可解释的AI框架用于英语写作评估中的准确性,公平性和学习者的感知
1School of Foreign Languages/School of Translation & Interpreting, Henan University; liubinscp204@163.com.
Journal of visualized experiments : JoVE
|January 12, 2026
概括
自动写作评估 (AWE) 需要可解释的AI (XAI) 来确保公平和透明度. 这项研究发现了AWE系统中的偏见,特别是在非母语英语的人群中,突出了提高公平性和学习者对教育技术的信任的需要.
科学领域:
- 教育中的人工智能
- 自然语言处理自然语言处理.
- 教育技术的教育技术
背景情况:
- 自动写作评估 (AWE) 提供了效率,但往往缺乏透明度和公平性.
- 传统的AWE框架可能会忽视学习者的感知和公平性,限制教育价值.
- 可解释的人工智能 (XAI) 对教育工具的信任和理解至关重要.
研究的目的:
- 为自动化写作评估 (AWE) 提出和验证一个可解释的AI (XAI) 框架.
- 整合一个多层次验证模型 (三级评估框架 - - TLEF),解决准确性,公平性和感知问题.
- 调查多语言学习者在AWE中的公平偏见和学习者认知.
主要方法:
- 从764名多语言学习者 (CEFR A2-C1) 收集数据,使用写作任务和问卷.
- 采用分层的随机抽样和双人工智能/人类专家评分.
- 使用统计分析,包括相关性,RMSE,均等赔率和SEM.
主要成果:
- AWE系统显示整体有效性 (r = 0.82),但发现了显著的差异.
- 中文母语者表现出较低的同意 (0.72) 和较高的RMSE (中位数2.15).
- 公平偏见在较低的熟练程度 (A2学习者) 更加明显,感知到的公平性介导了学习者满意度.
结论:
- 将公平性和知觉作为关键可解释性维度的重构增强了AWE的理论基础.
- 拟议的XAI框架提供了一种实际方法,以提高教育技术的透明度,公平性和社会接受度.
- 解决人工智能公平性和学习者的感知对于推动数字教育转型中的AWE至关重要.
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