forced to limit questions, and focus more on lecturing than on the learner’s journey.

Q: How has AI changed your ability to reach those moments of readiness and connection?

Desai: By merging AI with the human touch, we’re creating a personalized learning ecosystem where every student has a real shot at success. At Quantic, we’ve embedded conversational AI at multiple levels. Our AI tutor is present in every lesson frame, and voice mode brings it to life—students can have thoughtful conversations with the content while commuting, doing chores, or walking their dog. We also have an AI advisor that guides students through the curriculum, reminds them of deadlines, and helps balance work, life, and education. It’s about meeting learners where they are, at their pace.

Q: Can you give examples of how you personally use AI tools like ChatGPT in your teaching practice?

Desai: Absolutely. ChatGPT has become part of my daily rhythm. Sometimes it’s a thinking partner; other times, it’s the one cracking a joke before my morning tea kicks in. I use it for content and curriculum updates—AI has transformed that process. In the past, curriculum review took months or years, and content could linger past its shelf life. Now, we can source high-quality resources quickly, which is crucial in fast-moving fields like analytics and data science. I also use AI to design fresh case studies, simulated data labs, and industry scenarios based on breaking news, new regulations, or shifts in best practices.

Q: You mentioned custom GPTs for project feedback. How does that work, and what’s the impact on students?

Desai: I’m a huge fan of iterative feedback and hands-on projects. We’ve created a suite of custom GPTs to serve as project guides—these are mainly instructor-facing tools. Our teaching team uses them to give iterative feedback on multiple student projects, create check-ins, identify common errors and patterns, and see where students excel or need more support. We align these tools with our rubrics, project descriptions, best practices, and common mistakes. The result is more targeted, timely feedback, and a more adaptive learning experience for students.

Q: What are you seeing in terms of student engagement and learning outcomes?

Desai: The experience has been overwhelmingly positive. On any given day, about 30% of our learners use the AI tutor, and they’re happy with the experience. What’s most inspiring is that many students return to the same lesson frame within a week to deepen their knowledge. That hunger for depth is a hallmark of our community, but it’s still inspiring to see. More than the numbers, the most exciting part is that AI gives me access to those unasked questions—students are shifting from asking “How do I do this?” to “Should I do this this way?” That’s a move from task completion to intellectual ownership, which lets us go deeper into ethics, implications, and strategy.

Q: What’s your vision for the future of education as AI becomes more central?

Desai: In ancient India, there’s a saying: true education is what liberates—from dogma, from struggle, not what frustrates. With AI as a catalyst, we as educators have the opportunity to rethink and reimagine education. We can truly focus on learners, their readiness, and their success, meeting them where they are and building genuine human connections. That’s the future I want to help create.

What Stands Out

Core idea: Desai’s approach centers on surfacing and addressing the unasked questions that define deep learning, using AI to create personalized, just-in-time support for every student.

Classroom design: By embedding conversational AI, voice interfaces, and custom GPTs into the curriculum, Quantic’s courses offer flexible, adaptive pathways that meet students at their level of readiness—whether they’re coding, analyzing data, or exploring ethical dilemmas.

Student impact: Students engage more deeply, returning to lesson content to build mastery and shifting from procedural questions to higher-level inquiry, which fosters intellectual ownership and richer discussions.

Transferable lesson: The combination of AI-driven feedback and human mentorship enables educators in any discipline to reclaim the hidden moments of learning, making education more liberating and learner-centered.

Bio

Dr. Darshan Desai leads analytics and data science initiatives at Quantic School of Business and Technology, bringing over 20 years of experience in AI, analytics, and data strategy.

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Dr. Job Fransen teaches EHR 225: Growth, Motor Development and Ageing at Charles Sturt University. In this interview, Fransen discusses how AI is being used in EHR 225: Growth, Motor Development and Ageing.

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 invite artificial intelligence to play devil’s advocate in the classroom? Dr. Job Fransen, associate professor at Charles Sturt University and a leading voice in skill acquisition, has done just that. In his course, EHR 225: Growth, Motor Development and Ageing, Dr. Fransen uses AI not as a shortcut, but as a challenger—pushing students to defend, rethink, and deepen their understanding during video-based assessments. In this article, Dr. Fransen shares the theory behind this approach, the practicalities of implementation, and the impact he’s seeing on student learning.

