Personalizing explanations of AI-driven hints to users' characteristics: an empirical evaluation

TL;DR

This study evaluates personalized AI-generated hint explanations tailored to students' traits, demonstrating increased engagement, understanding, and learning in an educational setting.

Contribution

It provides empirical evidence that personalizing explanations based on student traits enhances engagement and learning outcomes in intelligent tutoring systems.

Findings

Personalized explanations increased student interaction with hints.

Personalization improved students' understanding of hints.

Learning outcomes were positively affected by tailored explanations.

Abstract

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.