Evaluation of Readiness among Prospective Mechanical Engineering Vocational Teachers to Apply Generative AI in Education Using Technology Acceptance Model (TAM)

Abstrak

This study evaluates the readiness of prospective vocational school teachers in integrating Generative Artificial Intelligence (GenAI) into learning using the Technology Acceptance Model (TAM) framework, which includes Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Behavioral Intention (BI), and Self-Efficacy (SE). The research employs a descriptive quantitative design with 100 respondents who are prospective vocational high school mechanical engineering teachers. The instrument consists of 30 items (Likert scale 1–5) and was validated by three validators using Aiken’s V (content = 0.90; construct = 0.88; language = 0.89; > 0.75). The results showed that PU (mean = 4.01; SD = 0.77) was the highest, followed by BI (mean = 3.93; SD = 0.92) and PEOU (mean = 3.89; SD = 0.82), while SE (mean = 3.66; SD = 0.95) was the lowest but relatively. Item-level analysis indicated strengths in perceptions of industry relevance and AI-assisted learning planning, as well as the need for strengthening technical skills and troubleshooting. These findings support the urgency for vocational curriculum development emphasizing AI literacy, pedagogical-technological training, and industry collaboration.