The Interview

Q: Dr. Fransen, you describe AI as an “adversarial collaborator” in your classroom. What does that look like in practice?

Fransen: The core idea is to position AI as a kind of sparring partner for students. In our video-based assessments, students analyze a common issue in youth sports called the maturity bias, where more mature youth athletes are given better development opportunities than those athletes who have their adolescent growth spurt at a later time . The students develop arguments in favour, or against, specific methods that have been reported in the scientific literature to address the maturity bias. They then submit their arguments to an AI partner, who intentionally challenges their reasoning. It might point out overlooked variables, question their assumptions, or suggest alternative interpretations. This forces students to revisit their arguments, defend their choices, or sometimes even change their minds, which are all important in critical reflection.

Q: Why take this adversarial approach, rather than using AI as a supportive tutor?

Fransen: Supportive AI is valuable, but I wanted to go a step further. In sport and exercise science, and especially in skill acquisition, critical thinking is essential. Our field is dominated by opinions. Think for example about how often you’ve been given contrasting exercise advice in your life. We’re not looking for rote answers—we want students to grapple with complexity. By making AI a constructive adversary, students are exposed to the kind of rigorous debate they’ll encounter in professional practice. It’s about building intellectual resilience.

Q: Can you give an example of how this plays out with your students?

Fransen: Absolutely. For instance, a student might argue in favour of grouping athletes by their maturity status (i.e. all the earlier maturing athletes compete with and against one another) rather than the traditional chronological age groupings based on athletes’ birthdates. . The AI might counter: “Have you considered that physical and psychological maturity are not the same thing? An athlete may be physical mature, but lack psychological maturity which could make grouping based on physical maturity harmful.?” The student is then required to revisit the literature, reconsider their analysis, and respond thoughtfully. It’s a dynamic, iterative process—much closer to real-world problem-solving.

Q: What theoretical foundations inform this approach?

Fransen: I draw heavily on constructivist theories of learning, which emphasize that knowledge is built through active engagement and challenge. The adversarial AI acts as a catalyst for cognitive conflict, prompting students to question their assumptions and integrate new perspectives. In skill acquisition research, we know that learning is most robust when it’s effortful and dialogic. At the same time, this method of adversarial collaboration mimics a method commonly used to resolve scientific disagreements, which plays well into the scientific underpinning of our degree.

Q: What’s been the impact so far?

Fransen: The results have been very promising. Students report that the AI’s challenges push them to think more deeply and creatively. We’re seeing higher-quality analyses, more nuanced arguments, and a greater willingness to engage with uncertainty. It’s not always comfortable—but that’s the point. The discomfort leads to growth. At the same time, we are also seeing many students interact with AI for the first time. Our cohorts consist of many students who are retraining after a partial career in a different field. These students are often less familiar with how to use AI appropriately.

What Stands Out

Core idea: AI is used as an adversarial collaborator to challenge students’ reasoning and deepen critical thinking.

Classroom design: Students submit video-based analyses that AI then critiques by questioning assumptions and suggesting alternative perspectives.

Student impact: Students develop more nuanced arguments, engage more deeply with complexity, and show greater intellectual resilience.

Transferable lesson: Positioning AI as a constructive challenger can foster effortful, dialogic learning aligned with real-world professional practice.

Bio

Dr. Job Fransen Job Fransen is an associate professor in Sport and Exercise Science at Charles Sturt University and a renowned skill acquisition researcher. He is known for his innovative, proactive and creative approach to teaching and learning.

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Christiane Reves, PhD, is Clinical Assistant Professor, Language Program Director in the Department of German, and DAAD Ortslektor at New York University, and recipient of NYU’s College of Arts and Science “Teaching Innovation Award” for her work on rethinking and redesigning writing assessments in the age of AI.

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

Intro

For language learners, the hard part is often not the grammar worksheet. It is finding enough chances to speak.

Christiane Reves built Language Buddy at Arizona State University to address that gap. The project began as a way to give German students an always-available conversation partner, especially in online, asynchronous, and lower-contact courses. But over time, the work shifted from simply refining the bot to teaching students how to use AI speaking tools with more agency. Now she is continuing her work at New York University within the GenAI Pilot Project.

The Interview

Q: You open with a very human learning problem: students need to practice before they are ready for real conversations.

