通过以数据为中心的多模态可解释人工智能实现透明自适应学习
Transparent Adaptive Learning via Data-Centric Multimodal Explainable AI
AI总结：
针对自适应学习系统透明度不足、现有XAI技术忽略用户需求的问题，提出融合传统XAI、生成式AI与用户个性化的混合框架，重新定义可解释性为动态沟通过程，以提升教育AI透明度并优化用户体验。
摘要：
人工智能驱动的自适应学习系统正通过数据驱动的学习体验适配重塑教育领域。但这类系统大多缺乏透明度，人们对其决策机制的了解十分有限。多数可解释人工智能（XAI）技术聚焦于技术输出，却忽略了用户角色与理解能力。本文提出一种混合框架，将传统XAI技术与生成式AI模型及用户个性化功能相结合，生成契合用户需求的多模态个性化解释。我们将可解释性重新定义为适配用户角色与学习目标的动态沟通过程。文中概述了该框架的设计、教育领域XAI的核心局限，以及在准确性、公平性和个性化方面的研究方向，旨在打造既能提升透明度又能支持以用户为中心体验的可解释AI。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
