Our research is a step toward ascertaining the need for personalization in XAI, and we do so in the context of investigating the value of explanations of AI-driven hints and feedback in Intelligent Tutoring Systems (ITS). We added an explanation functionality to the Adaptive CSP (ACSP) applet, an interactive simulation that helps students learn an algorithm for constraint satisfaction problems by providing AI-driven hints adapted to their predicted level of learning. We present the design of the explanation functionality and the results of a controlled study to evaluate its impact on students' learning and perception of the ACPS hints. The study includes an analysis of how these outcomes are modulated by several user characteristics such as personality traits and cognitive abilities, to asses if explanations should be personalized to these characteristics. Our results indicate that providing explanations increase students' trust in the ACPS hints, perceived usefulness of the hints, and intention to use them again. In addition, we show that students' access of the ACSP explanation and learning gains are modulated by three user characteristics, Need for Cognition, Contentiousness and Reading Proficiency, providing insights on how to personalize the ACSP explanations to these traits, as well as initial evidence on the potential value of personalized Explainable AI (XAI) for ITS.

我们的研究是朝着确定XAI中的个性化需求迈出的一步，我们是在调查AI提示和智能辅导系统（ITS）中的反馈的解释价值的背景下这样做的。我们向自适应CSP（ACSP）小程序添加了一种解释功能，这是一种交互式仿真，它通过提供适合其预测学习水平的AI驱动提示来帮助学生学习约束满足问题的算法。我们介绍解释功能的设计和一项对照研究的结果，以评估其对学生学习和感知ACPS提示的影响。这项研究包括对这些结果如何受到几种用户特征（如人格特质和认知能力）的调节的分析，评估说明是否应针对这些特征进行个性化设置。我们的结果表明，提供解释可以增强学生对ACPS提示的信任度，感知的提示有用性以及打算再次使用它们的意图。此外，我们表明，学生对ACSP解释和学习成果的访问受到三个用户特征（认知需求，争执和阅读能力）的调节，从而提供了有关如何个性化这些特征的ACSP解释以及初步证据的见解。个性化可解释AI（XAI）对于ITS的潜在价值。