Figure 2 — F1 scores across models
What students build
Every student works toward a polished research artifact. Depending on the project, this may include a 4–6 page workshop-style paper, reproducible codebase, poster, technical report, preprint, or external submission. We do not guarantee publication, but we help strong projects reach a submission-ready standard.
Hallucination Rates in AI Tutoring
Abstract — We evaluate three language models on 1,200 tutoring prompts, measuring factual consistency and response quality.
3. Experiments
Baselines include GPT-4o-mini, Llama-3-8B, and Mistral-7B…
model = load_baseline()
return model.evaluate()
Figure 2 — F1 by model
Best F1: 0.82 — Llama-3-8B outperforms baseline by 12.4%
Research tracks
Students select a track aligned with their interests and technical background. Each track includes structured mentorship toward a tractable, publishable research question.
LLM Evaluation & AI Safety
Students evaluate language models on reasoning, hallucination, bias, safety, robustness, or domain-specific tasks.
Example projects
- Evaluating LLMs on high school science misconception detection
- Measuring hallucination rates in AI tutoring responses
- Comparing small open-source models on safety classification
AI for Education
Students build and evaluate tools for grading, tutoring, feedback, or learning analytics.
Example projects
- Detecting math reasoning errors in student explanations
- Evaluating LLM feedback quality on college essays
- Building a dataset of student misconceptions
ML for Health & Biology
Students use public datasets to study biomedical prediction, medical imaging, genomics, or molecular ML.
Example projects
- Predicting disease risk from tabular health data
- Classifying medical images with deep learning
- Benchmarking models for molecular property prediction
Computer Vision
Students work on image classification, detection, segmentation, generative models, or multimodal learning.
Example projects
- Detecting urban features from satellite imagery
- Benchmarking vision models on real-world distribution shifts
- Classifying plant disease from image datasets
ML for Economics & Finance
Students apply machine learning to public datasets in economics, finance, housing, labor markets, and information quality.
Example projects
- Predicting housing prices using public economic data
- Detecting financial misinformation with LLMs
- Modeling labor market trends using public datasets
AI for Climate & Social Good
Students use ML to study climate, public policy, environmental risk, and social-impact problems.
Example projects
- Predicting urban heat islands from satellite and census data
- Classifying disaster-related social media posts
- Forecasting air quality with public environmental data
How the program works
A structured 14-week research process, from choosing a direction to a submission-ready paper.
Get to know the student, choose a direction
Mentor and student align on interests, background, and a research direction.
Read papers, learn background
Student builds the literature foundation and learns the technical background for the project.
Build and run experiments
Student implements the approach, runs experiments, and iterates on results.
Analyze results
Student evaluates findings, runs follow-ups, and sharpens the research story.
Write the paper and prepare submission
Student writes the paper and prepares a submission-ready package.
Mentored by graduate researchers
PhD students, postdocs, and graduate researchers in machine learning and related fields — matched on project fit, not tutoring availability.
Mentor network includes
Outcomes students can leave with
Research paper or technical report
Reproducible codebase
Research poster
Final presentation/demo
Mentor feedback
Submission-ready manuscript
External submission support
Stronger college/research profile
Disclaimer: Publication and workshop acceptance depend on project quality, venue fit, and reviewer decisions. We help students produce the strongest possible submission, but external outcomes are not guaranteed.
Who should apply
This program is for motivated high school students who are curious about AI and machine learning, ready to commit several hours each week, and excited to work on a real research project — not just complete another course or competition.
Prior research or ML experience helps, but it is not required. What matters most is drive, follow-through, and a genuine interest in reading, experimenting, and taking ownership of your work.
Pricing & admissions
Tuition varies by program length, mentor fit, and project scope. Families receive pricing after the consultation.
Frequently asked questions
No. We help students produce submission-ready work and support strong projects through external submissions, but acceptances depend on venue fit, project quality, and reviewer decisions.
Some coding experience is helpful. We match students to projects based on their current level, technical background, and goals.
Mentors are PhD students, postdocs, and graduate researchers in machine learning and related fields.
A paper or technical report, codebase, poster, and final presentation. Strong projects may also be submitted externally.
The program runs 14 weeks.
Students are screened for motivation, technical readiness, and project fit.
Contact us
Questions about the program, admissions, or scheduling a parent/student consultation? Send us a message and we'll follow up.
Apply for the next cohort
Tell us about your background, interests, and goals through our formal application. It usually takes about 10–15 minutes.
Completing the application does not guarantee admission or a specific mentor match. Strong applicants may be invited to a brief follow-up conversation.
Begin Application