Factors Affecting Lecturers’ Intention to Adopt Artificial Intelligence Tools for Assessing Students’ Performance in Malaysian Universities

Abstract

Empowering the student assessment process in higher education through artificial intelligence has become essential for delivering more adaptive, accurate, and equitable evaluations in a rapidly evolving educational landscape. As Malaysia seeks to integrate such technology into its assessment practices, lecturers’ support and willingness are essential for successful implementation. Thus, this research investigates the factors influencing lecturers’ intentions to adopt AI-based assessment tools in Malaysian universities by extending the meta-analysis based Modified Unified Theory of Acceptance and Use of Technology (meta-UTAUT). Data were collected from 414 lecturers in the Klang Valley and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results reveal that performance expectancy, effort expectancy, self-efficacy, trust, compatibility, and personal innovativeness influence lecturers’ attitudes. For adoption intention, performance expectancy, self-efficacy, personal innovativeness, and attitude are key predictors. Moreover, lecturers’ attitudes mediate the relationships between performance expectancy, effort expectancy, self‑efficacy, trust, compatibility, and personal innovativeness and their intention. This study offers novel insights into AI-based assessment adoption and represents the first empirical application of the meta-UTAUT model in this context. From a practical perspective, the findings provide actionable guidance for policymakers, administrators, and developers to support the effective implementation of these tools in Malaysian universities.