HRMARS - Purpose: This study investigates the relationship between Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) toward educators’ attitude towards Artificial Intelligence (AI) in education, based on the Technology Acceptance Model (TAM). Design/methodology/approach: This study used a quantitative approach with a structured questionnaire given to academicians at Akademi Laut Malaysia, resulting in 29 valid responses. The questionnaire included sections on Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward AI Adoption (ATT), and Demographic Profile, measured on a five-point Likert scale. Data were analyzed using Partial Least Squares–Structural Equation Modelling (PLS-SEM) in SmartPLS 4.0 to test the reliability of the constructs and the relationships between variables, offering insights into how educators’ views on usefulness and ease of use affect their attitudes toward adopting AI in education. Findings: This study applied the Technology Acceptance Model (TAM) to explore factors influencing maritime educators’ attitudes toward adopting Artificial Intelligence (AI) in education at Akademi Laut Malaysia. The findings show that Perceived Usefulness (PU) strongly affects educators’ attitudes, meaning they are more likely to accept AI when they see clear benefits to teaching and learning. However, Perceived Ease of Use (PEOU) did not have a significant effect, suggesting that how easy AI is to use is less important in this context. Overall, the results confirm that TAM is a useful model for understanding AI adoption in Maritime Education and Training (MET). Research limitations/implications: This study is limited by its narrow model scope, focusing only on the core TAM constructs; Perceived Usefulness and Perceived Ease of Use without including contextual factors such as trust, perceived risk, or organizational support that may better explain AI adoption in maritime education settings. Practical implications: This study does not include real-world factors such as trust, perceived risk, and organizational support, limiting its practical relevance for policymakers and institutions implementing AI in maritime education. Originality/value: This study contributes original insights by applying the Technology Acceptance Model (TAM) within the context of maritime education, a specialized and safety-critical field where research on AI adoption remains limited. It provides empirical evidence on how maritime educators perceive the usefulness and ease of use of AI, offering valuable understanding of the factors that influence technology acceptance in Maritime Education and Training (MET). The findings can guide policymakers and institutions in designing targeted strategies, training programs, and policies to support effective AI integration in maritime teaching and learning environments.
Suhaimi et al. (2026) studied this question.