Learn AI During Winter Break

Nova Accelerated AI Fundamentals for Middle School Students

Our project-based AI programming course was designed to spark curiosity in middle school students, introducing them to today’s most transformative technology. We offer small, live, online classes focused on AI that provide advanced middle school students with hands-on experience in cutting-edge tech. Developed and taught exclusively by Stanford, Harvard, and Yale alumni and students, this program provides mentorship to help students build a strong foundation in technology.

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What's included?

The Nova Accelerated AI Fundamentals course is an intensive course designed for advanced middle school students during fall, winter, and spring breaks. Students go beyond the classroom to learn and apply the latest advancements in technology, guided by instructors at the forefront of innovation.

Five sessions, ten hours

Over winter break, students can join our 2-hour small-group online classes and build AI projects. Classes meet daily, and are capped at a 1:5 instructor-to-student ratio.

Stanford, Harvard, and Yale instructors

Our program has been developed and taught exclusively by Stanford instructors with experience at leading companies like Facebook, OpenAI, and Microsoft.

Project-based AI programming

Our curriculum is frequently updated to include the latest tech advancements and includes hands-on programming. Our latest schedule includes AI agents and more.

Program costs are $499 USD, covering 10 hours of mentorship and all software expenses for hands-on activities.

We host two winter sessions

No programming or computer science experience needed. We simply ask for a genuine interest in learning to code!

Session 1: Dec 16th - 20th

Session 3: Dec 26th - 30th

Classes meet online from 9:30am-11:30am PT or 1:00pm - 3:00pm PT.

Five session schedule

Over the course of one week, middle school students will learn the fundamentals of AI through small-group instruction and hands on projects.

Introduction to Algorithms and AI Basics

Session 1

Overview: Learn about the fundamental mechanics of AI systems and recognize algorithms in everyday life. Understand the parts of an algorithm—input, transformation, output—and see how AI systems use datasets, learning algorithms, and predictions.

‍Activity: Explore examples of algorithms in daily life, like baking a cake, and examine simple AI examples to understand core concepts.

Classification and Machine Learning Basics

Session 2

Overview: Discover the basics of supervised machine learning, focusing on classification problems. Understand how the quantity and quality of training data affect the accuracy and robustness of ML models.

‍Activity: Demonstrate a basic supervised ML example (e.g., image classification) and discuss the role and importance of training data.

AI in Everyday Life and Prediction Analysis

Session 3

Overview: Explore common AI applications, such as recommendation systems and virtual assistants, to understand how they make predictions and influence our lives. Learn about socio-technical systems, their optimization, and the goals humans set for them.

‍Activity: Analyze real-world AI applications like YouTube’s recommendation algorithm, and discuss the difference between advertised and actual goals.
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Bias in AI Systems

Session 4

‍Overview: Learn about "algorithmic bias" in AI and its impact on different groups within socio-technical systems. Identify various stakeholders in these systems and understand how training data composition affects outcomes.
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‍Activity: Examine a case study on algorithmic bias and map out stakeholders in a system like facial recognition to see how different groups are affected.

Ethics, Stakeholders, and AI Design

Session 5

Overview: Participate in developing an ethical framework for a socio-technical system. Explore how stakeholder needs and values can inform system goals and design features that align with ethical considerations.

‍Activity: Construct an ethical matrix, analyze the ethical implications of a chosen technology, and brainstorm potential ethical redesigns to meet stakeholder needs.classification accuracy.

Our mentors have experience at leading institutions and companies

We look beyond impressive work experience in our mentors—teaching expertise and a genuine passion for AI are essential. With an acceptance rate of around 10% for our AI Fundamentals course, only the most dedicated make the cut. Of course, experience at top-tier AI companies is always a plus.

Hear from some of your exceptional mentors

Meera

MS Computer Science, Stanford University
BS Computer Science, Stanford University

"Starting early opens up endless opportunities, giving students the foundational skills they need to navigate and innovate in the future. My time working on machine learning at Google and NVIDIA has given me a deep appreciation and expertise in machine learning and AI—what I believe to be the most transformative technology of our time. I'm passionate about teaching and have spent nearly five years mentoring secondary school students in this dynamic field, hoping they’ll continue to advance the frontiers of AI in their education and possibly their future careers."

Hear from our middle school students

Jess
"I loved this course! The classes were interactive and the my teacher was amazing"

Raj
"All the projects made learning computer science and AI fun and exciting!"

Anna
"I really liked it - I learned so much! I'm excited to do the month AI course next"

Brian
"I'm glad I did something productive over break. I also liked the other three students!"

Cassie
"I haven't programmed before so it was great to learn something so important"

Tom
"We learned really cool things - from coding to GenAI. I wanted to learn about AI so this was a great experience"

Priya
"I really loved my teacher Nancy - she was so good at explaining new concepts, and made her class fun"

Divya
I plan to apply the skills I've built here to do AI research and am excited to learn as much as I can about AI"

What's the difference between the Nova AI Fundamentals and the Accelerated program?

Nova AI Fundamentals has the same time - 10 hours - as Nova Accelerated AI Fundamentals. The difference is in the timing of the program - during break vs. during the school year. How do you know which one is the best for you?

Choose Nova AI Fundamentals if:

You can commit ~3 hours per week, with 2 hours of class time and ~1-2 hours of homework during the school year,

You prefer more time to fully understand concepts instead of daily classes with new content.
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Choose Nova Accelerated AI Fundamentals if:

You can commit ~3 hours per day, including 2 hours of class time and ~1-2 hour of homework over your winter, spring, or fall breaks.
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You want a intensive crash course on AI, with hands-on learning through daily AI instruction.
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What's next after this?

So much more- this is just the first small step in your CS and AI journey. We encourage students to keep building and exploring in the rapidly evolving field of AI. It's exciting to see how many students continue to expand on their projects after the program.

Based on performance and with instructor recommendation, a few students—typically in 8th grade and with prior computer science experience—have started conducting advanced AI research through Nova Research or began working on their own patentable projects through Nova Patent.

Still have questions?

Attend an Info Session