Understanding AI Acceptance and Usage in History Education: An Application of the UTAUT Model Among Malaysian Higher Education Students

This study investigates the acceptance and usage of Artificial Intelligence (AI) in history education among Malaysian higher education students using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Despite AI's growing integration across academic disciplines, its adoption patterns in humanities, particularly history education, remain understudied. Through a survey of 512 history students from four Malaysian universities, this research examines how performance expectancy, effort expectancy, social influence, and facilitating conditions affect AI adoption in history education. Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis revealed that performance expectancy (β = 0.337, p < 0.001), effort expectancy (β = 0.286, p < 0.001), and social influence (β = 0.240, p < 0.001) significantly influence behavioral intention to use AI, while facilitating conditions showed no significant effect on usage behavior. The model explained 43.7% of variance in behavioral intention. These findings extend UTAUT's application to humanities education while providing practical insights for integrating AI tools in history education.