Exploring the Adoption of Generative Artificial Intelligence by TVET Students: A UTAUT Analysis of Perceptions, Benefits, and Implementation Challenges

Abstract

Background: This research investigates the perceptions, benefits, and challenges of generative artificial intelligence (AI) tools among students of Technical and Vocational Education and Training (TVET). A sample of 200 students from various institutions in Malaysia including Technical and Vocational Education and Training (TVET) institutions and universities in the fields of engineering, information technology, business studies and hospitality were surveyed for this study. You were selected for this study due to your experience with generative AI in your academic and real-world learning experiences. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study addresses whether performance expectancy, effort expectancy, social influence and facilitating conditions are key factors in students' intentions to adopt and use generative AI. The findings demonstrate the importance of generative AI in improving TVET education but also spotlight challenges to its mainstream implementation. The study then draws recommendations for educators and policymakers, on how to ensure informed and effective AI use in TVET settings based on these findings.
Objectives: The purpose of this research was to investigate the factors affecting the implementation of Generative AI among TVET students using UTAUT model, its advantages and disadvantages, as well as the role of institutional support and ethical concerns such as plagiarism and data privacy.
Methods: Quantitative survey approach applied to collect comprehensive data on the adoption of generative AI among TVET students. A total of 200 students across multiple disciplines, including engineering, IT, and hospitality participated in this study. The participants were selected based on their exposure on generative AI tools to ensure relevance in assessing adoption factors. Data collection was conducted through a structured questionnaire based on the UTAUT model, covering constructs key such as performance expectancy, effort expectancy, social influence, and facilitating conditions. The survey aimed to capture students’ perceptions, experiences, and challenges related to AI adoption in their fields of study. Additionally, regression technique was used to analysed the data and identify relationships between UTAUT constructs and adoption behaviour.
Results: The findings of the study focus on the validation of the UTAUT constructs and the analysis of survey responses. The descriptive statistics (mean, standard deviation) and inferential statistics (correlation, regression analysis) were applied to understand the impact of various factors on AI adoption.
Conclusions: This study highlights the potential of generative AI tools to change the landscape of TVET education. These tools can make the learning experience richer by improving the learning efficiency, enhancing creativity and improving problem-solving skills. Nevertheless, successful adoption is contingent on overcoming major obstacles, including technological literacy gaps, institutional support, and ethical considerations. The statistical analysis showed that performance expectancy and facilitating conditions were significant determinants of students' behavioral intention to adopt AI. It further stresses the importance of organized policies and training programs to promote responsible AI use. They can help facilitate an environment where generative AI can be maximized for TVET, ultimately ensuring that students are being equipped with essential digital skills necessary for a technology-driven future.