Multi-Tier Mentorship with AI-Assisted Development: Authentic Engineering for K-12 and Undergraduates

This research introduces a novel multi-tiered mentorship framework designed to bridge the gap between K-12 students' innovative engineering ideas and their technical execution, leveraging AI-assisted development. Undergraduates provide architectural oversight, mentoring high school students who utilize large language models and AI agents for authentic engineering projects. The framework was successfully tested with LuckyTag, a privacy-preserving NFC-based lost-and-found system, demonstrating a structured approach to effective K-12 and university collaborations using emerging AI technologies.

This framework offers a strategic model for cultivating future talent by providing practical engineering experience to younger students and leadership opportunities for undergraduates, enhanced by cutting-edge AI tools. It addresses a critical gap in STEM education by transforming how technical skills are developed and knowledge is transferred, fostering innovation and inter-generational collaboration.

K-12 students often lack the technical skills to implement their creative engineering ideas.

Forward consideration, not a verified fact.