Human-in-the-Loop Systems for Adaptive Learning Using Generative AI
This work addresses personalized learning for STEM students, building incrementally on existing adaptive learning research.
The paper tackles the problem of enhancing personalized learning by integrating student feedback into AI-generated solutions using a Human-in-the-Loop approach with generative AI, resulting in improved learning outcomes and confidence for STEM students compared to traditional AI tools.
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.