Explaining Problem Recommendations in an Intelligent Tutoring System
Pages 291 - 299
Abstract
Students learning with intelligent tutoring systems (ITS) do not always trust system recommendations. One solution for this is explainable AI (XAI), which is shown to increase trust in AI. Our research focuses on how students’ personality traits affect their interactions with XAI, and how XAI affects students’ trust and actions in an ITS. We evaluated this by adding XAI to SQL-Tutor and conducting a pilot study with 15 participants from an introductory database course. We found that personality traits affect students’ interactions with XAI, and that students engaging with XAI trust the system more.
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Jun 2024
449 pages
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 10 June 2024
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