<![CDATA[Purpose – This study aims to develop and evaluate a Generative AI-integrated Vibe Coding approach for producing an interactive web-based electrical-system simulator, addressing automotive vocational students' lack of programming skills for independently creating digital learning media, and to examine its feasibility, students' practicality responses, and its effect on cognitive learning outcomes. Methods – A Research and Development approach following the ADDIE model was used with a one-group pretest-posttest design. Participants were 32 D4 Automotive Engineering students at Universitas Negeri Makassar. The simulator was validated by four experts (two media experts, two content experts) and evaluated through pretest-posttest cognitive tests analyzed with the Normalized Gain (N-Gain) score and a paired-samples t-test. Findings – The simulator was rated Highly Feasible by experts (91.20% media, 89.50% content) and Very Good by students (90.75% practicality). Cognitive scores increased from a mean of 54.37 (pretest) to 86.87 (posttest), yielding a high-effectiveness N-Gain of 0.71. The paired-samples t-test confirmed a statistically significant improvement, t(31) = 19.42, p < .001, 95% CI [29.09, 35.91], Cohen's d = 3.43. Research implications – These findings offer preliminary conceptual implications for reframing the technological-knowledge dimension of TPACK around prompt engineering rather than manual coding syntax, positioning vibe coding as a potential bridge between domain expertise and digital media production for non-programmer vocational learners. This reconceptualization is tentative and requires confirmatory studies using controlled designs and dedicated instruments before it can be established empirically. Originality – This study is among the first empirical investigations in Indonesia to apply and test a vibe coding approach within a vocational higher-education curriculum for automotive engineering, extending prior vibe-coding research beyond professional software engineering and clinical education into technical vocational fields where learners are domain experts but programming novices.]]>
