自动作文评分系统中解释对学生信任和动机的影响

Rianne Conijn, Patricia K. Kahr, Chris J. Snijders
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引用次数: 5

摘要

在教育中使用人工智能(AI)时,包括透明度在内的伦理考虑发挥着重要作用。可解释的人工智能被创造为一种解决方案,以提供对人工智能算法内部工作原理的更多了解。然而,关于如何设计教育中的人工智能解释的精心设计的用户研究仍然有限。目前的研究旨在确定自动作文评分系统的解释对学生的信任和动机的影响。这些解释是通过对学生的需求启发研究,结合可解释人工智能的指导方针和框架来设计的。测试了两种类型的解释:全文全局解释和准确性陈述。结果表明,与没有解释相比,两种解释对学生的信任和动机都没有影响。有趣的是,系统提供的成绩,特别是学生自我评估成绩与系统成绩之间的差异,显示出很大的影响。因此,在考虑人工智能在教育中的解释效果时,考虑系统结果的影响(这里是:分数)是很重要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Effects of Explanations in Automated Essay Scoring Systems on Student Trust and Motivation
Ethical considerations, including transparency, play an important role when using artificial intelligence (AI) in education. Explainable AI has been coined as a solution to provide more insight into the inner workings of AI algorithms. However, carefully designed user studies on how to design explanations for AI in education are still limited. The current study aimed to identify the effect of explanations of an automated essay scoring system on students’ trust and motivation. The explanations were designed using a needs-elicitation study with students in combination with guidelines and frameworks of explainable AI. Two types of explanations were tested: full-text global explanations and an accuracy statement. The results showed that both explanations did not have an effect on student trust or motivation compared to no explanations. Interestingly, the grade provided by the system, and especially the difference between the student’s self-estimated grade and the system grade, showed a large influence. Hence, it is important to consider the effects of the outcome of the system (here: grade) when considering the effect of explanations of AI in education.
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