Leveraging AI in vocational education: a systematic review of theory, barriers and opportunities

Purpose This study explores how artificial intelligence (AI) is reshaping vocational education and training (VET) and whether leveraging AI might help refresh the sector’s image. Using the technology acceptance model (TAM), the study examines evidence of its integration in teaching, learning, administration and workforce preparation to understand where AI is already making an impact and where its potential lies. Design/methodology/approach This study uses a systematic literature review following PRISMA guidelines to analyse papers published between 2019 and 2024. Consistent with other systematic reviews on emerging technological fields, a small number of studies met the final criteria, reflecting this field's youth. Key patterns were identified through a six-step inductive thematic analysis. Findings Two major themes emerged: beliefs and attitudes towards AI in VET, and innovative integration of AI in practice. Firstly, students generally recognised its value and said it was easy to use, yet this did not always translate into active engagement. Teachers expressed both enthusiasm and caution, and professional development emerged as a critical need. Secondly, five innovative approaches to AI applications demonstrate AI’s potential to enhance the status of VET: AI qualifications, AI-based qualification frameworks, predictive achievement models, robot-assisted learning and wisdom teaching models. Research limitations/implications This review proposes a TAM-informed way of understanding AI adoption that reflects VET’s specific challenges, including longstanding issues around reputation and status. Practical implications For VET administrators and leaders, the findings point to phased and purposeful application of AI solutions, including using AI to support personalised learning pathways, assist administrative processes and improve learning outcomes. Sustained staff training in technical, ethical and data-related skills appears essential, especially for administrators. Social implications The review highlights the need for policy and regulatory guidance that can support innovation while addressing concerns about data protection, privacy, algorithmic transparency and academic integrity. Originality/value This study extends the TAM for VET by identifying theoretical and practical pathways for AI-enabled transformation in the sector. Drawing on international research employing various methodological approaches, this study offers a framework to close the gap between interest in AI and its practical use. It moves beyond technological determinism to examine how thoughtful AI integration in VET can transform institutional image, pedagogical practices and market positioning to align closely with changing labour markets.