基于用户特征的AI驱动提示解释个性化：一项实证评估
Personalizing explanations of AI-driven hints to users' characteristics: an empirical evaluation
AI总结：
本文通过实证研究，探讨了针对低认知需求和尽责性用户个性化AI驱动提示解释的效果，发现个性化能提升用户互动、理解和学习效果。
摘要：
本文扩展了一个现有的智能教学系统（ITS），该系统通过AI驱动的个性化提示支持学生学习，并能生成解释以说明为何/如何生成提示。在本研究中，我们针对具有低水平的两个特质——认知需求和尽责性——的学生，个性化这些提示解释，以增强他们与解释的互动，基于先前发现，这些学生通常不请求解释，尽管他们可能从中受益。我们通过正式的用户研究评估了个性化提示解释的有效性。我们的结果表明，个性化提高了目标用户与提示解释的互动、对提示的理解以及学习效果。因此，本研究为个性化可解释AI（PXAI）在教育中的价值提供了初步证据。
英文摘要：
The paper extends an existing Intelligent Tutoring System (ITS) that supports students' learning via AI-driven personalized hints and can generate explanations to justify why/how the hints were generated. In this work, we investigate personalizing these hint explanations to students with low levels of two traits, Need for Cognition and Conscientiousness in order to enhance their engagement with the explanations, based on prior findings that these students generally do not ask for the explanations although they would benefit from them. We evaluate the effectiveness of the personalized hint explanations with a formal user study. Our results show that the personalization increases our target users' interaction with the hint explanations, their understanding of the hints, and their learning. Hence, this work contributes to exiting initial evidence on the value of Personalized Explainable AI (PXAI) in education.
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。
