Explaining Problem Recommendations in an Intelligent Tutoring System

被引：0
|
作者：
Clarke, April [1 ]
Mitrovic, Antonija [1 ]
机构：
[1] Univ Canterbury, Dept Comp Sci & Software Engn, Christchurch, New Zealand
来源：
GENERATIVE INTELLIGENCE AND INTELLIGENT TUTORING SYSTEMS, PT I, ITS 2024 | 2024年 / 14798卷
关键词：
Intelligent tutoring system; SQL-Tutor; explainable; artificial intelligence; problem selection; EXPLANATIONS;
D O I：
10.1007/978-3-031-63028-6_23
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：291 / 299
页数：9
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