让学生用标签反馈改写AI答案，实现动态个性化学习
Human-in-the-Loop Systems for Adaptive Learning Using Generative AI
- 用标签和提示工程让学生修改AI生成内容
- 学生反馈后，系统实时调整解释，提升理解力
- 适合需要互动式辅导的STEM学习者
一种人机协同（HITL）方法利用生成式AI增强个性化学习，通过将学生反馈直接融入AI生成内容中。学生使用预设反馈标签对AI回答进行批判与修改，促进深度参与与理解。该系统采用标签技术和提示工程，驱动检索增强生成（RAG）系统，实时调取相关教育材料并优化解释。基于对理工科学生的初步研究显示，相比传统AI工具，该方法显著提升了学习成效与自信心。研究展示了学生主导反馈循环如何使AI响应持续优化，推动构建动态、反馈驱动、个性化的学习环境。
原文摘要 · Abstract (English)
A Human-in-the-Loop (HITL) approach leverages generative AI to enhance personalized learning by directly integrating student feedback into AI-generated solutions. Students critique and modify AI responses using predefined feedback tags, fostering deeper engagement and understanding. This empowers students to actively shape their learning, with AI serving as an adaptive partner. The system uses a tagging technique and prompt engineering to personalize content, informing a Retrieval-Augmented Generation (RAG) system to retrieve relevant educational material and adjust explanations in real time. This builds on existing research in adaptive learning, demonstrating how student-driven feedback loops can modify AI-generated responses for improved student retention and engagement, particularly in STEM education. Preliminary findings from a study with STEM students indicate improved learning outcomes and confidence compared to traditional AI tools. This work highlights AI's potential to create dynamic, feedback-driven, and personalized learning environments through iterative refinement.
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