Human-in-the-Loop Systems for Adaptive Learning Using Generative AI

2025-08-14

AI Summary

This study addresses suboptimal personalization and low knowledge retention in STEM education by proposing a human-in-the-loop (HITL)-driven generative AI adaptive learning framework. Methodologically, it integrates retrieval-augmented generation (RAG), structured prompt engineering, and a student feedback labeling mechanism, enabling learners to critically annotate and collaboratively refine AI-generated content. Feedback labels are used to train a dynamic response optimization model that supports student-driven, iterative AI refinement. The key contribution lies in explicitly modeling fine-grained human feedback as computable signals and embedding this closed-loop feedback directly into the generative pipeline. Preliminary experiments demonstrate statistically significant improvements: +23.6% in knowledge retention, +31.2% in classroom engagement, and enhanced self-efficacy (p < 0.01), validating the efficacy of feedback-driven human–AI collaboration for personalized STEM instruction.

Technology Category

• Humans and AI: Human-in-the-loop Machine Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Enhancing personalized learning using generative AI and student feedback
• Improving student engagement and retention in STEM education
• Creating dynamic learning environments through real-time AI adaptation

Innovation

• Human-in-the-Loop with generative AI
• Tagging and prompt engineering personalization
• Retrieval-Augmented Generation for real-time adaptation