What if your student could publish a research paper or launch a real experiment under Stanford graduate mentorship, starting in high school?

The AI Research Track is a core part of our **Stanford Grad AI Accelerator Program,** designed for ambitious high school students eager to explore real-world questions and generate original insights. Admission is selective: students must first complete our Weekend AI Spark Program; following Spark, our team and the student will jointly determine whether this AI Research Track or the alternative AI Application & Startup Track is the best fit.

Mentored by Stanford graduate students and researchers, participants go on a 10-week deep dive into the research process, turning their curiosity into a full AI research project, from ideation to final paper.

Whether your child is drawn to environmental science, psychology, health tech, or social impact, this AI track helps them:

• Review and analyze the latest academic literature
• Design experiments or simulations based on real-world problems
• Collect and interpret data using tools from academia and industry
• Write and present a formal research paper or scientific report

Ideal for students looking to strengthen their academic portfolios for elite summer programs (like RSI, Simons, or MIT PRIMES), highly selective college admissions, or those who simply want to experience the thrill of discovering something new, under the guidance of real researchers.

Program Supported and Awarded by:

🧪 Research Track Example Structure: 10-Week AI Research Accelerator

• Format: Online. 1 hr Session Each Week.
• NOTE: This is an example structure. The final curriculum and learning objectives will be personalized based on the students’ interests and experience level.
• Week 1–2: Topic Refinement + Literature Review
  • Interest and research area exploration
  • Deep dive into current AI research papers
  • Identify gaps in state-of-the-art models and datasets
  • Create an annotated bibliography of foundational and cutting-edge works
  • Learn academic writing structure using examples from Stanford AI courses
• Week 3–4: Problem Framing + Dataset Exploration
  • Define a well-scoped AI/ML research question (e.g., bias, fairness, explainability, fine-tuning)
  • Explore existing open-source datasets or prepare a small curated dataset
  • Design a reproducible baseline or experiment setup
  • Run feasibility checks and model selection with mentor feedback
• Week 5–8: Model Training + Experimental Analysis
  • Train baseline models using Python (e.g., scikit-learn, PyTorch, HuggingFace)
  • Analyze results using key metrics (accuracy, F1, BLEU, etc.)
  • Iterate on model architecture, hyperparameters, or data preprocessing
  • Visualize model performance, decision boundaries, or error cases
• Week 9–10: Final Paper + Presentation
  • Compile findings into a structured research paper (abstract, intro, methods, results, discussion)
  • Cite works using Overleaf or LaTeX templates from NeurIPS/ACL-style papers
  • Prepare a polished slide deck or research poster
  • Receive detailed mentor feedback for final revisions
  • Submit to youth journals, AI competitions, or science fairs