The Influence of Instructional Leadership on the Use of Artificial Intelligence Applications among Vocational College Lecturers in Sarawak Zone

Abstract

The integration of artificial intelligence (AI) in technical and vocational education and training (TVET) has gained increasing attention due to its potential to enhance instructional effectiveness. However, the level of AI application usage among vocational college lecturers remains uneven, particularly in less developed regions. This study examines the influence of instructional leadership on the use of AI applications among vocational college lecturers in the Sarawak Zone, guided by an integrated framework incorporating Instructional Leadership Theory, Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Self-Efficacy Theory. A quantitative cross-sectional design was employed involving 120 lecturers. Data were collected using a structured questionnaire and analysed using descriptive and inferential statistics. The findings indicate that instructional leadership, technology acceptance, and self-efficacy significantly influence AI application usage, with technology acceptance emerging as the strongest predictor. The model explains a substantial proportion of variance in AI usage, highlighting the combined role of leadership and psychological factors. The study provides empirical support for an integrated perspective of AI adoption, emphasising that effective AI integration in vocational education is shaped not only by technological factors but also by leadership practices and lecturers’ readiness.