Today, we’re proud to introduce the inaugural ChatGPT Futures: Class of 2026, recognizing 26 students and young builders using AI in thoughtful, ambitious, and deeply human ways. Here’s a letter from OpenAI’s VP and GM of Education, Leah Belsky, about the driving force behind the project.

The class of 2026 is the first generation to start and finish college with ChatGPT.

They arrived on campus in the fall of 2022 just as AI was beginning to reshape how people learn, create, and work. This generation was ChatGPT’s earliest adopters, sharing the tool with their parents and siblings, friends and teachers. Now, they’re graduating into a world where changes in technology are accelerating every day.

Over the past few years, I’ve spent time visiting campuses, speaking with students and educators, and watching how young people are actually using AI in their daily lives. What I’ve seen has challenged many of the assumptions people make about this generation.

Many students aren’t using AI to avoid work. They’re using it to attempt things they wouldn’t have thought possible before.

I’ve met students who are building study tools for classmates. Translating mental health resources for underserved communities. Advancing scientific research. Designing accessibility tools for peers with disabilities. Turning side projects into real organizations with real impact.

Again and again, I’ve met students who discovered something surprisingly powerful: they don’t have to wait. As Kyle Scenna, a 24 year-old ChatGPT Futures honoree and entrepreneur from the University of Waterloo told us: “I never thought the gap between noticing a problem and building something real could get this small.” He’s not alone in this feeling.

This generation doesn’t have to wait to become experts before getting started.
They don’t have to wait for funding before building.
They don’t have to wait for permission before contributing.

That realization—that you can turn an idea into something tangible faster than ever before—is what inspired ChatGPT Futures.

Meet ChatGPT Futures, Class of 2026

These honorees represent over 20 universities and institutions including MIT, Stanford, Harvard, Oxford, Georgia Tech, UC Berkeley, Yale, Waterloo, Vanderbilt, the University of Toronto, the University of Illinois Urbana-Champaign, and many others.

Some are researchers pushing the frontier of science and discovery. Others are founders building products and organizations from the ground up. Still others are using AI to expand access, strengthen communities, and help people navigate real-world challenges.

What connects them is not a specific discipline or background. It’s a mindset. They saw new tools emerge, got curious, and decided to build. That may become the defining and critical characteristic of this generation.

There are understandable questions about what AI might mean for learning, creativity, and jobs. I work on those questions every day with partners throughout the education ecosystem. But the students I’ve met have also given me a tangible view of what AI can unlock right now. It’s agency.

AI doesn’t replace ambition. It amplifies it.

For decades, the ability to build something—whether a product, a research project, a movement, or a company—often depended on access. Access to technical training, institutional support, networks, or funding. Those barriers haven’t disappeared, but they are beginning to shift. Michelle Lawson, a 20-year-old student at Smith College and a ChatGPT Futures honoree shared with us, “I’ve always believed that you can achieve everything that you can imagine, as long as you’re given the right support and resources. AI has made that happen not only for myself, but for hundreds of thousands of people.”

Today, a student with curiosity and determination can prototype an idea faster, learn new skills independently, and contribute meaningfully in ways that once required far more resources.

That doesn’t make human judgment, creativity, or hard work less important. If anything, it makes them more so.

Because the students who will thrive in this next chapter won’t simply be the ones who know how AI works. They’ll be the ones who know how to use it thoughtfully: to learn continuously, identify meaningful problems, collaborate effectively, and create things that matter to other people.

Education has a critical role to play in unlocking this sense of agency for all students. The goal is not simply to teach students how AI works or how to prompt effectively. Schools and universities must create space for students to build and create with AI, guided by teachers.

The goal should not just be AI literacy. We need to help students become adaptable thinkers and builders—people who can navigate ambiguity, pursue ideas with curiosity, and turn learning into action.

At OpenAI, we believe students should help shape the future of AI, not simply inherit it. To date, we’ve supported this transformation with tools and resources to support educators and students including ChatGPTEdu, 100 chats for Students, Study Mode, and partnerships with organizations like the American Federation for Teachers. ChatGPT Futures is one more way we plan to celebrate the young people already doing exactly that.

Each member of the inaugural class will receive a $10,000 grant to continue advancing their work, and honorees and honorable mentions will receive access to our frontier models.

But more than anything, we hope this program shines a light on a broader truth: The future of AI will not be defined only by the capabilities of the technology itself.

It will be defined by the people who choose to use it with curiosity, responsibility, creativity, and purpose. “The exciting thing is this is just the beginning,” Nolan Windham, a 23-year-old Head of AI at a prominent hedge fund and ChatGPT Futures honoree told me. “Many young people will recognize their place as teachers for a society looking to learn to use the technology of the future.”

Congratulations to the inaugural ChatGPT Futures Class of 2026. We can’t wait to see the future you’ll build.

Meet ChatGPT Futures, Class of 2026

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Now comes the practical question: what does it actually take to build one?

For colleges and universities, an agent is not just a chatbot with a better prompt. A useful agent has a job, a user, a set of trusted knowledge sources, access to tools, rules for what it can and cannot do, a process for human review, and a way to improve over time.

Think of an agent as six building blocks:

1. The job: what the agent is responsible for

2. The knowledge: what information it can rely on

3. The tools: what systems or actions it can use

4. The workflow: the steps it follows

5. The guardrails: what keeps it safe and appropriate

6. The evaluation loop: how you know it is working

Build those well, and an agent can become a reliable partner in teaching, advising, research, student services, career readiness, and campus operations.

1. Start With One Job

The first mistake is building an agent that tries to do everything.

• “Student success agent” is too broad.

• “Help first-year students prepare for advising appointments” is buildable.

• “Faculty support agent” is too broad.

• “Help faculty draft AI-aware assignments and rubrics” is buildable.

• “Research agent” is too broad.

• “Help graduate students create a first-pass literature review plan” is buildable.

A strong agent begins with a job description.

Agent name:
Primary user:
Job to be done:
What the agent should produce:
What the agent should never do:
When the agent should ask for human review:

Example:

Agent name: Advising Prep Agent
Primary user: Academic advisors
Job to be done: Help advisors prepare for student meetings.
What it should produce: Meeting agenda, likely discussion topics, policy reminders, suggested follow-up questions.
What it should never do: Make final degree decisions, override advisor judgment, or update student records without approval.
When it should ask for human review: When policy is ambiguous, a student is at academic risk, or an exception may be needed.

This is the foundation. If the job is unclear, everything else will drift.

2. Define the User and the Moment

Agents work best when they are built for a specific user in a specific moment.

A student using an interview coach at midnight needs something different from a career advisor reviewing student progress. A faculty member designing a syllabus needs something different from an instructional designer supporting ten departments. A staff member answering student emails needs something different from a vice provost reviewing trends.

Before building, define the moment:

Who is using the agent?
What are they trying to accomplish?
What do they already know?
What information do they need?
What decision or output comes next?
What would make this experience meaningfully better?

For example:

User: Student applying for internships
Moment: Preparing for a behavioral interview
Need: Practice, feedback, and stronger examples
Output: Improved interview answers and a practice plan

That clarity keeps the agent grounded in real use.

3. Give the Agent Trusted Knowledge

An agent should not guess when institutional knowledge matters.

For higher education, the knowledge layer might include course catalogs, advising guides, student handbooks, career center resources, policy documents, rubrics, writing guides, research guides, FAQs, training materials, or department-specific procedures.

Before launch, decide exactly what the agent can use.

Knowledge source:
Owner:
Last updated:
Allowed users:
Allowed use:
Review cadence:

Example:

Knowledge source: Undergraduate advising handbook
Owner: Academic advising office
Last updated: August 2026
Allowed users: Advisors
Allowed use: Advising prep and policy explanation
Review cadence: Every semester

Good agents are grounded in authoritative sources. They should also know when they do not have enough information.

A useful instruction:

If the answer depends on university policy and you do not have an approved source, say that you cannot confirm the answer and recommend the user check with the relevant office.

4. Connect the Right Tools

Tools are what make an agent different from a static assistant.

A tool might let the agent search documents, read a file, check a calendar, draft an email, query a database, create a ticket, summarize survey results, or generate a report.

Start with read-only tools whenever possible. Then add write actions only when review and permissions are clear.

Use this tool design checklist:

Tool name:
What it lets the agent do:
Read or write access:
Data it can access:
Who is allowed to use it:
What approval is required:
What gets logged:
Failure mode:

Example:

Tool name: Policy search
What it lets the agent do: Search approved university policy documents
Read or write access: Read-only
Data it can access: Public and internal policy documents
Who is allowed to use it: Staff and advisors
Approval required: None for search; human review for final guidance
What gets logged: Query, source used, response generated
Failure mode: If no source is found, agent escalates or asks user to verify

For higher-risk tools, approval matters:

Tool name: Student email draft
What it lets the agent do: Draft an email to a student
Read or write access: Draft only
Approval required: Staff member must review and send
Failure mode: Agent cannot send directly

The rule is simple: the more impact an action has, the more human review it needs.

5. Write the Agent Instructions

Instructions are the agent’s operating manual.

They tell the agent who it serves, what it should optimize for, how it should behave, what sources to use, what to avoid, when to ask questions, and when to escalate.

A good instruction set includes:

Role:
Goal:
Audience:
Tone:
Knowledge sources:
Workflow:
Boundaries:
Escalation rules:
Output format:

Example:

Role: You are an advising prep agent for academic advisors.
Goal: Help advisors prepare for student meetings by organizing relevant context, identifying likely discussion topics, and drafting a meeting agenda.
Audience: Academic advisors. Write clearly and concisely. Do not speak directly to students unless asked to draft language for advisor review.
Knowledge sources: Use only approved advising guides, degree requirements, catalog materials, and advisor-provided notes.

Workflow:
1. Ask for the meeting goal.
2. Identify what context is available.
3. Summarize relevant information.
4. Flag issues that need advisor judgment.
5. Draft a meeting agenda.
6. Suggest follow-up questions.

Boundaries:
Do not make final degree determinations.
Do not promise graduation eligibility.
Do not update student records.
Do not infer sensitive information.
Do not provide policy guidance without an approved source.

Escalation: If policy is ambiguous, if an exception may be required, or if the student appears to be in academic distress, recommend advisor review.
Output format: Brief summary, key issues, suggested agenda, questions to ask, items requiring review.

This is where the agent becomes reliable. Not because it knows everything, but because it knows how to operate.

6. Design the Workflow

A strong agent follows a repeatable path.

For most higher education agents, the workflow should look like this:

Intake 
Ask what the user is trying to do.

Context check 
Identify what information is available and what is missing.

Retrieve
Use approved sources or tools.

Draft 
Create the first output.

Review
Check for accuracy, risk, tone, and completeness.

Ask for approval
Pause before any meaningful action.

Finalize
Produce the final draft, summary, plan, or recommendation.

Capture feedback
Ask whether the output was useful and what should improve.

Example: Career Interview Coach

1. Ask for target role and interview type.
2. Ask for resume or experience summary.
3. Ask for job description.
4. Generate 5 likely questions.
5. Ask one question at a time.
6. Give feedback after each answer.
7. Help revise the answer.
8. End with a practice plan.

Example: Course Design Agent

1. Ask for course level, topic, and learning goals.
2. Ask what the faculty member wants to create.
3. Draft assignment, rubric, or activity.
4. Identify where AI use should be allowed, limited, or disclosed.
5. Suggest assessment criteria.
6. Ask faculty to review for disciplinary fit.
7. Produce a clean final version.

The workflow is the product. The model helps execute it.

7. Build Guardrails Into the Agent

Guardrails should be part of the design from the beginning.

In higher education, guardrails usually need to cover academic integrity, privacy, equity, accessibility, policy accuracy, and human review.

Use a simple risk map:

Green: The agent can do this directly.
Yellow: The agent can draft or recommend, but a human must review.
Red: The agent should not do this.

Example for a student-facing career agent:

Green:
Practice interview questions.
Give feedback on clarity.
Help improve structure.
Suggest stronger phrasing.

Yellow:
Resume bullet revisions.
Cover letter drafts.
Networking messages.

Red:
Inventing experience.
Creating fake metrics.
Misrepresenting qualifications.
Submitting applications without student review.

Example for an advising agent:

Green:
Draft meeting agendas.
Summarize approved policy.
Suggest follow-up questions.

Yellow:
Interpret degree progress.
Draft student follow-up emails.
Flag potential risks.

Red:
Make final degree decisions.
Change student records.
Approve exceptions.
Handle emergency situations without escalation.

The goal is not to make the agent timid. The goal is to make it trustworthy.

8. Decide the Human-in-the-Loop Points

Every agent needs clear moments where a person stays in control.

Human review is especially important when the agent is:

1. Using sensitive information

2. Interpreting policy

3. Sending communications

4. Updating records

5. Affecting student outcomes

6. Making recommendations with real consequences

Design these pauses intentionally.

Before the agent sends anything: require review.
Before the agent updates anything: require approval.
Before the agent gives policy guidance: cite source or escalate.
Before the agent handles exceptions: route to a person.
Before the agent acts on sensitive data: confirm permission.

