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

TL;DR

This paper presents a human-in-the-loop AI system that personalizes learning by incorporating student feedback to adapt educational content in real time, improving engagement and retention.

Contribution

It introduces a novel feedback-driven approach using tagging and prompt engineering to personalize AI-generated educational responses in STEM learning.

Findings

Improved student engagement and understanding.

Enhanced learning outcomes and confidence in STEM students.

Effective real-time content adaptation based on feedback.

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.