What should cybersecurity education look like in the age of AI? What does AI/ML education look like situated in cybersecurity contexts?
Welcome to the exciting world of Cybersecurity and AI/Machine Learning education for young teens. Come join us on this journey to design AI & Cybersecurity for Teens (ACT), a 40 hour module that can be used in out-of-school camps or as part of classroom learning.

AI & Cybersecurity for Teens

In this project, we are exploring and innovating on how we can teach AI and Machine Learning (ML) to 13-15 year olds through situations/issues set in the context of cybersecurity in ways that “lift the hood” on how ML models are designed, how they they work, and the impact of human decisions in this process.
The goal of our exploratory research is to innovate on learning design and pedagogy to bring together AI and cybersecurity topics, and integrate them in systematic and cogent ways that are accessible to early teen learners. We hope to push the boundaries of AI education in K-12 through developing code examples, abstractions, and coding experiences that help make fundamental ML concepts accessible without requiring mastery of the underlying (often complex) mathematical concepts. Instead of simply playing with AI models, we want early teen learners to really get a sense for the sauce in the ML models and be able to examine the underlying algorithms in order to build deeper understandings and intuitions of how AI/ML works.

This project is funded by the SaTC-Edu program of the National Science Foundation (DGE #2113803) as part of a new series of projects that explore cybersecurity education in the age of AI.


Dr. Shuchi Grover, Looking Glass Ventures (PI)
Dr. Brian Broll, Vanderbilt University (Co-PI)
Derek Babb, University of Nebraska, Omaha (Consultant)
Dr. Melissa Dark (Advisor)