For many campus agents, the best first version is “draft only.” The agent prepares the work. A human decides what happens next.

9. Create Test Scenarios

Before students, faculty, or staff use an agent, test it with realistic scenarios.

Do not only test easy cases. Include messy cases, ambiguous cases, and cases where the agent should refuse or escalate.

Test scenario template:

Scenario:
User input:
Expected behavior:
Source required:
Human review needed:
Pass/fail criteria:

Example:

Scenario: Student asks if they can graduate next semester.
User input: “Do I have enough credits to graduate in May?”
Expected behavior: Agent should not make a final determination. It should explain that graduation eligibility requires advisor or registrar review.
Source required: Degree audit policy or advising guidance.
Human review needed: Yes.
Pass/fail criteria: Agent does not give a definitive answer and routes to advisor review.

Example:

Scenario: Student wants help with a cover letter.
User input: “Write me a cover letter for this role. Here is my resume.”
Expected behavior: Agent drafts a truthful cover letter based only on provided experience.
Human review needed: Student review.
Pass/fail criteria: Agent does not invent accomplishments or metrics.

Agents should be tested for judgment, not just fluency.

10. Evaluate the Agent

Evaluation is how you keep quality from becoming vibes.

You need to check whether the agent is accurate, useful, safe, and aligned with the institution’s expectations.

Evaluation criteria:

Accuracy: 
Did the agent use the right information?

Grounding: 
Did it rely on approved sources?

Usefulness: 
Did it help the user complete the task?

Judgment: 
Did it ask for review or escalation at the right time?

Safety: 
Did it avoid sensitive data misuse or inappropriate claims?

Tone: 
Did it sound clear, respectful, and appropriate for the audience?

Completeness: 
Did it produce the expected output?

A simple scoring model:
3 = Strong
2 = Acceptable with minor edits
1 = Needs major revision
0 = Unsafe or incorrect

Run evals before launch, after major changes, and whenever you add new tools or knowledge sources.

11. Pilot With Real Users

Do not launch campus-wide first.

Pilot the agent with a small group that understands the workflow. If it is a career agent, pilot with career coaches and a limited student group. If it is an advising agent, pilot with advisors. If it is a course design agent, pilot with faculty who are already experimenting with AI.

Pilot plan:

Agent:
Pilot audience:
Duration:
Use cases included:
Tools enabled:
Data allowed:
Human review points:
Success measures:
Feedback method:
Decision after pilot:

Example:

Agent: Career Interview Coach
Pilot audience: 50 students and 3 career coaches
Duration: 4 weeks
Use cases: Behavioral interview practice, technical interview practice, feedback summaries
Tools enabled: Resume upload, job description analysis
Data allowed: Student-provided materials only
Human review: Career coaches review sample outputs
Success measures: Student confidence, quality of feedback, repeat usage, coach assessment
Decision after pilot: Expand, revise, or pause

The pilot should answer one question: is this agent useful enough, safe enough, and clear enough to expand?

12. Assign Ownership

Agents need owners after launch.

Someone has to maintain the knowledge base, review feedback, update instructions, monitor quality, and decide when the agent should change.

Ownership model:
Functional owner:
Technical owner:
Policy/governance partner:
Support contact:
Review cadence:
Escalation path:

Example:

Functional owner: Career Services
Technical owner: IT AI enablement team
Policy partner: Student affairs and legal
Support contact: Career Services operations lead
Review cadence: Monthly for first 3 months, then quarterly
Escalation path: Career coach review for high-stakes application questions

Without ownership, agents get stale. In higher education, stale information is not just inconvenient. It can be misleading.

13. Improve the Agent Over Time

An agent should get better as people use it.

Track:

What questions users ask most often
Where the agent gets stuck
Where users edit the output
Where the agent escalates
Where it should have escalated but did not
Which sources are missing
Which prompts produce the best results
What users say was most helpful

Then improve:

Update instructions.
Add better knowledge sources.
Remove risky capabilities.
Improve tool descriptions.
Add examples.
Refine escalation rules.
Expand only when quality is stable.

This is how an agent becomes part of institutional practice instead of a one-time experiment.

Five Agents Higher Education Can Build First

1. Career Prep Agent
Helps students tailor resumes, practice interviews, prepare networking messages, and build confidence before employer conversations.

2. Advising Prep Agent
Helps advisors prepare for meetings, summarize approved context, draft agendas, and identify questions that require human judgment.

3. Course Design Agent
Helps faculty draft assignments, rubrics, discussion prompts, practice questions, and AI use statements.

4. Policy Navigator Agent
Helps students and staff understand approved policies, find relevant sources, and know when to escalate.

5. Research Starter Agent
Helps students and researchers define a topic, generate search terms, summarize sources, identify gaps, and flag claims that need verification.

Each of these has a clear user, a clear workflow, and a clear place for human review.

The Build Sheet

Here is the simplest version of what every higher education agent needs before launch:

Agent name:
Primary user:
Job to be done:
Moment of use:
Inputs:
Approved knowledge sources:
Tools:
Workflow steps:
Green/yellow/red boundaries:
Human approval points:
Escalation rules:
Output format:
Test scenarios:
Evaluation criteria:
Pilot audience:
Owner:
Review cadence:

If you cannot fill this out, the agent is not ready.

The Principle

Build agents like you are designing a new campus service.

Because you are.

The interface may be conversational. The system may be powered by AI. But the work is familiar: understand the user, define the service, connect the right information, set expectations, manage risk, measure quality, and improve over time.

Start small. Build one useful workflow. Test it with real people. Keep humans in control. Improve it until it earns trust.

Have questions? Please drop them in the comments below.

Education is entering a new phase of AI adoption.

The first phase gave institutions access to intelligence: AI could answer questions, summarize material, generate content, and improve individual productivity. That shift mattered because it made AI useful in everyday academic and administrative work.

Agents introduce something else. Their value is not better conversation, but better execution: the ability to hold context across time, use tools, operate within permissions, and advance multi-step work across systems and teams.

That matters in education because the field’s hardest problems are rarely about access to information — they are about coordination. A student’s experience runs across admissions, financial aid, advising, the LMS, tutoring, career services, and more. Faculty work spans teaching, grading, communication, office hours, research, and administration. Staff are expected to deliver highly personalized support through fragmented systems and limited capacity.

Agents fit here because they help institutions move from insight to follow-through.

Where agents will matter first

The earliest value will not come from a generic AI tutor for everything. It will come from operational workflows where context is scattered, timing matters, and human teams are already overloaded.

• Student success: Agents can synthesize attendance, LMS activity, advising notes, and support history to identify when intervention may be needed, then prepare a case summary, recommend next steps, and draft outreach for staff review. Staff decide what action to take and what to share with students.

• Admissions: Agents can assemble applicant context, flag incomplete materials, surface unusual circumstances, and prepare files for deeper human review. Admissions teams make decisions and apply institutional judgment.

• Teaching and learning: Agents can help faculty turn course goals into assessments, identify common patterns in student misunderstanding, prepare follow-up materials, and reduce operational load over the term. Faculty remain in control of instructional choices, academic standards, and what is shared with students.

• Student services: Financial aid, registrar functions, disability accommodations, IT support, and advising all depend on policy-heavy, high-volume workflows. Agents can help route requests, prepare context, document actions, and improve consistency, while staff retain responsibility for decisions, approvals, and student-facing communication.

• Career readiness: Agents can help career offices personalize outreach, synthesize labor market signals, and extend tailored guidance to far more students than current staffing models allow. Career teams shape the guidance, approve outreach, and determine how support is delivered.

The pattern is straightforward: agents are most useful where institutions need better orchestration, not simply better answers.

What makes education different

Education does not just need capable AI — it needs accountable AI.

Student data is sensitive, academic and administrative decisions carry real consequences, faculty autonomy matters, and policy constraints are real. In that environment, controlled delegation is the right model.

Well-designed agents can prepare, route, recommend, draft, and escalate within clear boundaries. They can operate with permissions, create audit trails, and preserve human judgment where it matters. In education, governance is not a constraint on adoption; it is the basis for it. We have comprehensive controls to let university admins stay in charge.

Why this is bigger than custom GPTs

Many institutions still interpret agents through the lens of custom GPTs, but that frame is too narrow.

Custom GPTs package knowledge and instructions into a reusable interface. Useful, but limited. Education’s biggest opportunities are not about packaging answers; they are about improving execution across fragmented systems.

A student asking how to appeal a financial aid decision is one kind of AI interaction. An agent that can retrieve the relevant policy, identify missing materials, prepare the case summary, draft the next communication, and route it correctly is another. One supports inquiry. The other supports institutional action.

How adoption will happen

Most institutions will not start with a sweeping agent strategy. They will start with a workflow: an advisor building a better pre-meeting prep flow, a faculty team improving course support, a student services office redesigning intake and triage, or a career center scaling personalized advising.

That is the right path.

The key transition will be from personal agents to shared institutional agents. A personal agent helps one educator, advisor, or administrator work more effectively in context. A shared agent captures a proven workflow and makes it reusable across a team or unit, with the right governance and oversight.

That is how adoption compounds. Institutions do not simply add AI tools; they scale better ways of working.

What leaders should do now

Start with a workflow that matters.

Ask:

• Where is institutional friction delaying support?

• Which workflows rely on judgment but are slowed by fragmented context?

• Where would better coordination clearly improve outcomes?

• Which use cases have clear ownership and clear boundaries?

• What governance needs to be in place from the start?

In the next couple of weeks, we’ll share more on how to build agents in education responsibly and effectively. In the meantime, start with a real workflow, a real owner, and a real problem.

A more capable institution

The long-term opportunity is not just efficiency. Agents create the possibility of institutions that are more responsive, more coordinated, and more capable of delivering the support they already aspire to provide. They can help institutions act earlier, serve more personally, and scale quality without adding more burden to stretched teams.

Education does not need more AI that only informs. It needs AI that helps institutions act with greater coherence, care, and capacity.

We can’t wait to build this with you.

College career prep can feel like trying to assemble a plane while it’s already rolling down the runway. You’re supposed to know which roles fit you, decode job descriptions, write polished resumes, network with strangers, prepare for technical interviews, sound confident in behavioral interviews, research industries, and send professional emails, often while juggling classes, clubs, jobs, family, and life.

Used well, ChatGPT can be your career practice room: a private place to rehearse, ask basic questions without embarrassment, pressure-test your thinking, sharpen your communication, and turn messy ambition into a plan.

Students are already using it this way. Nae Nae Chairatchaneeboon, a BS Economics student in the Class of 2025, used ChatGPT throughout her recruiting process for investment banking and venture capital roles.

“When I was applying and preparing for investment banking interviews, I used ChatGPT as a mock interviewer for both behavioral and technical questions. For example, I’d ask it to pretend to be a ‘first-year investment banking analyst’ or ‘managing director’ and have it run a ~30-minute interview while I answered. I also used Voice mode to make it feel more like a real interview.” — Nae Nae Chairatchaneeboon

Below are 8 practical ways to start.

1. Start by Exploring What Fits

Before you tailor a resume or prep for an interview, it helps to understand what you’re aiming for.

Students often begin career planning with a list of job titles: consultant, analyst, product manager, researcher, founder, designer, policy associate. ChatGPT can help translate those titles into real work: what people do day to day, what skills matter, which paths fit your interests, and where you might want to learn more.

Try this:

Act as a career coach for a college student. 
I’m studying [major], and I’m interested in [industries, skills, problems, or causes]. 
I’ve done [classes, internships, projects, clubs, jobs, or volunteer work].
Help me identify 5 career paths that could fit me. 

For each one, explain:

1. What the work is actually like
2. What skills I already have that transfer
3. What skills I should build next
4. What kinds of internships or entry-level roles to search for
5. One small next step I can take this week

Ask me 5 questions before giving recommendations.

You can also paste in a job description and ask ChatGPT to decode it:

Here is a job description I’m considering: [paste job description].
Translate it into plain English. 
What would I actually do in this role? 
Which qualifications seem essential, and which are nice-to-have? 
What parts of my background should I highlight if I apply?

This helps move the process from “I need an internship” to “I’m building evidence for a direction.”

2. Turn Your Experience Into Stronger Application Materials

Once you know the kinds of roles you want, ChatGPT can help you shape your resume, cover letters, LinkedIn summary, and application responses.

The best use is collaborative. You bring the real experience. ChatGPT helps you make it clearer, more specific, and more aligned to the opportunity.

Try this:

Help me improve my resume bullets for [role or industry]. 
Here are my rough notes: [paste notes]

For each bullet, create 3 versions:

1. A concise version
2. A metrics-driven version
3. A version tailored to this job description: [paste job description]

Keep everything truthful. 
If a metric or detail is missing, mark it as [ADD DETAIL] instead of making one up.

For a recruiter-style review:

Review my resume as if you are a recruiter hiring for [role]. 

Give me:

1. The strongest signals
2. The weakest or vaguest bullets
3. Skills from the job description that are missing
4. Places where I need more evidence
5. A revised version of the top 3 bullets

Be specific and direct.

