Bringing Generative AI to Adaptive Learning in Education

TL;DR

This paper examines the convergence of Generative AI (GenAI) and Adaptive Learning (AL) to enhance personalized education. It surveys how GenAI can empower existing AL components (learner profiling and content/material recommendations) and proposes novel directions (content creation, intelligent agents, learning simulation). It discusses benefits such as dynamic, multimodal outputs and strong priors, while also outlining risks like hallucination and fairness, and argues for governance and responsible design. The work aims to map opportunities, risks, and research priorities to advance AL toward next-generation educational formats.

Abstract

The recent surge in generative AI technologies, such as large language models and diffusion models, has boosted the development of AI applications in various domains, including science, finance, and education. Concurrently, adaptive learning, a concept that has gained substantial interest in the educational sphere, has proven its efficacy in enhancing students' learning efficiency. In this position paper, we aim to shed light on the intersectional studies of these two methods, which combine generative AI with adaptive learning concepts. By presenting discussions about the benefits, challenges, and potentials in this field, we argue that this union will contribute significantly to the development of the next-stage learning format in education.

Figures (7)

• Figure 1: An Illustration of Adaptive Learning (AL).
• Figure 2: An overview of bringing GenAI to AL.
• Figure 3: Empowering existing algorithms in AL system with GenAI.
• Figure 4: GenAI for Content Creation in AL.
• Figure 5: GenAI for Intelligent Agent in AL.