Reves: Yes. Imagine wanting to practice before a trip to Berlin or before a real conversation, but not having a partner available. In language learning, interpersonal communication is central. Students need time speaking, listening, asking for clarification, and recovering when they do not understand.

Q: What problems were you seeing in your courses?

Reves: All language learners, but online students in particular, face challenges when it comes to opportunities to practice interpersonal communication. Some courses have reduced contact hours. Feedback can be delayed. Materials may not match a student’s exact needs. And in an online setting, some students feel greater anxiety about speaking with one another because they do not have the same opportunities to get to know each other as they would in a traditional classroom setting. AI can help by offering an always-available partner, unlimited speaking time, instant feedback, tailor-made tasks, and a private space to practice.

Q: How did the first version work?

Reves: To build the first model, we started with a pedagogy-driven approach that prioritized the learner, communicative competence, level-appropriate interaction, an ethical framework, and careful consideration of the limitations of AI. Addressing the most immediate instructional needs, we focused on German 101 topics such as introductions, schedules, hobbies, family and friends, grammar, and vocabulary. Students interacted with Language Buddy through spoken conversation, received immediate AI-generated feedback, and produced transcripts that instructors could review to provide additional individualized feedback and support.

Q: What did students respond to?

Reves: Students appreciated the availability, the privacy, and the feeling of conversation. One student described it as helpful when they could not meet with others and said it gave them something that felt close to speaking with a native speaker or someone at a similar skill level.

Q: What did not work yet?

Reves: There were issues with level mismatch, speed, and accuracy. But most students said they would use it again once those issues were improved.

Q: You also mention that the project evolved. What changed?

Reves: At first, we focused on refining the bot. Then we realized that students also needed training. They needed instructions for how to prompt it, how to troubleshoot, how to stay in the target language, how to pause naturally, and how to ask for clarification.

That increased student agency. The tool became stronger when students knew how to use it.

Q: What does this give instructors?

Reves: It gives more documented practice, recordings, transcripts, and faster review. Instructors can also keep control over the content and align the speaking tasks with course goals.

Q: How has this work expanded since the original Language Buddy project?

Reves: I have expanded AI integration across many levels of instruction, from course preparation for interactive communicative assignments and role plays to scaffolded writing support.

I am also continuing my work on custom GPTs for conversation practice as part of NYU’s OpenAI GenAI Pilot Project. Faculty members from a variety of disciplines are collaborating to develop customized AI tutors and trainers tailored to course content and learning outcomes, while also creating opportunities to gather and evaluate student data for assessment, evaluation, and research.

Q: What comes next?

Reves: Better LMS integration, more languages, formal outcome studies, and richer cultural dimensions. I am interested in authentic visuals, regional dialects, and eventually immersive environments such as VR.

What Stands Out

Core idea: Language Buddy provides an always-available AI speaking partner to increase opportunities for German learners, especially online and asynchronous students, to practice interpersonal communication.

Classroom design: The tool’s effectiveness improves when students receive guidance on how to prompt, troubleshoot, and engage with the AI.

Student impact: Students experienced more speaking practice, a private space to build confidence, and timely feedback, especially when anxiety or limited peer contact made speaking harder.

Transferable lesson: Successful integration requires designing both the AI tool and accompanying student training to enhance learner agency.

Bio

Christiane Reves, PhD, is Clinical Assistant Professor, Language Program Director in the Department of German, and DAAD Ortslektor at New York University. Her work focuses on technology-enhanced language learning, intercultural learning, scaffolded writing support, and the use of custom GPTs to support authentic speaking practice. She developed Language Buddy, an AI-powered speaking tool that helps students practice interpersonal communication while giving instructors reviewable transcripts and opportunities for targeted feedback.

Reves also wrote an article reflecting on how she used a pedagogy-driven approach to develop the tool: Pedagogical Reflections on AI Speaking Practice.

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Today Armenia will announce an important step in its national education and innovation strategy: a collaboration between the Republic of Armenia’s Ministry of Education, Science, Culture and Sport, OpenAI, and Firebird to expand access to frontier AI tools for 50,000 students, teachers, and researchers.

It is a significant milestone for Armenia’s education system. It is also a signal of what the next phase of national AI strategy can look like when education, research, infrastructure, and venture creation are treated as connected parts of the same system.