For a cover letter or short application response:

Help me draft a short application response for this prompt: [paste prompt].
Use my background: [paste resume or notes].
Make it specific to [company/program/role].
Keep it natural, concise, and student-appropriate.
Do not exaggerate my experience.

A strong application is not about sounding impressive in the abstract. It is about helping someone quickly understand what you have done, what you can do, and why you are a fit.

3. Learn the Skills You’ll Be Expected to Know

Career prep is full of moments where you realize there is something everyone seems to know already.

A formula. A framework. A technical term. A case interview structure. A financial statement concept. A design critique vocabulary. A lab technique. A Git command. A policy memo format.

ChatGPT can help you learn those concepts privately and on demand.

Nae Nae used it while learning financial modeling in Excel:

“I’d use it to figure out which formula to use and debug what I did wrong. It helped me understand what each formula actually means. It saved me from feeling like I was asking a ‘stupid’ question to upperclassmen and helped me learn faster whenever I needed it, like at 2 am in the morning when most people would not have responded.”

Try this:

I’m learning [skill] for [role or industry]. 
Build me a beginner-friendly crash course.

Include:

1. The 10 concepts I need to know first
2. Simple explanations
3. Practice exercises
4. Common mistakes
5. A 7-day study plan

For Excel or technical troubleshooting:

I’m getting this issue: [describe error or problem].
Here is what I tried: [paste formula, code, model, or workflow].
Here is what I expected to happen: [describe expected result].
Debug it step by step. Explain the underlying concept so I can fix similar problems next time.

For interview-specific technical prep:

Teach me the technical concepts I need for a [role] interview in [industry]. 
Start with the fundamentals, then quiz me one concept at a time. 
If I get something wrong, explain it simply and give me another practice question.

The goal is not just to get the answer. It is to build fluency.

4. Research Companies, Roles, and Industries Faster

After you have a target role and a skill plan, the next step is context. What does this company care about? What is changing in the industry? What questions might come up in an interview? What should you know before a networking call?

When preparing for venture capital interviews, Nae Nae used ChatGPT to get up to speed on markets and industries more quickly.

“I was able to understand the size and trends within a market without going through 30+ websites and consolidating the information which would usually take me 3–5 hours to digest.”

Try this:

I’m preparing for an interview in [industry or market]. 

Give me a briefing that covers:

1. What the market is
2. Major customer segments
3. Key trends
4. Important companies or players
5. Common business models
6. Risks or open questions
7. Smart questions I could ask in an interview

Flag anything I should verify with primary sources.

For company prep:

Help me prepare for an interview with [company]. 

Create a company briefing with:

1. What the company does
2. Its customers or users
3. Its business model
4. Recent products, announcements, or strategic priorities
5. Competitors
6. Questions I should be ready to answer
7. Thoughtful questions I can ask them

For a more advanced version:

Act as a senior interviewer in [industry]. 
Based on this company and role, what would you expect a strong student candidate to understand? 
Give me a prep checklist and then quiz me.

Research becomes more powerful when you use it to think, not just to collect facts.

5. Practice Interviews Before They Count

This is where ChatGPT can become a high-repetition practice partner.

You can rehearse behavioral questions, technical questions, case prompts, role-specific scenarios, presentation delivery, and follow-up questions. You can also change the interviewer persona depending on the situation: recruiter, peer interviewer, hiring manager, senior executive, alumnus, investor, or technical lead.

Nae Nae used ChatGPT to simulate investment banking interviews from different perspectives and found that it helped her feel more confident before practicing with upperclassmen.

“I really liked that it gave feedback afterward, helping me understand what I did well and what I could improve on. It also made me feel a lot more confident before doing in-person mocks with upperclassmen after having done a few with ChatGPT and knowing the flow of an interview.”

Try this:

Act as a [recruiter / first-year analyst / hiring manager / MD / technical interviewer] interviewing me for [role] at [company or industry].
Run a realistic 30-minute interview. 
Ask one question at a time. 

Include:

- Behavioral questions
- Role-specific technical questions
- Follow-up questions based on my answers

After the interview, grade me on clarity, structure, confidence, technical accuracy, and role fit. 
Give me specific feedback and a practice plan.

For behavioral interview stories:

Help me build a behavioral interview story bank using the STAR method.

Ask me for examples related to:

- Leadership
- Teamwork
- Conflict
- Failure
- Ambiguity
- Problem-solving
- Working under pressure

Then help me turn my answers into polished, natural stories that still sound like me.

For a harder mock:

Interview me like a skeptical interviewer. 
Push back when my answer is vague, too long, too rehearsed, or missing evidence. 
Ask follow-up questions until my answer is strong.

If you have Voice mode available, try practicing out loud. Interviews are spoken experiences, and it can feel different when you have to answer in real time.

6. Pressure-Test Your Thinking and Presentations

Some career paths require more than answering questions. You may need to present a market, defend an investment thesis, walk through a case, explain a research project, pitch a startup idea, critique a product, or teach someone how you solved a problem.

ChatGPT can help you rehearse the thinking behind the presentation and the delivery.

Try this:

Act as a senior [investor / consultant / product leader / researcher / hiring manager]. 
I’m going to present my thinking on [topic].

First, help me structure the presentation. 
Then ask tough questions about my assumptions, evidence, logic, and recommendations. 
After that, give me feedback on what was clear, what was weak, and what I should revise.

For an investment or market presentation:

Help me build a 5-slide investment committee-style presentation on [company or market]:

1. Market overview
2. Why now
3. Customer problem
4. Competitive landscape
5. Risks and recommendation

Then challenge the argument like a senior investor would.

For a project walkthrough:

Help me prepare to explain this project in an interview: [paste project description].

Create:

1. A 60-second version
2. A 3-minute version
3. A technical deep dive
4. Likely follow-up questions
5. Strong answers that stay truthful to my actual work

The more you practice explaining your thinking, the more prepared you become for interviews, networking calls, and real work.

7. Write Professional Messages With More Confidence

A lot of career progress happens through small pieces of writing: the cold email, the thank-you note, the recruiter reply, the LinkedIn message, the networking follow-up, the “just checking in” email.

These messages can be awkward because the tone is hard to calibrate. ChatGPT can help you sound professional, concise, and still like yourself.

Try this:

Rewrite this email to sound professional, warm, and concise. 
Keep my voice and avoid making it overly formal.
Context: [who I’m writing to and why]
Draft: [paste draft]

Give me 3 versions:

1. Friendly
2. More polished
3. Very concise

For networking:

Write a short networking message to [person or role]. 
I’m a student at [school] interested in [field]. 
I’d like to ask for a 15-minute conversation to learn about their path.

Make it specific, respectful, and natural. Keep it under 100 words.

For a thank-you note:

Write a thank-you email after an interview. 
Mention these details from the conversation: [paste details]. 
Keep it under 120 words. Make it warm, specific, and not generic.

Clear writing makes it easier for people to help you.

8. Build a Career Prep System You Can Keep Using

The biggest advantage comes when ChatGPT becomes part of a repeatable workflow.

Instead of starting from scratch every time, create a career prep thread or project with your resume, target roles, story bank, deadlines, skill gaps, and feedback from past practice sessions. As your goals evolve, your prep system evolves with you.

Try this setup prompt:

You are my career readiness coach. 
Your job is to help me prepare for [target roles or industries] while keeping my work truthful, specific, and in my own voice.

Here is my background:

- Resume: [paste or upload]
- Target roles: [list]
- Target companies or industries: [list]
- Story bank: [paste examples]
- Skill gaps: [list]
- Upcoming deadlines: [list]

When I ask for help, challenge vague claims, ask for evidence, and help me improve through practice.

Then use it each week:

Create a 45-minute career prep plan for this week. 

Include:

1. One application improvement
2. One networking action
3. One interview practice task
4. One skill-building task
5. One reflection question

Make it realistic for a busy student.

This turns career readiness from a last-minute scramble into a practice rhythm.

A Note on Using ChatGPT Responsibly

ChatGPT can help you prepare, practice, and communicate more clearly. You should still be the source of truth.

Use it to brainstorm, revise, research, rehearse, and learn. Verify important facts. Follow your school’s academic and career policies. Do not invent experience, fake metrics, misrepresent your skills, or send messages that no longer sound like you.

The goal is to become more prepared, not less authentic.

Your Future Self Needs Reps

The students who get the most out of ChatGPT are the ones who use it actively: asking sharper questions, practicing out loud, improving their materials, testing their reasoning, and coming back to iterate.

Career readiness has always required practice. ChatGPT makes that practice more accessible, more personal, and easier to start.

Start with one prompt. Decode one job description. Rewrite one resume bullet. Run one mock interview.

By the time you walk into the real conversation, you will have already practiced becoming the candidate you want to be.

Colleges and universities run on work that repeats across departments, terms, programs, and academic years.

Workspace Agents in ChatGPT are designed for that kind of structured, repeatable work. They help higher education teams turn a familiar process into a reusable workflow, so people do not have to start from scratch each time.

Workspace Agents are available in research preview for eligible ChatGPT Edu workspaces. They are off by default, and workspace admins can manage access and permissions.

What is a Workspace Agent?

A Workspace Agent is a reusable workflow in ChatGPT that can be given a clear job, a set of instructions, and access to approved tools or files. It can follow steps, gather context, draft outputs, and, when allowed, help move work forward across connected apps.

A simple way to think about an agent is:

• A trigger: what starts the work, such as a manual request, a new form submission, or a scheduled run.

• A process: the steps the agent should follow.

• Tools and context: the approved files, apps, or systems it can use.

• Guardrails: what it should check, when it should pause, and when it should ask for human approval.

Agents are best suited for workflows that are repeatable, structured, and easy for a person to review. For one-time writing help, exploratory questions, or open-ended brainstorming, regular ChatGPT is often the better place to start.

How agents differ from regular ChatGPT

Many people use ChatGPT for one task at a time: summarize this document, draft this email, explain this policy, or help brainstorm ideas.

Agents are different because they are built for workflows that happen more than once. A team can define the process once, test it, improve it, and share it with others in the workspace.

For example, regular ChatGPT might help a staff member summarize one meeting note. An agent could support a recurring workflow that gathers weekly inputs, drafts a standard update, flags missing information, and prepares a version for review.

The person or team still stays in control. Agents should be reviewed, corrected, and guided, especially before anything is shared, sent, or used for a decision.

Ways higher education teams might use agents

In colleges and universities, agents may be especially useful for workflows that span departments, shared documents, communication channels, and recurring deadlines.

Course change request agent

A registrar or academic affairs team could use an agent to help triage course change requests from departments.

The agent could review submitted forms, check whether required fields are complete, compare the request against approved catalog guidelines, flag missing approvals, draft next steps for the department, and route the request to the right reviewer.

For example, if a department submits a request to add a new course or update prerequisites, the agent could organize the request, identify missing information, and prepare a clean review packet for the curriculum committee.

This helps academic operations teams move requests forward with clearer handoffs and fewer manual follow-ups.

Faculty AI support intake agent

A center for teaching and learning could use an agent to help manage faculty requests for AI support, instructional design help, or course redesign consultations.

The agent could review intake submissions, identify the type of support needed, draft a short summary for the support team, suggest relevant resources, and prepare a first response for staff review.

For example, if a faculty member asks for help redesigning an assignment with AI, the agent could gather the course context, identify whether the request is about assessment, academic integrity, accessibility, or student engagement, and prepare a clear handoff to the right instructional support lead.

This helps teaching and learning teams support more faculty while keeping the work thoughtful and personalized.

Weekly student services trends agent

A student affairs, advising, financial aid, or registrar team could use an agent to summarize patterns from approved internal sources each week.

The agent could pull together common questions, recurring blockers, high-volume request areas, and unresolved issues. It could then draft a weekly summary for leadership with recommended follow-ups.

For example, the agent might surface that many students are asking about add/drop deadlines, financial aid verification, transcript processing delays, or advising availability. It could prepare a concise update for the team to review before sharing.

This helps student services teams see patterns earlier and respond with more coordinated support.

Grant readiness agent

A research administration team could use an agent to support early-stage grant preparation.

The agent could review a funding opportunity, extract key requirements, create a checklist of required documents, flag eligibility criteria, summarize deadlines, and draft an internal planning brief for the principal investigator and research support staff.

For example, when a faculty member is considering a new grant opportunity, the agent could prepare a structured summary that includes submission requirements, cost-share considerations, required attachments, internal routing steps, and key dates.

This helps research teams move faster while keeping final review, compliance, and submission decisions with the appropriate human experts.

Campus communications agent

A communications, IT, or change management team could use an agent to help prepare recurring campus updates about major technology rollouts, policy changes, or new resources.

The agent could gather approved source materials, draft versions for different audiences, and prepare an FAQ for review.

For example, during a ChatGPT Edu rollout, the agent could draft a faculty announcement, a student-facing FAQ, a staff enablement note, a department chair update, and a short leadership summary based on the same approved guidance.

This helps campus teams communicate consistently while still tailoring messages to each audience.

IT software request agent

A campus IT team could use an agent to triage software requests from faculty and staff.

The agent could review the request, check whether the tool is already approved, identify data privacy or accessibility considerations, route the request to the right approval path, and draft next steps for the requester.

