See credible examples
Start with a practical look at how OpenAI Research uses Codex in real research workflows.
Virtual faculty studio | Codex
OpenAI Codex Summer Studio for Faculty & Researchers
A four-week virtual studio for academics who want to use Codex on real academic work and build one useful example from their own materials.
Four 1-hour virtual sessions | July 29 - August 19
Why join
Learn by building from your own academic work.
Choose a track
Work alongside faculty focused on teaching, research and code, or lab and department workflows.
Use the Build Kit
Get starter workflows, prompts, example inputs, review checks, and before-and-after examples.
Four-week arc
A focused studio to help you move academic work forward.
The four 1-hour live sessions are planned for Wednesdays: July 29, August 5, August 12, and August 19. Each week is built around a clear step: see how Codex is used, choose the work you want to improve, build with a practical kit, then share what you made.
Week 1Wednesday, July 29
How OpenAI Research uses Codex
Purpose: See how Codex is used in serious research work, grounded in concrete examples from OpenAI Research.
What happens: An OpenAI researcher walks through concrete examples: reading an unfamiliar repo or notebook, finding where something is failing, cleaning up code, adding checks, running an experiment, and summarizing what changed.
Week 2Wednesday, August 5
Build in tracks
Purpose: Work in the setting that best matches your day-to-day academic work.
What happens: Join the track closest to your work. Each track includes starter examples and time to try one with your own material.
- Teaching and student learning
- Research and code
- Lab and department workflows
Week 3Wednesday, August 12
Use the Codex Build Kit
Purpose: Keep building with a reusable kit for courses, labs, files, and repos.
What happens: Use track-specific workflows, copyable prompts, example inputs, review checks, and before-and-after examples. The session may include themed clinics for common needs such as repo walkthroughs, notebook cleanup, teaching simulations, reproducibility checks, and lab onboarding.
Between Weeks 3 and 4August 12-19
Submit a case-study snapshot
Purpose: Turn your example into a clear story you can share with peers, your institution, and the OpenAI Education team.
What happens: Submit what you built, what Codex helped with, what you reviewed or changed, what got better, what another professor could reuse, and whether you are open to follow-up.
Week 4Wednesday, August 19
Faculty Showcase
Purpose: Learn from strong peer examples and identify stories worth developing.
What happens: Learn from faculty across the three tracks: what they brought in, how they used Codex, what they reviewed, what changed, and what you could adapt for your own teaching, research, lab, or department.
Tracks
Pick the path closest to your work.
Faculty bring different materials, but the shared tracks and Codex Build Kit keep the experience manageable for a large group.
Teaching and student learning
Assignments, code examples, simulations, technical course materials, student guidance, and feedback workflows.
Research and code
Notebooks, repositories, scripts, data checks, simulations, experiments, and reproducibility work.
Lab and department workflows
Lab onboarding, SOPs, student support, meeting prep, project tracking, and knowledge handoffs.
Apply
Apply to the Studio
Tell us who you are, where you teach, and what you would like to build with Codex. The OpenAI Education team will review applications and follow up with selected participants.
FAQ
What faculty need to know.
Is the studio virtual?
Yes. The studio is fully virtual, with four 1-hour sessions. Confirmed participants will receive calendar holds and session links before the program begins.
When does the studio meet?
Each live session is 1 hour. Sessions are planned for Wednesdays: July 29, August 5, August 12, and August 19. Confirmed participants will receive the final calendar holds and session links.
Who should apply?
Faculty who want to use Codex in teaching, research, code, lab operations, or department workflows. A starting idea is enough; a finished project is optional.
What should I bring?
Bring one real asset you are comfortable working with: a course assignment, notebook, repository, data workflow, lab onboarding document, simulation idea, or department process.
Do I need to know how to code?
Coding experience is optional. One track is focused on research and code, and the studio also supports teaching, student learning, lab operations, and department workflows.
Will faculty work be showcased publicly?
Some examples may be invited into follow-up interviews and editing for possible publication on the OpenAI for Education website or other OpenAI Education channels. Publication always requires faculty and institution approval.
What happens after I apply?
The OpenAI Education team will review applications and contact selected participants with next steps and session details.
Who can I contact with questions?
Email education@openai.com if you have questions about the studio, timing, or whether the program is a fit.