Relationship of Technology Acceptance and Behavioural Intention Toward AI-Based Educational Assessment among Lecturers in Saudi Arabia

Abstract

The increasing use of artificial intelligence (AI) in educational assessment has intensified the need to understand the factors that shape lecturers’ willingness to adopt AI-supported assessment practices. While prior studies have widely examined technology acceptance in education, fewer studies have focused specifically on the relationship between technology acceptance and behavioural intention toward AI-based educational assessment among lecturers in Saudi higher education. This study examined the extent to which technology acceptance constructs predict lecturers’ behavioural intention to use, continue using, and recommend AI-based educational assessment tools. A quantitative cross-sectional survey design was employed using an extended UTAUT-based questionnaire administered to lecturers in Saudi higher education institutions. The result was based on 412 usable responses. Descriptive statistics, Pearson correlation, and hierarchical multiple regression were used to examine the relationships among performance expectancy, effort expectancy, social influence, facilitating conditions, perceived trust in AI, perceived threat to professional autonomy, job security concern, and behavioural intention. The results indicated that performance expectancy, perceived trust in AI, facilitating conditions, effort expectancy, and social influence were positively associated with behavioural intention, while perceived threat to professional autonomy and job security concern were negatively associated with behavioural intention. The hierarchical regression model explained 65.2% of the variance in behavioural intention, with performance expectancy and perceived trust in AI emerging as the strongest positive predictors. The findings suggest that lecturers’ intention to adopt AI-based assessment is shaped not only by perceived usefulness and institutional support, but also by trust and professional concerns related to academic judgment and job relevance. The study contributes to technology acceptance research by offering a relationship-focused explanation of AI-based assessment adoption in Saudi higher education.