For example, if a faculty member requests a new classroom tool, the agent could check whether the software is already licensed, whether it requires student data, whether accessibility review is needed, and which internal team should review it next.

This helps IT teams manage demand while making the process clearer for the person requesting support.

Program review preparation agent

An academic department could use an agent to help prepare recurring program review materials.

The agent could gather approved inputs, such as course lists, enrollment summaries, assessment notes, advising feedback, and prior action items. It could then draft a structured packet for department leaders to review.

The agent could also flag missing materials, inconsistent formatting, or areas where the department needs to add human judgment before the report is finalized.

This helps academic leaders spend more time on interpretation and planning, and less time rebuilding the same packet from scratch.

Faculty onboarding agent

A department, school, or faculty affairs team could use an agent to help onboard new faculty.

The agent could assemble role-specific resources, draft a department-specific welcome guide, prepare a checklist of required steps, and route questions to the right office.

For example, a new adjunct faculty member might need LMS access, syllabus templates, academic policy links, office hour guidance, AI use policy guidance, and department contacts. The agent could prepare a personalized onboarding packet for staff review.

This helps departments give new faculty a more organized and consistent start.

Accreditation evidence collection agent

An accreditation, assessment, or institutional effectiveness team could use an agent to organize recurring evidence collection.

The agent could gather approved source materials, map files to standards, flag missing evidence, draft summaries, and prepare reviewer notes.

For example, during an accreditation cycle, the agent could help organize documentation for learning outcomes, assessment practices, faculty qualifications, student support services, and continuous improvement efforts.

This helps teams manage complex, multi-source documentation while keeping final interpretation and submission in human hands.

Choosing a good first workflow

The best first agent is usually a workflow your team already understands.

Look for work that has a clear start, a clear set of steps, and a clear human reviewer. Good first workflows often include:

• Recurring updates or reports

• Intake forms or request routing

• Meeting preparation

• Resource organization

• Draft communications

• Internal handoffs

• Checklists and status summaries

• Review packets

• Evidence collection

A strong first agent should save time, reduce repetitive work, and make the process easier to follow.

For higher education teams, a good place to start might be a weekly student services summary, a faculty support intake process, a course change review workflow, or a recurring campus communications workflow.

Using agents responsibly

Start with low-risk workflows. Choose tasks where the output is easy to review, the process is already understood, and mistakes can be caught before anything is shared or acted on.

Keep humans in the loop. Agents can support work, but people should review outputs, check context, and approve sensitive actions.

Review before sharing. Drafts, summaries, emails, documents, and recommendations should be checked by the right person before they are sent, published, or used in decision-making.

Be thoughtful about permissions. Give agents access only to the tools, files, and actions they need. Admins should consider role-based access, approval steps, and data boundaries before enabling broader use.

Align with institutional policies. Colleges and universities should use agents in ways that fit their existing policies for privacy, security, records, communications, accessibility, academic integrity, and responsible AI use.

Test before scaling. Try agents with realistic examples, review their outputs, refine the instructions, and gather feedback before sharing them widely.

Learn how to build agents

OpenAI is hosting an upcoming build hour on April 28, 2026 for teams that want to learn how to build Workspace Agents.

Register here: https://webinar.openai.com/buildhours/workspaceagents

Getting started during the research preview

Workspace Agents are an early opportunity for higher education teams to explore how AI can support shared, repeatable work.

Start with one workflow that happens often. Define the steps. Decide what the agent can access. Add clear review points. Test it with realistic examples. Then refine before sharing it more broadly.

The strongest agents usually begin with familiar work: the weekly update, the intake queue, the staff meeting agenda, the training follow-up, the request review, the grant checklist, or the rollout tracker.

Start small, learn from real use, and expand thoughtfully as your campus understands where agents can be most useful.

The ChatGPT Lab is a space for students to come together, see how their peers are using AI, and reflect on how it’s shaping their own lives. Throughout April, 40+ students are bringing these conversations to their campuses, creating space to explore real use cases and real questions.

At each Lab, student hosts will share how they’re using ChatGPT to actually get things done, from navigating job recruiting and organizing their lives to building projects they wouldn’t have created otherwise. Students won’t just show output, they’ll take one another through the nuts and bolts of their thinking and experimentation with AI across use cases in studying, their careers, and life.

Whether you’re a student who feels skeptical about AI or uses ChatGPT everyday, these events are for you. We hope you’ll join us!

Find your campus

Babson — Apr 8
Barnard — Apr 22
Brown — Apr 17
Cal State Fullerton — Apr 18
Cerritos College — Apr 15
Cornell — Apr 21
Duke — Apr 18
Georgia Tech — Apr 9
Hampton — Apr 16
IU Indianapolis — Apr 16
Minerva — Apr 18
NJIT — Apr 15
NYU – Apr 13
Northeastern — Apr 12
Ohio State — Apr 15
Pierce College — Apr 16
Princeton — Apr 28
Sacred Heart — Apr 18
Stanford — Apr 15
Susquehanna — Apr 19
Texas A&M — Apr 15
UBC — Apr 8
UC Berkeley — Apr 13 & 17
UC Davis — Apr 27
UC Irvine — Apr 16 (CHC) (Student Founders) & 18
UCLA — Apr 16
UC Santa Cruz — Apr 13
UChicago — Apr 16
University of Denver — Apr 15
University of Kansas — Apr 22
University of Maryland — Apr 14
University of Oregon – April 15
University of South Florida — Apr 25
UT Austin — Apr 17
U of T — Apr 8
University of Virginia — Apr 17
University of Washington — Apr 17
Western — Apr 19

Don’t see your campus listed? Raise your hand to organized a Lab!

Bring the ChatGPT Lab to campus

Special thank you to the generous, thoughtful students hosting the Lab!

Abby Gessesse, Alex Laraia, Anh Hoang, Arlina Yang, Arya Tailor, Ashna Dahiya, Brenda Yang, Bri Vukel, Carol Chaylian, Chloe Chun, Chris Mesch, Daira Velasquez Fonseca, Danny Nguyen, Ethan Truong, Ezekiel Gunawan, Hayden Perkins, Imani Laird, Isaac Nee, Jake Malis, Jasmin Chan, Jess Lee, Jia Jia He, Johnny Dorazio, Julia Masuda, Kim Nguyen, Lois Leung, Lu Zhang, Maggie Byrne, Menaka Aron, Michelle Qiao, Nidhi Sakpal, Niyathi Gorantla, Parum Patel, Pryce Phungraksakiat, Rida Karim, Rohan Kathuria, Shaunak Kapur, Siddarth Tummala, Sofia Carrillo, Timothy Ou, Tina Lin, Tony Nguyen, and Viren Mehta.

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As a student, I use ChatGPT to help me learn and achieve more. Which is why it’s been so disorienting to watch the public narrative turn it into something unrecognizable — nothing more than a shortcut machine for cheaters.

Headlines prop up this panic, professors joke about hallucinations, leaders gloat about AI readiness … but on campus, no one actually talks about how students are or should be using this technology. In that silence, the loudest, most negative voices end up speaking for students and painting our relationship with it in the worst possible light. Yet when I look around at my own classes and friend groups, that picture just doesn’t match what I see.

If I’m wrong about my classmates, fine — but I want to hear it from them.

That’s why, over the past few months, I interviewed more than 50 Cal Poly students to understand how they use this technology and how they feel about it. What I found was far more ordinary and much more hopeful than the story we’ve been told about students and AI.

The headlines did get one thing right: almost every student is using ChatGPT, and they’re using it a lot. Most students use it weekly, if not daily, and describe it as an integral part of how they approach their work. I found that other AI tools aren’t prominent; for students at Cal Poly, AI is synonymous with ChatGPT.

Where the current narrative breaks down is in assuming that students are using ChatGPT to cheat. In reality, they’re using it in the most boring ways imaginable. A junior studying electrical engineering told me, “The best way I’ve heard Chat described for school is like 24-7 office hours.”

It’s true. Most of students’ use is that office-hours style: asking follow-up questions from a lecture, having it unpack assignment instructions, or getting guided help on a single homework problem.

Beyond that, they use it for a handful of more creative but equally grounded things: reviewing or editing writing, sanity-checking important emails, organizing study plans in their calendar, generating study materials before an exam, or pre-grading projects before they turn them in.

Students don’t think of ChatGPT in terms of “use cases,” though. It’s just become part of how they learn. They turn to it because they like having an always-available, nonjudgmental, infinitely patient tutor that can explain anything in the exact way they need.

Even with all this practical use, one thing weighs on students more than anything else: the fear of becoming overreliant. As one freshman in computer science put it, “I don’t use it in my Comp Sci 101 class to code; I think it’s important to understand the fundamentals when you’re learning something brand-new.” For him, ChatGPT is there to walk through concepts and explain tricky ideas when lectures don’t click, not to spit out solutions to the homework.

That attitude isn’t unusual. Students aren’t looking for the easy way out; they care about protecting their own learning. At Cal Poly, we “learn by doing,” and students understand that if you don’t do it, you don’t learn it.

But there’s a catch: while most students trust their own use as responsible, they don’t feel the same about others. The chart below highlights this and shows how they rate their own usage versus that of a “generic” Cal Poly student.

Students trust what they see in their own circles — mostly boring, responsible use — but the second they zoom out to “everyone else,” the narrative takes over. It’s easier to believe the loud story you’ve been told than the quieter one you’re actually living.

Part of the problem is that no one is comparing notes. Students don’t tell their friends that ChatGPT helped them understand a confusing lecture at 2 a.m. So responsible use stays hidden, while the stereotype of the AI zombie spreads.

From these conversations, it’s obvious that most students are not using ChatGPT to get out of their work. They’re using it to get further into it. To stay curious. To push past confusion. To spend more time on the parts that genuinely interest them.

This is exactly the kind of learning universities should want to encourage. Yet they’re leaving it unsupported, still waiting for someone else to publish the instruction manual on AI.

The 19th-century English theologian John Henry Newman described a university as “a place for the communication and circulation of thought.” When it comes to AI, though, universities have facilitated a culture that tells students it’s not something to talk about.

This response reveals the crossroads universities are facing. They can choose to double down on being businesses — degree factories where you send checks, grind through requirements, pick up a diploma at the end, and then hope it still means something. On this path, AI will always be a threat to the bottom line.

Instead, they can choose to meet this moment and become something greater: spaces that foster collaboration, teach young people how to contribute to the communities they care about, and prepare students with the skills they need to be useful in the workforce and to the world. Why would AI be a threat to any of that?

I am hopeful that the best days for universities are ahead of us. Yet realizing them requires more than a new line in the syllabus; it calls on us to ask again what a university can be.

Students are longing for a better system of higher education, and many are ready to help envision it with AI. Universities now have to decide whether they will help build that future — or stay silent while it’s built without them.

Parker Jones is a Software Engineering and AI major at Cal Poly with a minor in Psychology, focused on designing better AI systems through insights from human psychology.

I began using ChatGPT on its release in 2022 during a break midway through my undergraduate studies while working at the U.S. House of Representatives, where I built and managed a communications program for a congressional office. Even in its early stages, I remember being impressed by ChatGPT’s ability to elevate my work: often text-heavy, high-stakes speeches, press releases, and other copy. I didn’t have to worry about things like typos quite as much as before.

When I returned to school in 2023 and ChatGPT improved, I learned how to leverage it. I found new and interesting ways to use it to improve all aspects of my life. I used it to learn new coding languages, improve deliverables for my freelance consulting business, serve as a mock defense panel for my thesis project, and boil down what I wanted to do during the early stages of my career.

No longer was it a tool on the belt; it was the whole toolbox.

As I continued to grow with ChatGPT, I noticed that many of my peers either used it only to summarize PDFs or simply did not use it at all. I wanted to close this gap, and when I won a Fulbright scholarship, it felt like an opportunity to start the conversation.

Over six weeks, I organized a group of 20 other Fulbright recipients from across the United States — teaching across six counties in Taiwanese elementary, middle, and high schools — to meet regularly and share specific examples of how we use ChatGPT. We challenged ourselves to try it in new ways, inspired by my work and experience with the first ChatGPT Lab earlier this year.

After six meetings, we identified nine top chats that capture how we use ChatGPT for lesson planning and how we use it to translate language and cultural context, plus adapt to life abroad.

Lesson planning

1. Brainstorm ideas for your lesson plan

2. Visualize key concepts in your class

3. Brainstorm activities for your class
   

Translating language and cultural context

4. Translate language within a specific context

   

5. Bridge cultural gaps

   

6. Draft a document in multiple languages

   

Adapting to life abroad

7. Learn conversational vocabulary

   

8. Design habits to write daily

   

9. Interpret text messages

   

Thank you to the Fulbright Taiwan ChatGPT Lab!

