为高校学习分析设计可落地的负责任AI框架
Towards an Operational Responsible AI Framework for Learning Analytics in Higher Education
- 梳理11个主流AI伦理框架，提炼适用于高校的7大核心原则
- 通过文献综述发现现有实践缺乏可操作性指导
- 提出可迭代更新的实用框架，适合高校教育机构使用
高校正越来越多地采用数据驱动策略提升学生学业成就，人工智能应用如学习分析（LA）和预测性学习分析（PLA）在识别风险学生、个性化学习、支持教师及辅助教育决策中发挥关键作用。然而，这些系统可能带来算法偏见等潜在危害，导致少数群体获得不平等支持。尽管已有研究强调高校学习分析中负责任AI的重要性，但现有工作大多缺乏具体实施路径。本文针对高等教育中的学习分析，提出一个全新的负责任AI框架。我们首先将11个已有的负责任AI框架（包括领先科技公司的版本）映射到高校学习分析场景，提炼出透明度、公平性、问责制等7项核心原则。随后通过系统文献回顾，分析这些原则在实际中的应用情况。基于上述发现，我们提出一个可操作的框架，旨在为高校机构提供实践指导，并具备随社区反馈持续演进的能力，确保其在未来学习分析系统发展中保持相关性。
原文摘要 · Abstract (English)
Universities are increasingly adopting data-driven strategies to enhance student success, with AI applications like Learning Analytics (LA) and Predictive Learning Analytics (PLA) playing a key role in identifying at-risk students, personalising learning, supporting teachers, and guiding educational decision-making. However, concerns are rising about potential harms these systems may pose, such as algorithmic biases leading to unequal support for minority students. While many have explored the need for Responsible AI in LA, existing works often lack practical guidance for how institutions can operationalise these principles. In this paper, we propose a novel Responsible AI framework tailored specifically to LA in Higher Education (HE). We started by mapping 11 established Responsible AI frameworks, including those by leading tech companies, to the context of LA in HE. This led to the identification of seven key principles such as transparency, fairness, and accountability. We then conducted a systematic review of the literature to understand how these principles have been applied in practice. Drawing from these findings, we present a novel framework that offers practical guidance to HE institutions and is designed to evolve with community input, ensuring its relevance as LA systems continue to develop.
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