AI Integration in TVET: Examining Educators’ Behavioral Intentions and the Moderating Role of Experience

Nadzirah Muhammad Merejok, Sri Fatiany Abdul Kader Jailani, Syukrina Alini Mat Ali and Noor’ain Mohamad Yunus

Information Management and Business Review, 2025, vol. 17, issue 2, 404-414

Abstract: Artificial Intelligence (AI) is a branch of contemporary science and information technology that aims to replicate human-like intelligence in machines. Its application has increasingly become a significant component in modern education, transforming the way knowledge is accessed and delivered. Educators are now required to recognize AI technology as a pivotal tool in both current and future educational environments. In light of this, the present study investigates the predictors of behavioral intention, namely, performance expectancy, effort expectancy, social influence, and facilitating conditions, towards the use of AI technology among lecturers in Polytechnics and Community Colleges (POLYCC) in Malaysia. This research is grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Employing a simple random sampling method to ensure equal representation, data were collected from 142 academic staff members in POLYCC through an online questionnaire. The data were analyzed using SPSS to test the proposed research model and hypotheses. Findings revealed that performance expectancy, effort expectancy, and facilitating conditions significantly and positively influence lecturers’ behavioral intention to use AI. In contrast, social influence did not have a significant effect. Additionally, the study identified experience as a moderating variable, which significantly strengthened the relationships between effort expectancy, social influence, and facilitating conditions with behavioral intention. These results highlight the importance of considering educators’ experience levels when designing strategies for AI adoption in teaching and learning environments.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:rnd:arimbr:v:17:y:2025:i:2:p:404-414

DOI: 10.22610/imbr.v17i2(I)S.4617

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