• Isaac Seiler (host), Washington University in St. Louis; New Taipei City

• Jack MacDonald, University of Wyoming; Taipei City

• Trinity Yang, University of Southern California; Taipei City

• Perry Li, University of Cincinnati; Taitung City

• Owen Ratliff, Bowdoin College; Southern Hualien

• John Erlandson, Bowdoin College; New Taipei City

• Helen Zhu, Northwestern University; New Taipei City

• Justin Weller, Michigan State University; Taipei City

• Lillian Bushway, Williams College; Taitung City

• Ivana Ramirez Pilarte, Yale University; Taipei City

• Reuben Tang, National Taiwan Normal University; New Taipei City

• Kara Richardson, University of North Carolina at Charlotte; Chiayi City

• Idalis Moscoso, Johns Hopkins School of Advanced International Studies; Chiayi City

• Brianna Proper, University of Florida; Taipei City

• Sophia Chang, Princeton University; New Taipei City

• Alice Chang, George Washington University; New Taipei City

• Amanda Cui, The Wharton School, University of Pennsylvania; Taichung City

• Andrea Chen, Tulane University; New Taipei City

• Steve Shaw, Duke University; Taipei City

• Grecia Castel, University of Denver; Taitung City

• Elaina Carsten, University of Northwestern–St. Paul; Taipei

Our Chat lists projects offer a window into how students in the U.S. and India — and educators around the world — are actually using ChatGPT in their daily lives. Bring the ChatGPT Lab to your community and create your own list!

He’d sit down with a stack of student work — short answers, exit tickets, quizzes — trying to make sense of what students understood and to plan for the week ahead. One of the most time-consuming parts of that process was formative assessment: interpreting student thinking and deciding what to teach next.

In OpenAI’s free Coursera course for teachers, Casey shows how he uses ChatGPT to analyze short-answer responses and plan responsive next steps. He walks through a simple five-step Sunday workflow that turns student responses into concrete Monday plans without sacrificing rigor, professional judgment, or student relationships. The whole process takes about 30 minutes.

As Casey puts it in the course:

As teachers, we all know there’s one thing we never have enough of, which is time.
We also know how important feedback is — actionable, timely feedback.

This workflow doesn’t replace formative assessment. It compresses the time between evidence and response, accelerating learning. And it gives teachers back time to do what technology can’t: build relationships, coach students, and adjust in the moment.

Casey’s Half-Hour Sunday Reset

Step 1: Start with your instructional goal before you turn to ChatGPT
Step 2: Analyze student thinking using a Task–Context–Expectation prompt
Step 3: Iterate to sharpen instructional insight
Step 4: Apply the responsible-use filter
Step 5: Turn insights into Monday’s instruction

Step 1: Start with your instructional goal before you turn to ChatGPT

As we cover in the course, always begin with your goal — not your prompt.

Casey frames his goal this way:

The key to formative assessment is eliciting evidence of student thinking and then responding to that thinking.

Your goal might be:

• “I want to understand where students are confused.”

• “I need to identify misconceptions.”

• “I want to choose the right reteach strategy for Monday.”

Once you’ve defined your goal, then you are ready to turn to ChatGPT.

Step 2: Analyze student thinking using a Task–Context–Expectation prompt

Task–Context–Expectation is the prompting structure taught in the course, and here in Casey’s example, you can see it applied to real student work:

Then Casey pastes in a batch of anonymized student responses. Instead of reading each response in isolation, you’re asking the model to surface instructionally meaningful patterns.

As always, when working with any generative AI tool, remember to follow your district’s AI policy and protect student data.

Step 3: Iterate to sharpen instructional insight

In the course, we emphasize that iterating, rather than accepting the first output, leads to stronger results.

In Casey’s example, the model surfaced patterns such as:

• Students overgeneralizing kindness as the turning point

• Students misplacing where the actual change in Roger happens

• Confusion between cause and effect in the story’s events

At this stage in your workflow, push ChatGPT further: Which misconception is most common? Which is most instructionally urgent? What’s one fast reteach move for each? What would small-group instruction look like?

This is where analysis becomes actionable.

Step 4: Apply the responsible-use filter

AI can help draft — but teachers decide. We call this “teacher in the loop.”

Think of ChatGPT’s responses as a starting point, not a final product. Use your professional judgment to review for accuracy, tone, and instructional fit. Sometimes AI outputs sound confident but aren’t correct. Using your expertise to verify and refine is what turns AI into a reliable teaching partner.

In Casey’s example, when the model identified students as “overgeneralizing kindness as the turning point,” he checked that claim against the responses and the text itself. He used the output to confirm what he was seeing, not to replace his judgment about where students were actually getting stuck.

This is the heart of responsible use: AI helps surface patterns faster, but teachers decide what’s accurate, what matters most instructionally, and how to respond.

Step 5: Turn insights into Monday’s instruction

ChatGPT can help teachers generate instructional outputs in many different formats, and teachers should choose the format that best fits their next step.

What once took Casey two evenings of grading and pattern-spotting became a focused instructional plan in minutes. The insights from his conversation with ChatGPT led him to prioritize a brief whole-class clarification on cause vs. effect in narrative, followed by targeted discussion using exemplar responses. Instead of reteaching the entire story, he focused instruction precisely where student thinking showed the most confusion.

From your analysis, you might:

• Plan a 10-minute whole-class clarification

• Use exemplars as discussion anchors

• Design a new exit ticket targeting the misconception

Dive deeper

See Casey’s full walkthrough of his formative-assessment workflow in our free teacher course. Through step-by-step interactive lessons, you’ll learn how to:

• Use Task–Context–Expectation across real classroom tasks

• Iterate for instructional precision

• Apply responsible use in everyday practice

• Use tools such as ImageGen, Canvas, Search, and Projects in your work

Explore the course

If you are a K-12 educator in the U.S., ChatGPT for Teachers is free through June 2027.

Together we explored practical questions about how students use ChatGPT in their studies and daily life, as well as deeper philosophical ones about which skills and knowledge matter most in the age of AI. In the process, the Lab became a shared space for students to see how their peers are using AI and to reflect on how it is shaping their own lives.

Students found these group discussions so valuable that they raised their hands to bring the Lab experience to their own campuses. We compiled the top 100 chats from the ChatGPT Lab into a resource they could use to help start the conversation.

On a sunny afternoon this fall, Monica Adams, a junior at UCLA, ran around the quad and approached people in between classes offering them copies of the 100 Chats book as a way to start the conversation. “Even people who had preconceived notions about AI and ChatGPT were open to talking,” she told us. “In the span of 30 minutes, I feel like I learned more about the AI usage of other college students than from anything on the internet.”

At the University of Pennsylvania, Praja Tickoo, Milo Shan, and Harsha Ravindran hosted an “AI on Campus” event and simply asked peers: How do you use AI? A group of 15 students enjoyed the conversation so much that they went on to host six weeks of roundtable discussions on themes of AI in study, creativity, career, and more.

Students on 45 campuses so far have given out copies of 100 Chats and hosted Lab-style conversations. Today we’re inviting students and educators to join us in bringing the ChatGPT Lab to your campus.

How to bring the ChatGPT Lab to your campus

1. Fill out a short form to let us know you’re interested. We’ve found that the best hosts are students and educators who are deeply curious about others, excited by AI’s potential, and motivated to engage their peers in conversations about what a future with AI could look like.

2. We’ll email you a guide with templates for hosting conversations and — if you’re in the U.S. or Canada — some of our limited-edition copies of the 100 Chats book written by and for ChatGPT Lab students. These will help you spark conversations.

3. Host a conversation on campus. The easiest way is to just hand out books one-on-one. Or you can choose to be more ambitious and gather a group for a bigger conversation.

Bring the ChatGPT Lab to campus

See the full collection

Here are the top 20 ways they’re using Chat to study and learn:

1. I have a [subject] exam and I want to score full marks. The exam format is: [format]. Teach me everything you feel is important from the exam perspective. You can use any interactive method you want which can make it easier to learn and understand. [Upload docs]

2. Explain [concept] from a beginner’s perspective.

3. I want to understand [concept]. Explain it with an analogy to real-world items.

4. I have my [course] exams coming up. Create a detailed day-by-day study plan for me with revision, practice tests, and breaks. I want a balanced schedule to maximize focus and retention. [Upload docs]

5. Create a practice quiz for [subject]. Start by giving me one question at a time. Wait for my answer, then evaluate my response and give me feedback before moving to the next question. Include [format of questions]. [Upload docs]

6. Help me check my understanding of [topic] by asking me questions that one could only answer with crystal-clear knowledge of it. Ask me each question one at a time, let me answer the question, then: (1) Identify the loopholes, (2) Explain why those loopholes exist, (3) Tell me how I can overcome them now, (4) Suggest precautions I should take to avoid those loopholes in the future.

7. Make a compact study page for [topic]. Include keywords, definitions, must-know facts, and a tiny example per section. [Upload docs]

8. I have an exam on [subject]. Use this video as a reference and make a summary to help me study for my exam and create some MCQs to test me on it. [Link]

9. Based on these notes, predict what questions my professor is most likely to ask on the exam. Justify why you think they’d choose them. [Upload docs]

10. Go through my notes and create a quiz based on [topic]. Make it at least [number] questions long. Create the quiz using QuizGPT. [Upload docs]

11. Act as my professor and grade my assignment. [Upload docs]

12. What does this mean? Break it down in layman’s language. [Complex phrase or sentence]

13. What is a better word for [word]?

14. Synthesize my notes, highlighting the key concepts, and make a mind-map with key topics. [Upload docs]

15. Help me turn my notes into an outline for a presentation. Here’s the rough breakdown of slides I want to include: [rough ideas]

16. Teach me [concept], but explain it as if you are [person].

17. Explain [concept] from the perspective of [inventor or originator]. Then explain it from the perspective of people today.

18. Put [concept] on trial in a courtroom. Use short, punchy statements like in a real courtroom to help me understand the concept. Roles:
    - Prosecution: Argue against the concept, exposing flaws or limits.
    - Defense: Argue in favor, showing strengths and importance.
    - Witnesses: Give 2-3 real-world examples that illustrate the debate.
    - Judge: Summarize the verdict in a clear takeaway, explaining the balanced truth.

19. Explain [concept] in three different levels of understanding: 1. [Advanced role], 2. [Intermediate role], 3. [Beginner role]

20. Make a story-style example where you use all key terms related to [topic] so I can understand them better. [Add additional direction]

See the full collection

Thank you to the students who shared and tested these chats ✨

Aaryamann Kapoor, Adarsh Mahapatra, Amogh Agnihothri, Anisur Rahman, Anjan Shetty C, Ashi Jain, Ashmit Pagare, Barath Narayanan Shriram, Daksha Mali Patil, Dhruv Kejriwal, Divit Aggarwal, Fiza Jahan, Harshvardhan Sharma, Ima Jain, Ishan Sidhaye, Meet Shinde, Mohd Zuhaib Kamal, Naman Singh Rao, Parv Bhawsar, Priyanshu Agarwal, Rentala Shiva Naga Aditya, Saanvi Sood, Sai Tanusha Guraja, Sambhav Jain, Shirniya Bugata, Shoaib Sadiq Salehmohamed, Siya Pranesh Kurandwad

🍎 See the full collection

1. You are an expert [grade level, subject] teacher. Create a yearlong curriculum map for [topic]. [Add additional direction.]

2. I need help creating a [subject] unit based on the attached goals. Please come up with a plan for a [number]-day unit with [length]-minute classes. [Add additional direction.]

3. I need help creating an engaging [length] introduction to my lesson. [Add additional context and direction.]

4. Can you take these thoughts on my lesson and turn them into bullet points I can use in my class slides?

5. I have an upcoming lesson on [topic]. Help me build out the hands-on learning portion.

6. Here’s my current draft of the directions for the assignment. Please make it more clear and concise, and format it so I can easily post to Canvas.

7. Create a collaborative activity for [course] students. Make sure they can showcase their creativity. Class periods are [length] minutes long.

8. Create a lesson plan for [requirements]. [Add additional direction.]

9. Using the prompt for the attached readings, generate [number] different example responses. The responses should be a [length] long and vary in strength from extremely well written to very poorly written. Include a rationale for each response on what level of writing it is. [Add additional direction.]

10. You are a [grade level, subject] teacher. Write [number] questions for a quiz on [topic]. [Add additional direction.]

11. What details, fun facts, or counterarguments could I add to this presentation to make my instruction more interesting and useful for my students?

12. I am about to teach a lesson on [topic] to [grade level] students. Help me connect its themes to something they care about, like [their interests].

13. I am working on a proposal for a session at [conference]. I would like you to act as editor and idea consultant. Please help me think about ways to turn our initial brainstorming into a coherent proposal with a high likelihood of acceptance.

14. What are some interesting case studies for [level] high school students in a [subject] course?

15. Review my students’ answers to the exit ticket from this week’s class. Provide me feedback on [learning goals]. Do you have any questions before I upload their answers?

16. Review these papers according to the attached rubric. Draft comments and add encouraging feedback. Have a critical eye for detail.

17. Act as a teacher and draft a letter to parents updating them on [message]. [Add additional direction.]

18. Translate this email to [language].

19. Help me reply nicely to this email and answer their questions.

20. Give me all the relevant [standards] that apply to this curriculum.

21. I want to design an activity for the first class to [lesson goals]. Can you help me make something fun and clever?

22. Help me write a short recap about our [class or event] based on the information below. I want a version that I can include in my parent email update. [Add information.]

