Development and Validation of an Extended UTAUT-Based Instrument for Measuring Lecturers' Acceptance and Behavioral Intention Toward AI-Based Educational Assessment in Saudi Higher Education

Abstract

The increasing integration of artificial intelligence (AI) into educational assessment has created a need for valid, context-sensitive instruments to measure lecturers' acceptance of and behavioural intention toward AI-based assessment practices. Although existing technology acceptance models provide useful theoretical foundations, many available instruments were developed for general educational technologies and may not adequately capture the professional, ethical, and psychological concerns associated with AI-based assessment. This study aimed to develop and validate an extended Unified Theory of Acceptance and Use of Technology (UTAUT)-based instrument for measuring lecturers' acceptance and behavioural intention toward AI-based educational assessment in Saudi higher education. The instrument was developed based on UTAUT and extended by incorporating three AI-related constructs: Perceived Trust in AI, Perceived Threat to Professional Autonomy, and Job Security Concern. A quantitative instrument-development design was employed. Items were generated through literature review, construct mapping, and expert validation. The instrument comprised sections on demographic profile, technology acceptance, extended AI-related constructs, and behavioural intention. Rasch measurement analysis was used to examine item fit, person and item reliability, separation indices, dimensionality, rating scale functioning, and item hierarchy. The preliminary findings indicated that the instrument demonstrated acceptable psychometric properties, with most items fitting the Rasch model and representing the intended constructs. The validated instrument is expected to provide researchers, university leaders, and policymakers with a diagnostic tool for assessing lecturers' readiness to adopt AI-based educational assessment. This study contributes to educational measurement by providing a theoretically grounded, contextually relevant instrument for future research on AI adoption in higher-education assessment.