Design and Evaluation of Trustworthy Knowledge Tracing Model for Intelligent Tutoring System

IF 2.9 3区教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Learning Technologies Pub Date : 2024-03-20 DOI:10.1109/TLT.2024.3403135

Yu Lu;Deliang Wang;Penghe Chen;Zhi Zhang

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引用次数: 0

Abstract

Amid the rapid evolution of artificial intelligence (AI), the intricate model structures and opaque decision-making processes of AI-based systems have raised the trustworthy issues in education. We, therefore, first propose a novel three-layer knowledge tracing model designed to address trustworthiness for an intelligent tutoring system. Each layer is crafted to tackle a specific challenge: transparency, explainability, and accountability. We have introduced an explainable AI (xAI) approach to offer technical interpreting information, validated by the established educational theories and principles. The validated interpreting information is subsequently transitioned from its technical context into educational insights, which are then incorporated into the newly designed user interface. Our evaluations indicate that an intelligent tutoring system, when equipped with the designed trustworthy knowledge tracing model, significantly enhances user trust and knowledge from the perspectives of both teachers and students. This study, thus, contributes a tangible solution that utilizes the xAI approach as the enabling technology to construct trustworthy systems or tools in education.

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智能辅导系统可信知识追踪模型的设计与评估

在人工智能（AI）飞速发展的过程中，基于 AI 的系统错综复杂的模型结构和不透明的决策过程引发了教育领域的可信性问题。因此，我们首先提出了一个新颖的三层知识追踪模型，旨在解决智能辅导系统的可信性问题。每一层的设计都是为了应对一个特定的挑战：透明度、可解释性和问责制。我们引入了一种可解释的人工智能（xAI）方法来提供技术解释信息，并通过既定的教育理论和原则进行验证。经过验证的解释信息随后会从其技术背景转换为教育见解，然后将其纳入新设计的用户界面。我们的评估结果表明，当智能辅导系统配备所设计的可信知识追踪模型时，从教师和学生的角度来看，都能显著增强用户的信任度和知识水平。因此，这项研究提供了一个切实可行的解决方案，利用 xAI 方法作为构建教育可信系统或工具的使能技术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

7.50

自引率

5.40%

发文量

审稿时长

>12 weeks

期刊介绍： The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.