Transparent Adaptive Learning via Data-Centric Multimodal Explainable AI
This addresses the problem of opaque decision-making in educational AI systems for learners and educators, but it appears incremental as it builds on existing XAI techniques.
The paper tackles the lack of transparency in AI-driven adaptive learning systems by proposing a hybrid framework that integrates traditional XAI with generative AI and user personalisation to generate multimodal, personalised explanations, aiming to enhance transparency and user-centred experiences.
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.