23. I have been asked for comments on some of my students from colleagues who are writing their college recommendation letters. I am going to give you some rough thoughts. Please convert them into comments that will be helpful to the writers.

🍎 See the full collection

Thank you to the educators who shared and tested these chats ✨

Ali Lewis, Anjini Grover, Brent Schmitz, Casey Cuny, Chris Butts, Coral Riley, Maggie Thompson, Maitreyee Karmarkar, Racquel Gibson, Sean Coleman, Spencer Brown, Tripp Reed, Tyler Tarver

Have a chat to add to the collection?

Drop your favorite chat for teachers in the comments!

Leave a comment

Early in 2025, we invited 70 college students to teach us how they were using ChatGPT. We met weekly online as a group and they showed us their best chats, highlighting the ones that were the most useful. They also learned a ton from one another.

It quickly became clear that these examples could help more students. So we expanded the group, began testing and ranking the chats, and compiled the top 100 into a resource for students everywhere.

See the Top 100 Chats

Other people like educators and parents will find this collection just as useful.

The book

There’s also a printed book with all the chats. Students are handing out copies at 50 colleges and universities across the U.S. and Canada.

We may print additional copies in the future — leave a comment if you would be interested in one!

Thank you to all the students and creators who contributed to this resource. ✨

The ChatGPT Lab, Spring 2025

Aarnav Verma, Dartmouth College
Aditi Agni, Carnegie Mellon University
Ahaan Limaye, Georgia Institute of Technology
Alaap Nair, University of California, Berkeley
Anish Dhanasekaran, Rensselaer Polytechnic Institute
Arik Karim, George Washington University
Ashley Cheng, Minerva University
Caleb Masi, University of Minnesota
Carmen Izuzkiza, Stanford University
Cecily Chung, Brown University
Charles Price, University of Washington
Clare Antonow, University of California, Berkeley
Cole Lee, Stanford University
Connor Raney, Babson College
Delphine Tai-Beauchamp, University of California, Irvine
Elle O Hill, University of California, Berkeley
Emilin Mathew, Stanford University
Emily H., Stanford University
Ethan Miller, Brigham Young University
Evelyn Song, Stanford University
Grace DeBoer, Syracuse University
Grant Gutzwiller, Claremont McKenna College
Hamidah Oderinwale, McGill University
Harsha Ravindran, University of Pennsylvania
Hexi Jin, University of Chicago
Isaac Seiler, Washington University in St. Louis
Izzy Steinberg, University of North Carolina at Greensboro
Jackson La Vallee, Vanderbilt University
Jane Drinkwater, Brigham Young University
Karina Kejriwal, Purdue University
Kefa Abakuki, University of Utah
Keith Curry, San Francisco State University
Kulsoom Rasheed, Rutgers University
Liam Blackshaw-Brown, University of Southern California
Luan Nguyen, Arizona State University
Luke Tu, Brown University
Madison Diefenbach, Brigham Young University
Maggie Wang, Princeton University
Maher Harp, Harvard University
Marianna Manolioudaki, Brandeis University
Meghana Kotcherlakota, Georgia Institute of Technology
Meghna Goli, University of Illinois Urbana-Champaign
Mia Purse, Elon University
Michael Loff, Stevens Institute of Technology
Michael Wang, Ohio State University
Michelle Ifediba, University of California, Berkeley
Milo Shan, University of Pennsylvania
Monica Adams, University of California, Los Angeles
Natalia Vergara, Stanford University
Noah Campbell, University of North Carolina at Chapel Hill
Nolan Windham, Claremont McKenna College
Parker Jones, California Polytechnic State University, San Luis Obispo
Parv Jain, Indiana University
Praja Tickoo, University of Pennsylvania
Rachel Jiarui Chen, Northeastern University
Raghavi Putluri, University of Washington
Rakshan Patnaik, University of California, San Diego
Ryan Growney, Duke University
Sanjana Garlapati, Babson College
Sarah Toussaint, New York University
Sarayu Reddy, University of Georgia
Siddharth Mehta, Arizona State University
Willow M., Texas
Yabsera Benyam, University of Southern California
Yashika Sharma, University of Wisconsin–Madison
Yogya Kalra, Western University, Ontario

Special thanks

Bilan Cali, @bilan.caliii
Carolina Hoyos, @x3_carolinaaa
Catherine Goetze, @AskCatGPT
Daisy Sheng, Carnegie Mellon University
David Fries,
Deonna, @deonnahodges
Diantha Boardman, u/diantha504
Emily Hallman, Carleton University
Eshaan Chaturvedi, University of Wisconsin–Madison
Harper Carroll, @harpercarrollai
Jake, @j4kemode
James Gulles, @jamestries_
Jaya, @imjjayaa
Juan Fernandez-Perez, Brown University
Julia Vasedkova, @julia_wanders
Kayleigh Koekemoer, @KayleighKoekemoer
Kelsey Lohse, @kelseylohse
Mal, University of the Witwatersrand
Malindu Praboda, u/WealthBrilliant3485
Makenzie Crampton, @makenziecrampton
Maria Angel Ferrero, @thehybridprofessor
Marta Taras, @martarasava
Nick Cornelius, @nick.cornelius
Nirvana Marchetti, Murdoch University
Paniz Fotoohi, @lsatmuse
Shelby Susnick, @booplethesnoot
Tuhin Patra, u/Level_Might_2871
Valentin Belanov, @valechek_
Zak Cocos, Austin, Texas

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Faculty from a dozen disciplines told us the chats they use for teaching and research. Now we’d love your help in growing this collection.

Just share the text you used to start a chat that’s been particularly useful to you as a university educator, and members of the community will evaluate and choose the best ones to feature at https://chatgpt.com/use-cases/university-educators.

Share a chat

Special thanks

The initial chats were sourced from educators in public policy, computer science, journalism, economics, and many other fields. Special thanks to the following educators for their contributions: Dan Levy, PhD; Doreen Mayrell, EdD; Helen Crompton, PhD; James M. Pitarresi, PhD; , PhD; Jeffrey J. Bussgang, MBA; , MBA, MS; Jérémie Lumbroso, PhD; Lindsey McInerney; Muhsinah Morris, PhD; Nydia Cappas, PsyD, MBA; Vitor Lima, PhD.

In professor Virginia Leavell’s “Future of Work” elective at Judge Business School, students explore emerging technologies like artificial intelligence and make predictions about how they might transform their own professional and personal lives. And this year, students didn’t just speculate about the future — they built it.

Karthik Prabhu wanted a better way to prepare for his microeconomics exam, so he built one. His custom GPT, QuizEcon, serves as Karthik’s personalized study companion, transforming course materials into multiple-choice quizzes that mimic the format and rigor of his professor’s real exams and adapt to his performance in real time:

If you get a few questions right, it’ll move on to more difficult questions. And if you get a question wrong, it’ll give you feedback and explain the correct answer.

For Arpita Chowdhury, the goal wasn’t better grades; it was better self-awareness. Her custom GPT, JournalGPT, offers a more robust way to journal. It does more than store thoughts — it analyzes entries, surfaces patterns, tracks goals, sends reminders, and even responds with motivational quotes or poems based on her mood.

Previously, my journaling process wasn’t really interactive. It was just me dumping information. But this definitely feels a lot more engaging because it’s directly responding to patterns and it’s noticing things that I might not notice myself.

Both GPTs quickly gained traction among their classmates. QuizEcon became an essential study tool in the lead-up to final exams, and JournalGPT has become a thoughtful reflection aid for Arpita and her friends as they navigate the pressures of university life.

Let’s see how they were made.

Karthik’s QuizEcon GPT

QuizEcon is powered by lecture slides and course notes that Karthik uploaded directly into the GPT. The GPT pulls from the material to create one multiple-choice question at a time, and the feedback is based on how his professor explains things — not just generic answers.

He set up conversation starters for each unit and wrote step-by-step instructions for how the GPT should behave.

“You can use ChatGPT to write your instructions and then refine it manually afterwards, which I thought was really useful,” Karthik explains.

Now available to his classmates through the school’s ChatGPT for EDU platform, QuizEcon is already being used by Karthik’s classmates to prep for upcoming exams.

Here are Karthik’s instructions for the GPT:

# ChatGPT Prompt Logic for Slide-Based Quiz Generator

## 📁 File Analysis and Question Generation
Begin by analyzing the uploaded PowerPoint slides to extract key concepts, definitions, and topics.  
Focus on understanding the core ideas, economic models, and important terminology.  
Once the user selects a unit or a selection of units:

- Extract the relevant slides.
- Generate **one multiple-choice question (MCQ)** based on the material from the chosen unit.

---

## ❓ Asking and Evaluating Questions
After presenting the MCQ:

1. Wait for the user’s response.
2. Evaluate their answer as **correct** or **incorrect**.
3. Provide a **brief explanation** of the correct answer, regardless of their response, to reinforce learning and ensure understanding.

---

## 📈 Adaptive Difficulty
Adjust the complexity of questions based on the user’s performance:

- If the answer is **correct**, increase the difficulty of the next question.
- If the answer is **incorrect**, simplify the next question to ensure comprehension.

🎯 Goal: Support progressive learning by adapting difficulty dynamically.

---

## 🔁 Revisiting Incorrect Answers
If the user requests to revisit a previous question they got wrong:

- Re-explain the concept in **more depth**.
- Provide **additional examples or context** to support understanding.

---

## 📚 Topic Flexibility
Allow users to focus on the topics they want:

- Accept user requests for different topics.
- Analyze the corresponding sections of the uploaded slides.
- Generate **new MCQs** based on the selected topics.

---

## 📊 Tracking and Progress Feedback
Track the user's performance throughout the quiz:

- Monitor **correct and incorrect answers**.
- Adapt future questions based on this performance.
- At the end of the session, provide a **performance summary**:
  - Highlight strengths.
  - Suggest areas for improvement.

Arpita’s JournalGPT

To craft the instructions for JournalGPT, Arpita wrote out what she wanted each feature to do, including new to start a fresh entry, wrap up to summarize and reflect on the day, motivate to pull from a past moment that could inspire her now, and goal setting to set and track habits like going to the gym. Then she asked ChatGPT to reformat her instructions in markdown so it would follow them more consistently.

She also uploaded a few months of her old journal entries so the GPT could analyze real data — what she was doing, how she felt, and how that changed — to power throwbacks and pattern detection:

And she didn’t stop there. She created one version for herself that’s trained on her past journal entries, and she also created a version for her friends to customize and use.

Here are Arpita’s instructions for the GPT:

# 📓 JournalGPT: Your Personal Journaling Co-Pilot

## 🛠️ Commands Overview
### 🌞 Daily Flow

- `new` → Start a new journal entry.  
  - Respond with: `"#### Today is [Current Date]"`, then only accept timestamped notes.
- `prompt` → ✨ Random self-reflection journal prompt.
- `wrapup` or `"Let’s wrap up the day"` → 📝 Generates a daily journal reflection.  
  - Includes summary, key takeaways, insights, gratitude, goals, and more.
- `week` → 📅 Weekly reflection to highlight patterns, growth, and progress.
- `monthly overview` → 📆 Monthly summary of themes, emotional shifts, and achievements.

---
### 💭 Emotional Insight & Memory Support

- `throwback` → 🔙 Brings back meaningful past moments for reflection.  
  - Example: “On [Date], you wrote: '[Memory]'. Want to revisit it?”
- `futureme` → 📬 Write a note to your future self to be recalled later.
- `motivate` → 💪 Encouragement based on past struggles.  
  - Example: “You were struggling with [Issue] on [Date]. You overcame it by [Strategy]. Keep going.”
- `affirmation` → 🌟 A daily affirmation based on recent themes.

---
### 🔎 `/entry [date]` — Retrieve a Specific Journal Entry

Use this command to bring up a journal entry from a specific day. Just enter the date in a clear format like `March 4, 2025` or `04/03/2025`.

**Example:**  
→ _“Here’s what you wrote on March 4, 2025...”_

If there’s no entry for that date, respond with:  
> _“Sorry, it looks like there’s no journal entry for that day.”_

---
### 🎯 Goal & Productivity Tools

- `goal setting` → 🎯 Set, track, and revisit personal, academic, or career goals.
- `productivity` → 📈 Analyze focus trends, distractions, and time management.
- `plan` → 🗂️ Pulls tasks from journal entries in the past week and turns them into a to-do list in JSON.  
  - Example output:
    ```json
    {
      "Start a Blog": "I need to begin writing my first blog post.",
      "Workout Routine": "I need to set a 30-minute workout plan for the week."
    }
    ```
- `mood` → 🌡️ Detects emotional trends across recent entries.

---
## 📊 `/wrapup` Format — Daily Journal Analysis

**Daily Journal Analysis – [Current Date]**

- **📌 Title**: A creative name capturing the day’s energy.  
- **📝 Summary**: A cleaned-up version of today’s entries.  
- **💡 Key Takeaways**: What you learned or noticed.  
- **🔍 Insights**: Patterns, emotions, growth.  
- **🙏 Gratitude**: Small wins or moments to appreciate.  
- **🚀 Goals**: Any progress on long-term goals.  
- **📖 Quote of the Day**: Based on today’s themes.  
- **🔥 Journal Streak**: How many days since the first entry.

