结合AI辅助开发的多层级指导模式：面向K-12学生与本科生的真实工程实践
Multi-Tier Mentorship with AI-Assisted Development: Authentic Engineering for K-12 and Undergraduates
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中文总结 AI 辅助
本文提出结合AI辅助开发的多层级指导框架，让K-12学生与本科生协作开发LuckyTag系统，试点显示该模式可降低技术障碍、提升架构理解，为跨群体计算协作提供了新方案。
中文摘要 AI 辅助
K-12学生常具备创造性的工程构想，但缺乏将构想落地的技术技能；而本科生拥有编程专业能力，却鲜有机会主导真实项目或指导他人。AI辅助工具的快速发展为连接这两类群体提供了潜在桥梁，但针对K-12学生与高校间有效协作的架构仍未得到充分探索。本文提出一种多层级指导框架，使高中生能够借助大语言模型（LLM）与AI代理开展AI辅助开发，参与真实工程实践，同时本科生导师提供架构层面的监督。我们通过LuckyTag对该框架进行测试，这是一个基于NFC的隐私保护失物招领系统。该框架将高中生定位为产品负责人，本科生定位为技术架构师，教师定位为低干预顾问。一项试点研究纳入4名高中生、3名本科生和2名教师，调查数据显示参与者感知到的技术障碍大幅减少，且在系统架构理解上有所提升。主题分析表明，AI会放大而非取代指导需求，要求人类对逻辑与安全进行监督。这些发现提出了一种用于K-12学生与高校在计算整合领域开展公平协作的混合模式，该模式强调“AI微观管理”与架构推理，而非传统的语法学习。
英文摘要
K-12 students often possess creative engineering ideas but lack technical skills to build them, while undergraduates have coding expertise but few opportunities to lead real-world projects or mentor others. The rapid development of AI-assisted tools offers a potential bridge to connect these groups, yet the structure for effective K-12 and university collaborations remains underexplored. This paper introduces a multi-tiered mentorship framework enabling high school students to engage in authentic engineering through AI-assisted development using large language models and AI agents, while undergraduate mentors provide architectural oversight. We test this framework through LuckyTag, a privacy-preserving NFC-based lost-and-found system. The model positions high schoolers as product leads, undergraduates as technical architects, and faculty as minimal-intervention advisors. A pilot with four high school students, three undergraduates and two faculty yielded survey data showing high perceived barrier removal and gains in system architecture understanding. Thematic analysis reveals that AI amplifies rather than supplants mentoring demands, requiring human oversight for logic and security. These findings suggest a hybrid model for equitable K-12 and university collaboration on computing integration that emphasizes "AI micromanagement" and architectural reasoning over traditional syntax.
