Our Project

For domain-specific concepts, traditional generative AI applications tend to give inconsistent and inaccurate guidance due to the lack of grounded, authoritative content, causing students who utilize these tools to be mislead. This product addresses this gap by providing scalable, structured, and conceptually grounded tutoring for static-analysis CTF challenges.
We propose building an AI tutoring agent designed specifically to answer student questions about static analysis CTF challenges, leveraging Retrieval-Augmented Generation (RAG) and a curated knowledge graph of program analysis concepts. This will be delivered as a web-based interactive tutoring system that allows students to ask natural language questions and receive grounded, consistent, and educationally structured answers.
Sponsor: Lan Zhang, Assistant Professor, NAU