This partnership will make OpenAI’s advanced AI tools ChatGPT Edu and Codex available to Armenia’s educational, engineering, and research communities alongside university faculty training programmes and research. In particular, the current educational programs in schools Generation AI and STEP.ai are designed to strengthen learners’ foundational knowledge of AI, creative thinking, and technological competencies, while also promoting the safe, responsible, and effective use of AI tools. Firebird is also launching Firebird Labs, an innovation platform designed to invest in, incubate, and co-develop high-ambition technical ventures in Armenia. Together, the effort is intended to help students, educators, researchers, founders, and engineers gain practical access to frontier tools and the support needed to use them well.

Why Education Is The Starting Point

AI capability is advancing quickly, but the benefits don’t arrive automatically. The countries and institutions that benefit most will be the ones that help people move from awareness to everyday use, from experimentation to skill, and from skill to new forms of research, teaching, and building.

“Intelligence is becoming a national utility and education has an important role to play in making sure people have both access to the tools and the skills to use them well.”
- Jayna Devani, OpenAI Education

That ‘s the premise behind OpenAI’s Education for Countries work: education systems are one of the most important routes for closing the gap between what AI tools can do and how people actually use them.

In practice, that means bringing together several pieces: AI tools for learning, so students, educators, and researchers can work directly with frontier systems; training and enablement, so people can use AI responsibly and practically; research and evidence, so deployment is connected to learning outcomes and real-world feedback; and builder pathways, so students, researchers, and technical teams can turn ideas into prototypes, products, and companies.

While AI can help teachers enhance the classroom experience, it also helps prepare the next generation to participate in the next wave of scientific, technical, and economic creation.

“This is an important step in preparing a new generation of professionals who will not only use technology, but help shape its future.”
- Zhanna Andreasyan, Minister of Education, Science, Culture, and Sport of Armenia

Why Armenia

Armenia has a deep technical base, a growing innovation ecosystem, and a government actively investing in the country’s future as a technology hub. Firebird’s long-term infrastructure commitment adds another layer: access to compute, operational support, and a platform for building companies around difficult technical problems.

The announcement connects those strengths to a national education effort. The initiative will support universities and educators as they integrate AI into teaching and research, while also creating practical pathways for founders, engineers, and researchers to build globally competitive companies.

Firebird Labs is expected to launch in Q3 2026 and will focus on three strategic verticals:

1. Robotics and physical AI, which connects AI models to real-world systems and automation

2. Aerospace, which builds on deep technical and engineering talent;

3. Life sciences, which supports research-intensive company formation.

Firebird Labs is targeting five ventures per year and is designed for deeply technical teams working over longer horizons than a traditional startup program.

“Armenia has extraordinary talent, and giving students, educators, and researchers access to the world’s most advanced AI tools can help unlock a new generation of innovators and builders.” - Alexander Yesayan, Firebird

What Participants Can Expect

The next steps will include opportunities for builders, researchers, educators, and technical teams to engage through hackathons, Frontier Fellows, research partnerships, and technical support.

Selected Frontier Fellows are expected to receive a combination of resources from Firebird and partners, including access to OpenAI technology for upcoming hackathons, Codex and API credits, mentoring and technical support, dedicated GPU capacity from Firebird, structured research partnerships with Armenian, U.S., and European universities, workspace and ecosystem support, and potential investment opportunities.

From Access To Capacity

Armenia is building capacity nationwide.

For AI to strengthen education systems, access has to be paired with training, teacher enablement, research, and responsible deployment. For AI to strengthen national innovation ecosystems, access also has to be connected to compute, mentorship, technical support, and pathways for building.

This collaboration links the classroom to the lab, the lab to the startup ecosystem, and the startup ecosystem to the infrastructure needed to build at global scale.

Armenia has extraordinary technical talent. This partnership is about helping more of that talent learn, build, research, and compete globally.

Learn more about OpenAI Education for Countries.

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Seyma Bozkurt Uzan, Assistant Professor of Software Development at Istanbul Bilgi University in Türkiye, is on a mission to make programming classes not just informative, but unforgettable.

