Intelligent tutoring systems and learning performance: applying task-technology fit and IS success model

Abstract

Purpose Intelligent tutoring systems (ITS) are a supplemental educational tool that offers great benefits to students and teachers. The systems are designed to focus on an individual’s characteristics, needs and preferences in an effort to improve student outcomes. Despite the potential benefits of such systems, little work has been done to investigate the impact of ITS on users. To provide a more nuanced understanding of the effectiveness of ITS, the purpose of this paper is to explore the role of several ITS parameters (i.e. knowledge, system, service quality and task–technology fit (TTF)) in motivating, satisfying and helping students to improve their learning performance.

Design/methodology/approach Data were obtained from students who used ITS, and a structural equation modeling was deployed to analyze the data.

Findings Data analysis revealed that the quality of knowledge, system and service directly impacted satisfaction and improved TTF for ITS. It was found that TTF and student satisfaction with ITS did not generate higher learning performance. However, student satisfaction with ITS did improve learning motivation and resulted in superior learning performance. Data suggest this is due to students receiving constant and constructive feedback while simultaneously collaborating with their peers and teachers.

Originality/value This study verifies that there was a need to assess the benefits of ITS. Based on the study’s findings, theoretical and practical implications are proposed.