The Influence of Instructional Leadership on the Use of Artificial Intelligence Applications among Vocational College Lecturers in the Sarawak Zone: A Systematic Literature Review

Abstract

The integration of artificial intelligence (AI) has transformed teaching and learning practices, particularly within technical and vocational education and training (TVET). Despite its potential to enhance instructional effectiveness, assessment accuracy, and skills-based learning, the adoption of AI among vocational college lecturers remains uneven and highly context-dependent. Instructional leadership has been increasingly recognised as a key factor influencing educators’ readiness and willingness to integrate emerging technologies; however, empirical evidence on its role in AI adoption within vocational education remains fragmented.

This study presents a systematic literature review (SLR) examining the influence of instructional leadership on the use of AI applications among vocational college lecturers, with particular relevance to the Sarawak Zone and comparable TVET contexts. Using a structured review protocol aligned with PRISMA principles, peer-reviewed journal articles published between 2019 and 2025 were systematically identified, screened, and synthesised from major academic databases. The review focuses on studies addressing instructional leadership practices, AI adoption, technology acceptance, and lecturers’ self-efficacy.

The findings indicate that instructional leadership influences AI adoption through direct mechanisms, such as vision setting, instructional support, and professional development, as well as indirect mechanisms involving perceived usefulness, ease of use, self-efficacy, and organisational support. Technological infrastructure and institutional readiness further moderate this relationship. Notably, the review reveals a significant lack of context-specific studies focusing on vocational colleges in peripheral and rural regions, including Sarawak. This SLR consolidates existing evidence and identifies critical research gaps to inform leadership practices, institutional policies, and future research on sustainable AI integration in TVET.