Toward Personalized XAI: A Case Study in Intelligent Tutoring Systems

TL;DR

This study explores the importance of personalized explanations in AI-driven intelligent tutoring systems, demonstrating that tailored explanations enhance trust, perceived usefulness, and learning outcomes, influenced by individual user traits.

Contribution

The paper introduces a personalized explanation feature for adaptive hints in an ITS and evaluates its impact, highlighting the role of user characteristics in tailoring XAI.

Findings

Explanations increase trust and perceived usefulness of hints.

Personalization modulates access to explanations and learning gains.

User traits influence the effectiveness of personalized explanations.

Abstract

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 are useful in Intelligent Tutoring Systems (ITS). We added an explanation functionality for the adaptive hints provided by 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 explanation and learning gains are modulated by user characteristics, providing insights toward designing personalized Explainable AI (XAI) for ITS.