The rapid advancement of Artificial Intelligence (AI) has transformed the automotive industry, particularly in vehicle diagnostics, which increasingly relies on data-driven and intelligent systems. This shift necessitates that students in Automotive Engineering Education not only possess technical skills but also demonstrate AI competence and effective teaching performance. This study aims to analyze the level of AI competence and teaching performance among students in the context of vehicle diagnostic learning. A descriptive survey research design was employed involving 87 students as respondents. Data were collected using structured questionnaires measuring dimensions of AI competence (AI literacy, AI tool utilization, AI-based problem solving, and AI integration) and teaching performance (lesson planning, instructional delivery, classroom interaction, technology use, and learning evaluation). Data were analyzed using descriptive statistics, including mean, percentage, standard deviation, and score categorization. The findings indicate that students generally demonstrate a high level of AI competence, particularly in AI literacy and AI tool utilization, although AI integration remains at a moderate level. Similarly, teaching performance is categorized as high in lesson planning, instructional delivery, and classroom interaction, but relatively lower in technology integration and learning evaluation. These results reveal a gap between technological competence and its pedagogical implementation. In conclusion, while students are technologically prepared, their ability to integrate AI effectively into teaching practices remains limited. The findings highlight the need for strengthening AI-integrated pedagogical competencies and provide an empirical basis for developing AI-TPACK-based learning models in vocational education.
Effendi et al. (Wed,) studied this question.
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