AI Research & Competition
Learn to understand, build, and question AI.
Explore programsAI Research and Competition (AIRC) teaches students how AI works and how to investigate it. From a first app to original research, students learn to turn questions into experiments and explain what the evidence shows.
Behind the curriculum
Our team brings experience from MIT CSAIL, Stanford HAI, and Carnegie Mellon's Human-Computer Interaction Institute.
For more than five years, we have worked on AI literacy, including developing curricula for MIT App Inventor (25M+ learners) alongside UNESCO specialists. This work extends to AI education in schools and universities.
Courses
Build the knowledge, tools, and judgment
to explore ideas independently.
No technical background needed
Explore what AI can do, then use it to make something of your own. Interactive tools, App Inventor, and a first encounter with research help students learn how to begin and what to try next.
What you'll take awayAn app project, a short abstract, and experience presenting your ideas, with tools to keep exploring independently.
Where it can lead
For younger learners continuing from AIRC 001
Use basic Python to explore data, make patterns visible, and investigate questions through projects. Build on App Inventor experience while learning to test an idea beyond the app itself.
What you'll take awayA working project, a Python notebook, and an explanation of what you found.
Where it can lead
For students exploring AI in depth
Connect the mathematics behind language models to the experiments and papers that advance them. Learn to question evidence, design a fair comparison, and communicate a research idea with clarity.
What you'll take awayExperience reading papers critically, designing comparisons, and communicating an informed research proposal.
For students building research skills
Turn an idea into an experiment someone else can reproduce. Build with Python and Git, test models on real data, and examine where they fail.
What you'll take awayPractical experience with reproducible experiments, a research demonstration, and a guided mini-paper.
AIRC 001: Discover selected.
Research mentorship
Learn directly from our research team. Discover where your curiosity can lead.
AIRC 301 · Research
Develop an original research project with sustained mentorship. Define a question, design the experiments, and learn to turn the results into a paper that makes a clear contribution.
What you'll take awayAn original research project and a paper developed through sustained feedback.
Where it can lead
Student competitions and journals, with scope for suitable IEEE and ACM conferences.
AIRC 401 · By invitation
Develop a novel method with published researchers and test it against strong alternatives. For students ready to make a focused contribution to an active research direction.
What you'll take awayA focused research contribution, rigorous experiments, and a paper prepared for peer review.
Where it can lead
Workshops at leading AI conferences in the highest tier (A*), including:
Our community
Our community includes U.S. Olympiad team members, ISEF first-place category winners, and Thermo Fisher JIC grand award recipient. They come from leading high schools and universities, including Phillips Exeter, Groton, MIT, Harvard, Stanford, and beyond.
“My favorite thing about the way the course was taught was the activities assigned in class, such as the research paper and GitHub UI project. These projects will certainly serve as the basis for my future endeavors in coding. Working with other students, peer-reviewing, and actually doing experiments myself brought out the joy in this class.
One concept I found really interesting was using Matplotlib to simulate graphs, allowing me to use NumPy to accurately visualize data.”
Perspective 1 of 4. Fred Zhang, Amador Valley High School, AIRC 201: My favorite thing about the way the course was taught was the activities assigned in class, such as the research paper and GitHub UI project. These projects will certainly serve as the basis for my future endeavors in coding. Working with other students, peer-reviewing, and actually doing experiments myself brought out the joy in this class. One concept I found really interesting was using Matplotlib to simulate graphs, allowing me to use NumPy to accurately visualize data.
Applications
Find a course for your next step, or share your interests for a future research opening.
