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.

翻译：人机协同（HITL）方法通过将学生反馈直接整合到人工智能生成的解决方案中，利用生成式人工智能增强个性化学习。学生使用预定义的反馈标签对人工智能的回应进行评价和修改，从而促进更深层次的参与和理解。这使学生能够积极塑造自己的学习过程，而人工智能则充当自适应伙伴。该系统利用标签技术和提示工程实现内容个性化，并以此驱动检索增强生成（RAG）系统检索相关教育材料并实时调整解释说明。本研究建立在现有自适应学习研究的基础上，展示了学生驱动的反馈循环如何修改人工智能生成的回应，以提高学生的知识保持度和参与度，特别是在STEM教育领域。一项针对STEM学生的初步研究结果表明，与传统人工智能工具相比，该方法能改善学习成果并提升学习信心。这项工作凸显了人工智能通过迭代优化创建动态、反馈驱动和个性化学习环境的潜力。