AI for Youth Academy Future Scholars Research Initiative

Summer Camp · Coming Summer 2026 · Weekend Studios

Build world-changing ideas with AI, one year at a time.

AI for Youth Academy is a five-year journey for middle and high school innovators. Build foundational math, programming, and machine learning intuition, then apply it in a capstone project with guidance from mentors and college ambassadors.

Explore the program Current offerings Request the program guide
5-Year Pathway
Four years of foundations plus a capstone project year.
Mentor Network
College ambassadors and industry coaches in every cohort.
Future Focused
Ethics, creativity, and impact baked into every project milestone.

Why This Academy Exists

A small extracurricular circle for standout students who want to geek out, go deeper, and build projects way beyond the usual curriculum.

Our Guiding Beliefs

Three principles shape every AI for Youth session

  1. Hands-on practice beats theory alone. Middle schoolers grasp AI faster when they build, train, and test real models.
  2. Early foundations unlock high school opportunities. Students who understand AI before eleventh grade are ready to lead clubs, volunteer on tech initiatives, or launch bold ideas.
  3. Self-motivated learners thrive. Complex topics stick when students own their learning journey and stretch further with mentor support.

Five-year roadmap

A five-year plan built for intuitive understanding.

Thanks for your trust; welcome to the journey. Over the next four years students build foundations in math, programming, machine learning, and neural networks. These topics sound graduate-level, so we focus on core ideas without formal derivations. Year 5 is a project capstone that applies the full stack.

  1. Year 1 · 2D Math + Python Foundations

    2D vectors, matrix representations, and beginner Python

    Spring term core foundations

    Students build intuition for 2D space, matrix representations of vectors, and linear equations while learning Python basics through FIRST LEGO Challenge robotics.

    Key Themes

    • 2D space, points, vectors, and matrix representations
    • 2D representations of linear equations on a map
    • Python fundamentals: functions, loops, and conditionals

    Hands-on Moments

    • Program a FIRST LEGO Challenge robot car to navigate a 2D map.
    • Use the FIRST LEGO Python library and function calls to control movement.
    • Practice loops and if/else logic to make navigation reliable.

    Tools

    • Python
    • FIRST LEGO Python library
    • FIRST LEGO Challenge field

    What Students Walk Away With

    • Explain vectors and linear equations with 2D map examples.
    • Write beginner Python programs using functions, loops, and conditionals.
    • Connect robot movement to 2D math representations.
    SPRING 2026 2D math + Python foundations with LEGO labs.
  2. Year 2 · Supervised Learning & Regression

    Quadratics, tangents, and first ML models

    Year 2 core studio

    Students represent quadratic equations in 2D space, build tangent intuition, and learn supervised learning through linear regression and loss functions. Programming expands to OpenCV basics, NumPy vector representation, and a PyTorch implementation of linear regression.

    Key Themes

    • Quadratic curves in 2D space and tangent intuition
    • Supervised learning, linear regression, and loss functions
    • Vector representation with NumPy and first PyTorch models

    Hands-on Moments

    • Visualize quadratic curves and tangents with code-driven graphs.
    • Practice OpenCV image basics to build simple datasets.
    • Implement and train a linear regression model in PyTorch.

    Tools

    • Python
    • NumPy
    • OpenCV
    • PyTorch

    What Students Walk Away With

    • Explain supervised learning and loss with intuitive examples.
    • Build a linear regression model end to end.
    • Use NumPy vectors and OpenCV to prepare training data.
    Studio Series Supervised learning labs with regression projects.
  3. Year 3 · Linear Algebra & Unsupervised Learning

    3D space, projection, and clustering

    Year 3 core studio

    Students move into 3D space, 2D projection, and linear algebra basics while exploring dimensionality reduction and unsupervised learning. They also build classification models with scikit-learn using the MNIST dataset.

    Key Themes

    • 3D space, 2D projection, and linear algebra basics
    • Dimensionality reduction and k-means clustering
    • Classification models, including an intro to SVMs

    Hands-on Moments

    • Project 3D points into 2D and interpret spatial relationships.
    • Run k-means clustering on image embeddings or numeric datasets.
    • Train scikit-learn classifiers on the MNIST dataset.