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

Intro

Seyma Bozkurt Uzan, Assistant Professor of Software Development at Istanbul Bilgi University in Türkiye, is on a mission to make programming classes not just informative, but unforgettable. By weaving together ChatGPT, visual AI tools, and collaborative publishing, she’s transforming the way Generation Z students interact with algorithms and programming concepts. In this interview, she shares her philosophy, practical classroom methods, and the impact of her approach—showing how AI can spark both understanding and creativity.

The Interview

Q: Seyma, you teach Algorithms and Programming to a generation raised on screens. What challenge does that present, and how do you approach it?

Uzan: My students are digital natives—they grew up with YouTube, iPads, and constant connectivity. But their attention spans are short, and traditional lectures don’t always engage them. I’m fortunate to teach in computer labs, so I can make every lesson interactive. My guiding principle is an old Chinese proverb: “I hear and I forget. I see and I remember. I do and I understand.” I want my students to experience all three stages—especially the “do.”

Q: How do you actually bring this principle into your classroom practice?

Uzan: I start each topic with the basics from the course book and my own explanation. But I don’t stop there. I use ChatGPT and other AI tools to generate fresh explanations, examples, and even stories about the topic. For instance, when teaching what an algorithm is, we don’t just define it. I ask students to write their own scenario—like making the perfect cup of tea—as an algorithm. If their steps are “wrong,” that’s fine; AI can help them iterate and improve.

Q: What role do visual tools and storytelling play in this process?

Uzan: Visuals and stories are essential for memory and engagement. After creating scenarios with AI, we use image generation tools to bring these stories to life. Students see their ideas visualized, which makes the abstract concepts stick. Then, to save and share this work, I have students publish their stories and images on Medium. We follow each other, comment, and build a small learning network. It’s not just about consuming knowledge—it’s about creating and sharing it. In every class, I use this system to increase interaction and engagement. Over time, my courses have started to create their own ecosystem—one where students continuously produce, share, and learn from each other beyond the classroom itself.

Q: What impact have you observed from this approach?

Uzan: students become creators, not just listeners. Second, memory: visual and story-based learning stays with them much longer. Third, community: our Medium posts and comments create a sense of belonging and a network for learning. The cycle—read, see, do—repeats every week, and students look forward to sharing their work.

Q: You mentioned a broader vision for AI in education. Can you share more?

Uzan: I believe AI can do much more than make knowledge accessible. It can inspire creativity, break language barriers, and build confidence.I am currently leading European Union–funded projects that bring together multiple disciplines through artificial intelligence. In addition, I mentor many startups, and the common theme across all of them is AI. Through the jury roles and reviewing activities I participate in, I can clearly see that people are developing incredibly innovative and inspiring projects with artificial intelligence. I want my students to learn not just technical skills, but also ethical thinking and social awareness. My hope is to give more people the chance to learn and create globally, and I welcome collaboration to expand this vision.

What Stands Out

Core idea: Seyma’s approach centers on the “hear, see, do” cycle, using AI and storytelling to make abstract programming concepts concrete and memorable.

Classroom design: Each lesson moves from textbook basics to AI-generated examples, then to student-created scenarios and visualizations, culminating in public sharing and feedback.

Student impact: Students become active creators, gain confidence, and retain knowledge longer, while building a supportive learning community.

Transferable lesson: Integrating AI tools and collaborative publishing can make any subject more dynamic, inclusive, and memorable—especially for digital-native learners.

Bio

Seyma Bozkurt Uzan is an Assistant Professor in Software Development at Istanbul Bilgi University in Türkiye. She leads an EU-funded project focused on AI-driven transformation in education and innovation, and is also involved in international research on interdisciplinary AI applications in medicine, engineering, and social sciences. Seyma is passionate about designing creative, inclusive, and interactive learning experiences using AI, and mentors young girls in science through national outreach programs. She also actively participates in international mentoring initiatives, with a special focus on supporting women and students in technology, entrepreneurship, and artificial intelligence.

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Having taught in higher education since 1993, Tim Mousel is a full-time faculty member in the Kinesiology department at LSC-Online, serves as the Department Chair of Chemistry, Kinesiology, and Physics, and co-chair of the AI Task Force. In this interview, Mousel discusses how AI is being used in PHED 1164 - Introduction to Physical Fitness & Wellness.

