Transparent Adaptive Learning via Data-Centric Multimodal Explainable AI

TL;DR

This paper introduces a hybrid, multimodal explainable AI framework for adaptive learning systems that enhances transparency and personalisation by integrating traditional XAI with generative models and user-specific explanations.

Contribution

It presents a novel data-centric, user-tailored explainability framework combining traditional XAI and generative AI for adaptive learning environments.

Findings

Framework enables personalised, multimodal explanations.

Addresses key limitations of existing XAI in education.

Guides future research on fairness and accuracy.

Abstract

Artificial intelligence-driven adaptive learning systems are reshaping education through data-driven adaptation of learning experiences. Yet many of these systems lack transparency, offering limited insight into how decisions are made. Most explainable AI (XAI) techniques focus on technical outputs but neglect user roles and comprehension. This paper proposes a hybrid framework that integrates traditional XAI techniques with generative AI models and user personalisation to generate multimodal, personalised explanations tailored to user needs. We redefine explainability as a dynamic communication process tailored to user roles and learning goals. We outline the framework's design, key XAI limitations in education, and research directions on accuracy, fairness, and personalisation. Our aim is to move towards explainable AI that enhances transparency while supporting user-centred experiences.