    Tools

    • Python
    • NumPy
    • scikit-learn
    • MNIST

    What Students Walk Away With

    • Describe when to use supervised vs. unsupervised methods.
    • Apply dimensionality reduction and k-means to real data.
    • Build and evaluate a classifier with scikit-learn.
    Studio Series Unsupervised learning and classification labs.
  4. Year 4 · Deep Learning & Foundation Models

    CNNs, fine-tuning, and modern model stacks

    Year 4 advanced studio

    Students build pre-calc graph intuition, then dive into CNN structure, fine-tuning workflows, and foundation model applications. Programming includes AI-assisted PyTorch work and an optional transformer overview if time allows.

    Key Themes

    • Pre-calc graph intuition for model behavior
    • CNN architecture, fine-tuning, and applied use cases
    • Foundation models and an optional transformer overview

    Hands-on Moments

    • Fine-tune a CNN on a small custom dataset.
    • Use AI-assisted tools to accelerate PyTorch coding and debugging.
    • Explore a lightweight transformer demo if time allows.

    Tools

    • PyTorch
    • Pre-calc graphing tools
    • AI coding assistants

    What Students Walk Away With

    • Explain CNN structure and why fine-tuning works.
    • Apply a foundation model to a targeted task.
    • Use AI-assisted workflows to iterate faster and document choices.
    Advanced Studio Deep learning labs with CNNs and fine-tuning.
  5. Year 5 · Capstone Project

    Build a complete project from idea to demo

    Project year

    Students plan and deliver a full AI project, combining math intuition, ML workflows, and neural network tools into a real-world prototype.

    Key Themes

    • Problem framing and project planning
    • Dataset selection, model choice, and iteration
    • Communication, ethics, and final demo

    Hands-on Moments

    • Define a capstone scope with mentor feedback.
    • Build, test, and refine an end-to-end prototype.
    • Share results through a showcase, report, or demo.

    Tools

    • Student-chosen tool stack
    • Python
    • Presentation tools

    What Students Walk Away With

    • Deliver a working AI project with clear documentation.
    • Justify model choices and trade-offs.
    • Create a portfolio-ready showcase artifact.
    Capstone Studio Mentor-guided project cycle.

Learning experiences

Designed for curious creators who learn by doing.

Whether summer camps or weekend studios, every cohort blends collaborative play, rapid prototyping, and real-world mentorship. Students build confidence and community while solving meaningful problems.

Studio-powered summer camps

Choose immersive week-long or multi-week camps that mix morning deep-dives with afternoon build sessions. Showcase nights bring families, educators, and community partners together to celebrate progress.

  • Daily design sprints and lightning talks from guest mentors
  • Hands-on labs with maker kits, cameras, and edge AI devices
  • Reflective journals and demo booths to document growth

After school & weekend labs

Weekly meetups keep the momentum going during the school year. Teams plan, prototype, and iterate with guidance from college ambassadors and industry coaches.

  • Community-centered problem statements sourced from partners
  • Peer code reviews and showcase standups every other week
  • Pathway badges that recognize technical and leadership growth

Mentor moments & future pathways

Students meet researchers, designers, and entrepreneurs who apply AI responsibly. Alumni get early access to internships, hackathons, and paid teaching-fellow roles.

  • Portfolio coaching and college-ready recommendation letters
  • Connections to national AI competitions and showcase events
  • Alumni network with year-round learning and leadership opportunities

Track planner

Choose the focus that fits your student leaders.

We run two parallel tracks with the same five-year pacing. Years 1-2 are a shared core foundation. Starting in Year 3, students choose a volleyball or figure skating focus for projects and datasets.

Students playing volleyball

Track A

Volleyball Focus Track

Theme: Volleyball (introduced in Year 3)

A shared Year 1-2 core covers math, Python, and supervised learning. Starting in Year 3, projects and datasets focus on volleyball movement, gameplay, and team insights.

Figure skater practicing on ice

Track B

Figure Skating Focus Track

Theme: Figure Skating (introduced in Year 3)

A shared Year 1-2 core covers math, Python, and supervised learning. Starting in Year 3, projects and datasets focus on figure skating movement, choreography, and performance insights.

Join the waitlist

Ready to enroll your student?

Seats are limited and selection is highly competitive. Share your details to receive the program guide and get notified when enrollment opens. Students who are exploring on their own are always welcome to follow our self-paced study guides while they wait.