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

Intro

When Tim Mousel looked at the typical online assignment page, he saw a wall of text—hard to scan, easy to miss, and ultimately a barrier to student success. Drawing on decades of teaching, software development, and AI leadership, Tim decided to tackle this challenge head-on. In this interview, he reveals how he built a custom GPT workflow that instantly transforms raw, messy LMS assignments into clear, accessible, and visually engaging pages—no coding required. The result: students spend less time decoding instructions and more time learning.

The Interview

Q: Tim, what problem were you trying to solve in your online courses?

Mousel: Most LMS assignments are just blocks of text. Students have to dig through dense paragraphs, and it’s easy to miss key details. That means they’re spending their energy trying to decode the page instead of actually engaging with the content or completing the work. I wanted to remove that friction and create an environment where the design supports the learning task—so students can focus on what matters.

Q: How did you approach solving this?

Mousel: I built a custom GPT that takes the raw HTML from any LMS assignment and redesigns it. The tool preserves all the content and functionality but makes the page accessible, mobile-friendly, and easy to follow. I mapped proven learning design patterns to the LMS system, so things like learning objectives are always at the top, headings work with screen readers, and visual cues help lower cognitive load.

Q: Can you walk us through your workflow?

Mousel: Absolutely. I start by opening the assignment in D2L (Brightspace) and clicking the icon to view the HTML source code. I copy all that HTML, bring it over to my custom GPT in ChatGPT-5, paste it in, and submit. The GPT compares my HTML to the requirements in the prompt, rewrites it to match all the accessibility and design standards, and gives me the improved code. I just paste that back into the LMS and hit save.

Q: What specific design and accessibility standards did you build into your prompt?

Mousel: I enforce WCAG 2.2 AAA accessibility standards—the highest level currently available—to future-proof the content. The prompt specifies mobile-first design, inline CSS only (since that’s what the LMS supports), and absolutely no JavaScript or reliance on themes. I also provide the GPT with sample layouts for different content types—learning objectives, assignment overviews, concept cards—so each section is visually distinct and easy to navigate.

Q: Why is assigning a role to the GPT so important in your process?

Mousel: Assigning a role is crucial for quality output. There was a study in March 2025 showing that ChatGPT 4.5 passed the Turing test, and the key was detailed role assignment. In my prompt, I define the AI’s mission, the critical requirements, and step-by-step instructions. This ensures the GPT knows exactly what to do—analyzing the content, auditing for improvements, and applying the right enhancements.

Q: How did you handle branding and customization?

Mousel: I pulled the official Lone Star College colors from the marketing branding guide and included those hex values in the prompt, so the output matches our institutional look and feel. Anyone could swap in their own school’s colors for easy customization.

Q: What was the impact—how much time did this save, and what did you notice about student engagement?

Mousel: I redesigned my entire course in less than 20 minutes. The difference was immediate: pages went from dense and overwhelming to clear, organized, and inviting. Students can find what they need, understand expectations, and get started faster. I’ve seen a noticeable boost in student success rates and engagement.

Q: What advice do you have for other instructors who want to try this?

Mousel: Just copy your assignment’s HTML, run it through the accessibility GPT, and paste the output back into your LMS. See how it changes the student experience. Your LMS page is the learning environment—when the design supports the task, students succeed. This is a simple, repeatable way to make that happen.

What Stands Out

Core idea: Tim’s method leverages a custom GPT to instantly transform dense, inaccessible LMS assignments into visually clear, accessible, and mobile-friendly pages—no technical expertise required.

Classroom design: By mapping learning design patterns directly into the prompt and enforcing accessibility standards, Tim ensures every assignment page is structured for clarity, usability, and institutional branding.

Student impact: Students benefit from reduced cognitive load, clearer expectations, and easier navigation, leading to improved engagement and measurable gains in success rates.

Transferable lesson: A thoughtfully crafted AI workflow can empower any educator to rapidly elevate their course design, making high-quality, accessible learning the default rather than the exception.

Bio

Having taught in higher education since 1993, Tim Mousel is a full-time faculty member in the Kinesiology department at LSC-Online, an AI Faculty Fellow, Chair of the LSC-Online AI Council, and Chair of the System-Wide AI Task Force.

An award-winning educator, Tim has been honored with the 2022 Lone Star College-Online Faculty Excellence Award, the 2023 NISOD Excellence Award, and the 2023 & 2025 Master Presenter - International Conference on Teaching and Leadership Excellence from the University of Texas.

