梳理教育领域可解释AI的定义与挑战，揭示术语混乱根源
Systematic Literature Review: Explainable AI Definitions and Challenges in Education
- 系统分析19篇文献，归纳出15种可解释AI定义
- 发现62项教育场景下的AI挑战，分属7类主题
- 指出缺乏统一定义导致伦理、技术等概念混淆
可解释人工智能（XAI）旨在将黑箱算法过程转化为透明机制，提升教育等领域中对AI应用的信任。本文采用PRISMA方法开展系统综述，共识别出19篇相关研究。分析显示存在15种不同的XAI定义及62项挑战。通过主题分析，这些挑战被归为七大类：可解释性、伦理、技术、人机交互（HCI）、可信度、政策与指南及其他。研究揭示，当前缺乏标准化的XAI定义，导致伦理、可信度、技术实现与可解释性等概念交叉重叠且表述不一，造成理解混乱，阻碍其在教育领域的有效应用。
原文摘要 · Abstract (English)
Explainable AI (XAI) seeks to transform black-box algorithmic processes into transparent ones, enhancing trust in AI applications across various sectors such as education. This review aims to examine the various definitions of XAI within the literature and explore the challenges of XAI in education. Our goal is to shed light on how XAI can contribute to enhancing the educational field. This systematic review, utilising the PRISMA method for rigorous and transparent research, identified 19 relevant studies. Our findings reveal 15 definitions and 62 challenges. These challenges are categorised using thematic analysis into seven groups: explainability, ethical, technical, human-computer interaction (HCI), trustworthiness, policy and guideline, and others, thereby deepening our understanding of the implications of XAI in education. Our analysis highlights the absence of standardised definitions for XAI, leading to confusion, especially because definitions concerning ethics, trustworthiness, technicalities, and explainability tend to overlap and vary.
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