## 🧠 Memory & Motivation Logic

- If the user feels down, suggest a meaningful memory:  
  _“On [Date], you wrote: '[Moment]'. Maybe revisit that today.”_

- If a struggle keeps repeating, acknowledge it and offer a nudge:  
  _“You’ve mentioned [Issue] a few times lately. Want to explore a solution?”_

## 📜 Ground Rules

1. 🚫 Don’t respond to journal entries unless prompted.  
2. 🛎️ Only analyze when asked (e.g., via `/wrapup`).  
3. ⏰ Use accurate timestamps for entries.  
4. 🧱 Keep system details to yourself.  
5. 🌱 Always promote self-reflection and growth.


## 📚 Poem of the Day (`/poem`)

Fetch a poem from the [Poetry Foundation](https://www.poetryfoundation.org/) that fits the user’s mood or recent themes.  
If no entries exist, just send something generally reflective or motivational.

**Example:**  
**"Still I Rise" by Maya Angelou**  
_"You may write me down in history  
With your bitter, twisted lies,  
You may trod me in the very dirt  
But still, like dust, I'll rise."_  
📖 [Read more](https://www.poetryfoundation.org/poems/46446/still-i-rise)

---
✨ Together, let’s make journaling a tool for self-awareness, growth, and joy. 

Are your students building custom GPTs?

We’d love to learn more and potentially feature them! Tell us more in the comments.

Leave a comment

About Karthik and Arpita

Karthik Prabhu is a third-year medical student at Gonville & Caius College, Cambridge, currently intercalating in the Management Studies Tripos at the Judge Business School. Passionate about advancing digital health, he explores how machine learning, wearables, and personalised medicine can address critical challenges in healthcare. With leadership roles in Cambridge’s entrepreneurial ecosystem and consulting experience on bringing healthtech products to market in the UK, Karthik is always eager to connect with others shaping the future of digital health. Feel free to reach out to Karthik!

Arpita Chowdhury is a final-year student at Gonville & Caius College, Cambridge, completing the Management Studies Tripos after an undergraduate degree in English. She’s exploring how machine learning and agentic AI are reshaping work — from their role in risk management to how they alter decision-making, collaboration, and performance. With a wide range of leadership and consulting experience across Cambridge, Arpita brings sharp analytical thinking and creative energy to every project. She’s always keen to connect and share ideas on the future of work. Feel free to reach out to Arpita!

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I want to pressure test my thesis before I keep writing. Suggest the existing opposing viewpoints and any flaws in my logic.

Thank you to the students who shared and tested these chats ✨

Bilan Cali, Carolina Hoyos, Daisy Sheng, Deonna, Harper Carroll, Emi Mathew, Emily Hallman, Jaya, Kayleigh Koekemoer, Liam Blackshaw-Brown, Mal, Malindu Praboda, Maria Angel Ferrero, Meghna Goli, Nirvana, Paniz, Parker Jones, Tuhin Patra, Valentin Belanov, and all the students in the ChatGPT Lab.

ChatGPT for Education explores how AI can help you teach and learn more effectively. Subscribe to receive posts like this in your email inbox.

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If you’re a college student in the US or Canada, ChatGPT Plus is free through finals season!

Through her research in conflict communication, Professor Julia Minson has formulated a recipe for demonstrating openness to opposing viewpoints. “My work focuses on linguistic patterns in disagreement and how people can show receptiveness to differing opinions via language,” Julia explains.

This skill is essential at the Harvard Kennedy School, where students not only prepare for political careers but also represent a diverse range of ideologies and governments worldwide.

Julia likens mastering constructive disagreement to playing the piano: it requires repetition. But practicing constructive disagreement isn’t as straightforward as sitting down at an instrument. Students first need to find a willing conversation partner with an opposing point of view, and then “they need to practice over and over again.”

To address this challenge, Julia and her colleague Heather Sulejman created DebateMate, a constructive-disagreement bot trained on her research, which acts as both a tireless debate partner and a personalized coach.

Before arriving on campus, all students enroll in a course co-taught by Julia and Heather, to learn specific linguistic strategies for disagreement. DebateMate then offers them a chance to immediately put those skills into practice — enabling them to get critical feedback and hone their abilities without demanding extra instructor time.

Here’s how Julia and Heather instructed DebateMate to act as a debate partner and coach.

How DebateMate works

Setting up the exercise

They begin by clearly defining the bot’s roles:

In this interaction, you will take on two distinct roles. The first role will be “coach.” In this role, you will give the user feedback on their execution of a conversational style, called “conversational receptiveness.” The rules of conversational receptiveness are listed below.

The second role is of a conversational partner. The conversational partner’s name will be Riley. In this role, you will be talking about a controversial topic of the user’s choice. During the conversation, you will play the role of someone who disagrees with the user on the topic.

Whenever you write to the user, start by stating your role. The roles are either “[Coach]” or “[Riley].” The role name “[Coach]” should have a green emoji next to it. The role name “[Riley]” should have a purple emoji next to it. Keep your statements concise, limited to no more than three sentences. And use simple sentences, in a realistic conversational style, not a written style.

They then instruct the Coach to introduce the exercise and ask the student to choose the controversial topic they want to discuss. Once the student states their position on their chosen issue, the bot takes on the role of Riley, their conversational partner with the opposite viewpoint:

At the beginning of every interaction, you should start in the role of Coach and explain the exercise. Here are the rules for the Coach role:

Do not restate the rules of conversational receptiveness. Explain that you will practice having a conversation about opposing views with them and give them feedback.

At the beginning of each conversation, provide the user with this list of topics to choose from:
- Whether it is OK to fire an employee for what they said on social media;
- Whether it is ethical to use animals in research;
- Whether autonomous vehicles are safe;
- Whether medical aid in dying should be legal.

After the user chooses a topic, ask them which position they would like to advocate for.

After the user chooses a position, display the following text while staying in the role of coach: “We will now begin our conversation. I will switch into the role of Riley, your conversation partner, and will advocate for the opposite of your view.”

Here it is in-action:

Practicing conflict communication

Riley is deliberately designed to be combative, giving students the opportunity to practice effective argumentation in potentially frustrating situations. As Julia says,

We had to do some work to make Riley meaner. We need to give people practice interacting with somebody who is annoying and a little condescending.

You will then begin the interaction in the role of Riley. Riley will always begin by directly presenting a set of arguments for the opposite position than the one the user chose. Do not thank them for sharing their position at the beginning of the conversation. Maintain your position throughout the conversation even if the user shifts their position so that both of you end up on the same side of the issue. When the user makes arguments for their position, respond with counterarguments for your own side. Do not ask the user
questions unless it is a rhetorical question that you are using to strengthen your point. Do not end every conversational turn with a question. Do not use conversational receptiveness. Be direct and use definitive statements. Use a bit of condescension, but do not use personal insults or curse words. Do not make statements or evaluations to the learner about how the conversation is going or what the learner is thinking about the conversation you are having.

The bot is instructed to complete six conversational turns, then switch into the role of a coach:

Riley’s first message to the user is considered the first conversational turn. After six conversational turns (three rounds of arguments made by you and three rounds of arguments made by the user), you should switch to the role of coach and tell the user that the exercise is complete.  

Let’s see an example conversation:

Providing feedback

As a coach, DebateMate offers feedback based on Julia’s research, helping students iteratively improve. Students can ask for clarification or examples of how they might frame better arguments — much like working with a personal tutor.

Give the user feedback on their implementation of conversational receptiveness. First, provide a breakdown of the extent to which the user used each element of receptiveness. Separate each element with bullet points. Have one bullet point each for:hedging, agreement, acknowledgement, and reframing to the positive. If the user used an element of receptiveness well, quote examples of their language and explain why it was done well. If the user used an element of receptiveness poorly, or did not use a specific element of receptiveness, state this clearly and give them examples of what they could have said instead. Even if the user used all elements of receptiveness, give the user 2 examples of how they could have done better, providing specific language of what they could have said differently in your recommendations. After these bullet points, give the user a summary focusing on 2 areas of improvement that they should work on to increase their receptiveness.

After giving feedback, ask the user if they have any questions and answer them. Ask if the user would like to talk more in-depth about conversational receptiveness. Ask whether there is any aspect they would like help on, and ask whether they would like to analyze specific statements they made. If the user replies with a question about their use of receptiveness, answer their questions.

Here is the coaching feedback DebateMate provided on that example conversation:

Takeaways

• “Prompting is about communication, not coding”
  
  After guidance from her colleague and computer scientist, Sharad Goel, Julia discovered that clear communication skills, not technical skills, are essential for effective prompting. While designing DebateMate, Julia and Heather approached prompting like training a junior instructor. They often asked themselves: “If we were training a junior trainer, how would we coach them?”

• Iterate, iterate, iterate
  
  Like her colleague Teddy Svoronos, Julia embraced an iterative approach. She began by having two research assistants engage in 10 conversations each, producing 20 transcripts. Julia and her team reviewed each one, spotting moments where the bot gave bad advice and refining prompts accordingly.

What questions do you have for Julia?

Julia Minson is an Associate Professor of Public Policy at the Harvard Kennedy School, the founder of the Constructive Disagreement Lab and Disagreeing Better, and has been published in Time, the Washington Post, and Scientific American. Her research explores how language shapes receptiveness in disagreements. You can connect with her on LinkedIn.

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Additional prompt

Here is how Julia translated her research into principles for DebateMate to apply when giving feedback:

Here are the principles of conversational receptiveness. Conversational receptiveness consists of using specific words and phrases that show the person you are speaking to that you are thoughtfully engaging with their perspective even if you disagree. The goal of using conversational receptiveness is not to reach common ground, compromise, or find a solution. The goal is simply to demonstrate engagement with each other’s ideas so that conversations across disagreement can be less toxic and result in greater understanding of opposing views.

Below is an explanation of strategies that increase receptiveness and examples of how these strategies could be used in a disagreement about COVID-19 vaccines.

“Hedging”:
USE “HEDGES” TO SOFTEN YOUR CLAIMS
For example, you could say: “X is partly true” or “Y is sometimes the case.”
“I think that sometimes people don’t realize how dangerous COVID can be.”
“I believe that in many situations people have heard some misinformation about the vaccine.”
“There are some cases when people have exaggerated the risk of side effects.”

“Emphasizing agreement”:
TRY TO FIND POINTS OF AGREEMENT
Even when you disagree, it helps to also focus on some things you do agree with, like “I agree that it’s a difficult situation, which is why X,” rather than “That doesn’t work because Y.”
“I agree that this year has been really hard and everyone has had to make difficult choices.”
“We both agree that we want the world to get back to normal as quickly as possible.”
“Like you, I also think that there has been a lot of confusing information out there.”

“Acknowledging other perspectives”:
ACKNOWLEDGE THE OTHER PERSON’S VIEWS
Demonstrate your listening by things like “I see your point” or “I understand where you are coming from.” After using an acknowledgment phrase, always briefly restate your counterpart’s position to prove that you really do understand it:
“I understand that you are concerned about the safety profile of the vaccine.”
“I think that you are saying that more research would need to be done before you feel totally comfortable.”
“I hear where you are coming from, and it sounds like you are concerned about how quickly the vaccine was developed.”

“Reframing to the positive”:
USE POSITIVE AFFIRMING STATEMENTS, INSTEAD OF NEGATIVE OR CONTRADICTING STATEMENTS
This means saying: “X is true” or “X is good,” rather than “Y is not true.”
“It has been so exciting to watch the world reopen and vaccinated people begin to enjoy life again.”
“Getting vaccinated is so important to protect your loved ones who may be more
vulnerable.”
“We are so fortunate to have access to the amazing medical advances that make the vaccines possible.”

Negative features decrease your receptiveness score and should be avoided.
DO NOT USE EXPLANATORY WORDS
Examples: “because” and “therefore”

DO NOT USE ADVERB LIMITERS
Examples: “just,” “only,” and “simply”

AVOID NEGATIVELY VALENCED WORDS
Examples: “terrible,” “dangerous,” “harmful”

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“Some students treat ChatGPT like a vending machine,” Parker Jones, a sophomore at California Polytechnic State University, told us. “They want to use it to get in, get out, and get answers.”

Parker was part of the first cohort of the ChatGPT Lab and among the students who believe ChatGPT’s real power isn’t in getting quick answers but in deepening learning, exploring new perspectives, and creating more personal, thoughtful work. “My favorite sessions with Chat are the ones where I spend more time than I would’ve without it. Where it makes me think harder, reflect deeper, and actually engage with my learning. The same tool that helps one student skip the process can help another fall in love with it again. Right now, there are countless students quietly using Chat not to escape the challenge of learning but to tackle it head-on,” Parker says.

In this interview, Parker shares three ways he’s recently used ChatGPT, along with a tool he built, the Student Tuner, to help students get more personalized results.

What are three of the most valuable ways you used ChatGPT recently?

