# Transparent Adaptive Learning via Data-Centric Multimodal Explainable AI

arXiv: https://arxiv.org/abs/2508.00665 · HTML: https://mlsanity.com/paper/2508.00665/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation](https://mlsanity.com/paper/2310.03112/) (2023)
- [Steering AI-Driven Personalization of Scientific Text for General Audiences](https://mlsanity.com/paper/2411.09969/) (2024)
- [Why do explanations fail? A typology and discussion on failures in XAI](https://mlsanity.com/paper/2405.13474/) (2024)

## Built on by (most cited first, 1 of 1)

- [EHSAN: Leveraging ChatGPT in a Hybrid Framework for Arabic Aspect-Based Sentiment Analysis in Healthcare](https://mlsanity.com/paper/2508.02574/) (2025)

## Abstract (arXiv)

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.