用生成式AI打造可解释的个性化学习系统
Transparent Adaptive Learning via Data-Centric Multimodal Explainable AI
- 融合XAI与生成模型，按用户角色生成多模态解释
- 将解释力重新定义为动态沟通，提升教育适应性
- 适合关注AI教育透明度与用户体验的研究者
人工智能驱动的自适应学习系统正通过数据驱动的方式重塑教育体验。然而，许多系统缺乏透明度，难以解释决策过程。现有的可解释人工智能（XAI）技术多聚焦于技术输出，忽视用户角色与理解能力。本文提出一种混合框架，整合传统XAI技术、生成式AI与用户个性化，生成针对用户需求的多模态、个性化解释。我们重新定义可解释性为基于用户角色与学习目标的动态沟通过程。文中阐述了框架设计、教育场景下XAI的关键局限，并探讨了准确性、公平性与个性化方面的研究方向。目标是实现既增强透明度又支持以用户为中心的学习体验的可解释AI。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。