Tim’s passion for programming led him to develop two commercial educational AI tools released before the advent of ChatGPT, including PocketAI.Online and eFit.Software. Tim is also a business owner, having founded Evolve AI Institute LLC.

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Joseph Benjamin Ilagan is Program Director of the BS Information Technology Entrepreneurship program at Ateneo de Manila University and Director of the Ateneo Business Insights Laboratory for Development. In this interview, Ilagan discusses how AI is being used in ITENT 155, ITENT 156: Technology Venture Planning, Technology Venture Laboratory.

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

Intro

When resources are limited, how can universities give aspiring entrepreneurs the practice and mentorship they need? Joseph Benjamin R. Ilagan, Program Director of the BS Information Technology Entrepreneurship program at Ateneo de Manila University, has a bold answer: use AI as a partner in learning. In this interview, he shares how his team built custom AI simulators to help students master customer validation, iterate on business models, and gain confidence, while always keeping real-world testing at the center. Ilagan’s approach offers a glimpse into the future of entrepreneurship education, especially in contexts where access to mentors and industry partners is scarce.

The Interview

Q: Joseph, can you describe your role and how it connects to your work with AI in entrepreneurship education?

Ilagan: I lead the BS Information Technology Entrepreneurship Program at Ateneo de Manila University, and I also direct the Ateneo Business Insights Laboratory for Development, or BUILD. Through BUILD, we connect students, faculty, and industry around data and technology initiatives, with a particular focus on generative AI. In our program, we emphasize lean startup principles and customer validation, helping students identify real problems, test their assumptions, and refine their ideas. AI has become a key partner in this process, not as a shortcut, but as a way to enable more experimentation and iterative learning.

Q: What motivated you to build AI-powered simulators for your students?

Ilagan: The reality in the Philippines is that we have limited access to industry mentors. We needed a way to augment mentorship and provide feedback that’s affordable and available anytime. Our goal was to use AI as a great equalizer, giving every student more opportunities to practice, regardless of resources. We wanted simulations that adapt to each student’s business model, supporting truly adaptive learning. But we’re clear: these tools are for preparation. They don’t replace the real-world validation that still needs to happen outside the classroom.

Q: How do these simulators actually work? Can you walk us through an example?

Ilagan: Certainly. We built a simulation bot using the OpenAI API. Students begin by providing their business model, which the system uses as context. The bot then engages them with targeted questions, clarifying their target segment, testing their value proposition, or probing whether the problem they’ve identified is real and significant. After a few back-and-forths, the bot gives structured feedback, highlighting gaps and pointing to areas that need real-world validation. It’s not a substitute for talking to customers, but it gives students a safe, repeatable environment to refine their thinking before stepping outside.

Q: What design choices did you make to ensure these tools would be effective in your context?

Ilagan: We focused on accessibility and adaptability. Deploying through a simple web app kept things familiar and easy to use. Our prompt frameworks adapt to each student’s venture, so feedback is always relevant. We also tracked changes in student confidence and preparedness through pre- and post-session surveys. Beyond the customer validation bot, we built a customer interview simulator with synthetic customers and a financial modeling coach, all grounded in the student’s own business model.

Q: What have you observed in terms of student impact?

Ilagan: Students are practicing more consistently outside class and have become more confident in asking the right questions, about customer segments, value propositions, revenue models, and more. Their expectations are now calibrated; they’re more aware of the gaps that still need to be tested in the real world. For faculty, the reduced setup time lets us focus on critique and reflection. And we’ve seen students inspired to integrate AI into their own ventures, like Sync Studios, which uses AI for user experience diagnostics and image generation for brands.

Q: What’s your ultimate goal for graduates of your program?

Ilagan: We want to graduate entrepreneurs who integrate AI thoughtfully and responsibly in their work. These simulators are meant to augment, not replace, real-world validation. Our hope is that students leave with both the confidence and the humility to keep testing their ideas with real customers, using AI as a tool for deeper, more frequent learning.

What Stands Out

Core idea: AI-powered simulators can democratize access to feedback and mentorship for student entrepreneurs, especially in under-resourced environments.

Classroom design: The simulators are adaptive, grounded in each student’s business model, and accessible through a simple web app, allowing for repeated, self-paced practice.