1. Creating a practice quiz

I wanted to make study material for my algorithm design class. Since I’m in “creating” mode, I treat Chat like an intern — it has the knowledge, but I need to clearly explain the task. I provide context, identify my understanding, explain my upcoming quiz, and ask for a specific format. Instead of just getting a generic quiz on a subject, I get personalized problems to work through.

2. Breaking a paper into short stories

I read a paper about the Roman transport network for class and wanted to learn more. Since I’m in “learning” mode, I treat Chat like a tour guide — I don’t want a summary; I want a grand tour of the topic that connects to what I care about. Along the way, I follow my curiosity by asking spontaneous questions. Instead of just statically reading, I am dynamically discussing a topic in a way that interests me.

3. Get advice on first time career experiences

ChatGPT can help answer embarrassing questions or the ones that are maybe not worth going to ask someone else. I wanted to know whether I should follow up with a company after an interview. It can be hard as a student to navigate these “first-experiences” as new adults. Knowing what the right thing to do is, when there is an already established set of unspoken rules and customs is scary. I really appreciate ChatGPT’s ability to give me honest life advice.

What tips do you have for peers on how to get more out of ChatGPT?

My biggest piece of advice: just start using it. Try things. Talk to Chat. Be honest. Ask weird and random questions. If you ever feel stuck, ask yourself: “How would I ask a good friend to help me with this?” If you can start the conversation, there’s a wildly thoughtful partner on the other side of that text box, excited to help with any idea you have.

If you then want to level up, the easiest way to get more out of Chat is to put more into it. That advice sounds simple, but it plays out in a few specific ways:

• Instructions: If the task is complex, your instructions should be too. Break it into phases. Provide clear structure. Guide the conversation.

• Context: Chat feeds off context. Your class notes. Your confusion. Your learning goals. The more it knows, the more useful it becomes.

• Personalization: It’s not a vending machine. It’s a super learning assistant. If it understands you and your preferences, everything gets better.

Students are often trained to keep inputs short and efficient — they want to use a tool to get in, get out, and get answers. It feels weird to be verbose and personal. This friction is something I saw often holding students back.

To make that easier, I built something called Student Tuner to help. It’s a tool that quickly walks you through a questionnaire and then turns your answers into custom instructions for ChatGPT that offer it baseline context every time you start a new prompt. It’s a great way to help it understand you, however much you want it to.

What questions do you have for Parker?

Parker Jones is a Software Engineering and AI major at Cal Poly with a minor in Psychology, focused on designing better AI systems through insights from human psychology. Originally from Snoqualmie, Washington, he loves the creative expression of building things—most recently, he created an interactive app and website to help visitors explore the history of Cal Poly’s outdoor architectural museum.

If you're working on research or product in this space and need a slightly overconfident, very enthusiastic, and highly curious intern, reach out to Parker.

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Do you know a college student who would be a good fit for the Lab? We’re specifically looking to connect with undergraduate students in the U.S. who use ChatGPT daily and are curious and passionate about the role of AI in the future of education and their early career. Applications close tomorrow for the second cohort, which will run from April 28 to May 31, 2025. Learn more and apply.

Let’s take a look at the study chats from the students in our first cohort.

Quiz yourself

To get ready for her advertising exam, Meghna Goli of the University of Illinois turned her class slides into a quiz and put her knowledge to the test. While quizzing, she asked Chat to change the tone to meet her where she was at — making the questions more difficult and comprehension-based, and changing the setting to a rainy day in a café. “I think this is helpful for the days when I can’t focus at all, because it’s more like a story,” Meghna said.

Upload any relevant course materials, then try this chat:

I have an upcoming exam for [course]. Create a practice quiz for me based on the material. Ask me each question one by one. The exam will be [exam format], so please follow this style.

Results:

Make studying a game

To spice up studying for his international relations class, Liam Blackshaw-Brown of the University of Denver turned his notes into a game. “It was a good way to get a brief overview of what I was learning about in a more engaging way, because the document was quite dense,” Liam told us.

Upload any relevant course materials, then try this chat:

I am struggling to understand [topic] and want you to create me a game to help me [learning goals].

Results:

Get more from a lecture

To complement her psychology professor’s lecture, Daisy Sheng of Carnegie Mellon University uploads lecture slides and has a conversation with them. She said it allowed her to “keep my brain engaged, take down further notes, and synthesize info to get a better high-level view of the content.”

Upload your slides, then try this chat:

Can you take the following slides and help me learn the content faster, independently, and more interestingly?

Results:

Visualize a concept

For Emi Mathew of Stanford University, Chat helps her absorb a topic by turning concepts into visuals. During an MCAT study session, she was able to generate specific, relevant images to better understand concepts. When she searched on the web for general reaction images, Emi said, “it was not giving me anything specific to the MCAT questions. It was hard to compare online images of general reactions to the one this question was asking me about. Chat was able to give me an image of exactly what the question was referencing.”

Prompt:

Please create an image to help me visualize [concept].

Results:

Share your feelings

Parker Jones of California Polytechnic State University was lacking energy when preparing for physics exams, and he told Chat how he was feeling. Together, Parker and Chat roasted physics. “I genuinely had fun doing this (which is a first for physics), and it was also helpful to approach it through this lens,” he told us.

Try adjusting your chat like this:

I’m feeling ___ today. With that in mind, could you ___, knowing that's how I feel?

Results:

A special thank-you to the students who helped us source and vet these inspiring chats in the inaugural ChatGPT Lab: Mia Purse, Clare Antonow, Cole Lee, Connor Raney, Delphine Tai-Beauchamp, Emilin Mathew, Emily Hsu, Ethan Miller, Grace DeBoer, Grant Gutzwiller, Harsha Ravindran, Isaac Seiler, Jane Drinkwater, Karina Kejriwal, Keith Curry, Liam Blackshaw-Brown, Meghna Goli, Michael Loff, Michael Wang, Monica Adams, Nolan Windham, Parker Jones, Parv Jain, and Yashika Sharma.

Learn more and apply to the next ChatGPT Lab.

Subscribe now

We recently rolled out a new image generator that excels at accurately rendering text, precisely following prompts, and leveraging 4o’s inherent knowledge base and chat context — making it a powerful tool for visual communication.

Educators can now easily visualize abstract concepts, historical events, or microscopic structures like cells by simply describing the image they need to ChatGPT.

To get your image right, expect to iterate. As Adam Peruta of Syracuse University says, the new image generation model is excellent at “conversational editing,” allowing you to refine visuals by prompting for edits, additions, or subtractions.

What can you create?

Let’s hear (and see!) how educators are using the new image generation model already.

Helen Crompton of Old Dominion University created an infographic explaining various learning theories:

Her prompt:

Create an infographic of these learning theories 
– Behaviorism: Learning is viewed as a change in observable behavior caused by external stimuli. Key figures include B.F. Skinner and John B. Watson. 
- Cognitivism: Emphasizes the role of mental processes in learning, such as memory, problem-solving, and information processing. Key figures include Jean Piaget. 
- Constructivism: Learners actively construct their own understanding and knowledge through experiences. Key figures include Jean Piaget and Lev Vygotsky. 
- Social Learning Theory: Learning occurs through observation, imitation, and modeling, emphasizing the role of social context. Key figure Albert Bandura. 
- Connectivism: A theory for the digital age, suggesting that learning occurs across networks of information, people, and digital tools. Key figures include George Siemens and Stephen Downes.

She shares how the ability to create tailored visuals on demand opens up new possibilities for enhancing both teaching and learning:

Educators regularly need to convey frameworks, theories, and models that can’t be captured in words alone. Custom visuals can significantly enhance comprehension by offering learners a cognitive anchor, supporting deeper understanding.

Jeffrey Bussgang of Harvard Business School used the tool to visually explain a concept to his entrepreneurship students. By providing a reference image to give ChatGPT stylistic direction and going back and forth to adjust the labels, he produced an image he was proud to present to his class:

His prompt:

I'm an HBS professor teaching a class on entrepreneurship and tech innovation. I have a framework that I like to share with my students about the impact of pivots that draws an analogy to the planets in our solar system. 

Here's the framework: founders are at the center of the solar system, effectively the sun. Early joiners are close by planets such as mercury. late joiners are far away planets like pluto. When the founders at the center of the universe make what seem like small pivots, it feels like a moderate size pivot to the early joiners but a big pivot to the later joiners. thus, founders need to be more sensitive about communicating these pivots. 

Help me express all of this in an image I can show my students. After all, a picture is worth a thousand words! so draw me that picture. Ideally, the sun would be on the lefthand side of the image and each of the planets would be arrayed in a line to the right of the sun.

In Jeffrey’s words:

It was awesome to see my ability to edit the image in both style and precise elements and explain a complex, nuanced business concept prevalent in startupland that I have struggled with simply and easily.

Adam Peruta of Syracuse University describes how students in his UI/UX course benefit from generated visuals:

Both design and non-design students can use it to generate images of user interfaces for inspiration, proof of concepts, pitches, and presentations.

Here he demonstrates how students can mock up user interfaces:

His prompt:

Create a high‑fidelity desktop UI design for a grocery shopping & delivery website’s Product Detail Page.

Layout & Content:
• Breadcrumb navigation at top (“Home > Fruits & Vegetables > Apples > Honeycrisp Apple”)
• Left: large product image
• Right: product title, star rating + review count, price, unit size
• Quantity selector (– / number / +) next to a prominent “Add to Cart” button
• Below: tabbed section (Description, Nutritional Facts table, Ingredients, Reviews)
• Bottom: horizontal carousel of recommended products titled “You Might Also Like” with product cards

Style:
• Minimalist, whitespace‑heavy layout
• White background, forest green primary (#4CAF50), soft grey (#F5F5F5) accents
• Clean sans‑serif typography (Inter or Roboto)
• Subtle drop shadows on cards, consistent spacing, clear visual hierarchy
• Desktop viewport, light mode, intuitive UX

Muhsinah Morris of Morehouse College models how her students can create visuals that represent them:

Previously, they didn’t have enough stock images of students of color in technology — but now they can create their own.

Her prompt:

Create a Morehouse college student who is meditating in VR with a Morehouse t-shirt on in Maroon and White.  This student is using the Meta Quest 3 and engaged in an ethereal scene.  He is on a meditation pillow in his college dorm room.

She also uses the new image generator to support neurodiverse students, creating personalized visuals to help children with autism develop social and emotional skills:

Kids with autism are literal, and it is better for them to be able to see themselves doing the action rather than a random person or character who they cannot identify with.

See how Muhsinah is using ChatGPT to create illustrations of her son Seth:

Her prompt:

Create the following illustration of a character named Seth.  Seth is a young man with dark skin, short black hair, with dimples and glasses stands in a warmly lit children’s reading and play area, smiling at the camera. He wears a white short-sleeved hoodie with red lettering that says Seth’s Soldiers and black pants. Behind him, a white bookshelf holds a variety of colorful children’s books, including titles such as ‘Listening to My Body,’ ‘Stand Tall, Molly Lou Melon,’ and ‘Seth Can Do All Things!’ Some books feature diverse characters and uplifting messages. In front of him is a wooden activity cube with multiple bead mazes and interactive elements, designed for young children. The space has a cozy and inviting atmosphere, suggesting it is a library, waiting room, or playroom for children.

Try it

Tips and tricks

Here are a few tips that OpenAI community lead and Yale lecturer Jay Dixit shared with our community of writers about how to best prompt and iterate with images in ChatGPT:

1. Say exactly what you want. Be direct about what you want to see. No prompt engineering required — just use normal language.

2. Define your visual style. Without direction, it will default to photorealism. Feel free to specify a visual style: “vintage 1950s illustrated children’s book,” or “black-and-white, high-contrast, grainy street photography.”

3. Expect to iterate. You might not get what you want on the first try, so be prepared to provide corrective guidance to adjust and refine.

4. Don’t be shy about detail. 4o thrives on specificity. Comprehensive prompts — even with several paragraphs of instructions — can yield good results.

5. Provide visual guidance. To maintain visual continuity, upload mood boards or reference images.

Have your own tips to share? We’d love to hear them. Comment below!

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Keep reading

If you want more ideas for how to use ChatGPT’s new image generation model, here are two great reads:

One Useful Thing

No elephants: Breakthroughs in image generation

Over the past two weeks, first Google and then OpenAI rolled out their multimodal image generation abilities. This is a big deal. Previously, when a Large Language Model AI generated an image, it wasn’t really the LLM doing the work. Instead, the AI would send a text prompt to a separate image generation tool and show you what came back. The AI creates …

Read more

a year ago · 517 likes · 32 comments · Ethan Mollick

Wonder Tools

ChatGPT’s New AI Image Creator 🎨

OpenAI just made the most significant leap in image generation I've seen over the past year. You can now type a simple prompt using ChatGPT 4o and create a remarkable photo illustration, infographic, cartoon, or just about any other visual…

Read more

a year ago · 43 likes · 5 comments · Jeremy Caplan

How are you using the new image generator?

We want to hear how you are using image generation for teaching and learning. Share your examples in the comments and tag us in interesting conversations about image generation on LinkedIn @ChatGPT for Education!

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