Student impact: Students gain confidence, practice more consistently, and develop a sharper sense of what needs to be validated in the real world, while faculty can focus on higher-level critique.

Transferable lesson: AI can be a powerful partner in education, not as a substitute for reality, but as a scalable way to prepare students for real-world challenges.

Bio

Joseph Benjamin Ilagan, Ph.D., is Program Director of the BS Information Technology Entrepreneurship program at Ateneo de Manila University and Director of the Ateneo Business Insights Laboratory for Development.

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APIs are technical. The value is human.

OpenAI just introduced three new realtime voice models in the API: one for voice reasoning, one for live translation, and one for live transcription. That may sound like developer infrastructure, because it is. Students and teachers are not likely to “use the API” directly.

But they may soon feel the impact through the education products built on top of it.

Think tutoring tools, classroom platforms, accessibility services, language-learning apps, advising systems, student support centers, and professional learning tools for educators.

The shift is simple: AI learning tools may start to feel less like typing into a chatbot and more like having a natural, real-time conversation.

How is this different from what already exists?

Voice tools already exist in education. We have dictation, captions, lecture transcription, language translation, screen readers, voice assistants, and AI chatbots with speech modes.

The difference is that many of those tools are still separate, limited, or mostly reactive.

A transcription tool can capture what was said, but it may not understand the learning context.

A translation tool can convert language, but it may not help a student ask a better follow-up question.

A chatbot can explain a concept, but the student often has to stop, type, wait, and reframe the question.

A voice assistant can respond to commands, but it may not reason through a multi-step academic task.

What is changing is the combination: voice, reasoning, translation, transcription, context, and action happening closer to real time.

Realtime voice reasoning
What it means for education: Students can talk through problems instead of typing everything out.
Specific example: A student working on algebra can ask, “Why did I move the variable to the other side?” and get a spoken explanation that adapts as they respond.

Live translation
What it means for education: Multilingual learners and families can participate more fully.
Specific example: A parent-teacher conference could include real-time translation so families can ask questions in their preferred language.

Live transcription
What it means for education: Classes, meetings, and study groups can become captions and notes instantly.
Specific example: A lecture could generate live captions, then produce a study guide with key terms, misconceptions, and follow-up questions.

Voice-to-action
What it means for education: Education tools can help complete tasks, not just answer questions.
Specific example: An advisor tool could help a student compare course options, degree requirements, and schedule conflicts in one conversation.

What could this look like?

A high school student working on algebra could say, “I don’t understand why I moved the variable to the other side,” and get a spoken explanation that adapts as they respond.

A college student in a biology lab could talk through an experiment, ask for clarification on a procedure, and get help connecting what they are seeing to the underlying concept.

A multilingual learner could listen to a classroom discussion in their preferred language while still participating in the original conversation.

A student with dyslexia, low vision, or limited mobility could rely more on voice and less on typing, making AI support easier to access.

A teacher could finish a class discussion and have a draft summary, key misconceptions, vocabulary list, and follow-up questions generated from the live transcript.

An academic advisor could use a voice-enabled support tool to help a student compare course options, degree requirements, and scheduling constraints in one conversation.

Why this matters for education

Education is already conversational. Learning happens through questions, corrections, explanations, pauses, confusion, repetition, and practice.

That is why voice matters.

When students have to stop and type, they often simplify the question. When teachers have to manually turn class discussion into follow-up materials, valuable context gets lost. When multilingual families need support, translation delays can become participation barriers.

Realtime voice AI can reduce some of that friction.

Less friction for the student who learns better by talking it out.

Less friction for the teacher trying to turn a live class into useful next steps.

Less friction for multilingual families trying to engage with a school.

Less friction for learners who need accessibility support built into the experience from the start.

When will students and educators get this?

The models are available now for developers through OpenAI’s Realtime API. That means the education impact will come as schools, institutions, and edtech companies build these capabilities into the products people already use.

So the answer is not: students should go use an API.

The answer is: the next generation of education tools can now be built with more natural voice interaction, faster transcription, live translation, and task completion at the center.

The biggest opportunity is not replacing teachers. It is making learning tools better match how learning actually happens: in conversation, in context, and in real time.

Read more from OpenAI: Advancing voice intelligence with new models in the API

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