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<p>Engineering drawing is a core subject in vocational education, yet many pre-service vocational teachers face challenges integrating computer-aided design (CAD) tools and emerging technologies into instruction. Generative AI (GenAI) offers new opportunities for creating adaptive learning content and visual representations, but little is known about teachers’ perceptions of its benefits in engineering drawing education. This study examined pre-service vocational teachers’ perceptions of GenAI using the Expectancy-Value Theory (EVT) framework, focusing on perceived benefit of AI (BOA), knowledge of AI (KOA), value of AI (VOA), and cost of AI (COA). Data were collected from 266 pre-service vocational teachers through a validated questionnaire administered after a hybrid Vocational Teacher Development Program (VTDP) integrating CAD, 3D modeling, and ChatGPT-based instructional design tasks. Data analysis comprised four key procedures. Descriptive statistics were used to summarize participant demographics, while independent t-tests assessed differences in perceptions across educational backgrounds. Confirmatory factor analysis was conducted to establish the validity and reliability of the measurement model. Finally, structural equation modeling was employed to examine the hypothesized relationships and mediation effects among the principal constructs. Results indicated no significant differences between participants with vocational and general high-school backgrounds across all constructs. SEM revealed that BOA significantly predicted KOA, VOA, and COA, with KOA partially mediating the relationship between BOA and VOA. These findings highlight the central role of benefit perception in shaping GenAI adoption readiness. Teacher-education programs should embed benefit-driven demonstrations and AI-literacy activities to foster effective and sustainable GenAI integration in vocational training.</p>

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Understanding Pre-Service Vocational Teachers’ Perceptions of GenAI Using ChatGPT Through the Lens of Perceived Benefit

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Andri Setiyawan,
  • Soeharto Soeharto,
  • Tommy Tanu Wijaya,
  • Zsolt Lavicza

摘要

Engineering drawing is a core subject in vocational education, yet many pre-service vocational teachers face challenges integrating computer-aided design (CAD) tools and emerging technologies into instruction. Generative AI (GenAI) offers new opportunities for creating adaptive learning content and visual representations, but little is known about teachers’ perceptions of its benefits in engineering drawing education. This study examined pre-service vocational teachers’ perceptions of GenAI using the Expectancy-Value Theory (EVT) framework, focusing on perceived benefit of AI (BOA), knowledge of AI (KOA), value of AI (VOA), and cost of AI (COA). Data were collected from 266 pre-service vocational teachers through a validated questionnaire administered after a hybrid Vocational Teacher Development Program (VTDP) integrating CAD, 3D modeling, and ChatGPT-based instructional design tasks. Data analysis comprised four key procedures. Descriptive statistics were used to summarize participant demographics, while independent t-tests assessed differences in perceptions across educational backgrounds. Confirmatory factor analysis was conducted to establish the validity and reliability of the measurement model. Finally, structural equation modeling was employed to examine the hypothesized relationships and mediation effects among the principal constructs. Results indicated no significant differences between participants with vocational and general high-school backgrounds across all constructs. SEM revealed that BOA significantly predicted KOA, VOA, and COA, with KOA partially mediating the relationship between BOA and VOA. These findings highlight the central role of benefit perception in shaping GenAI adoption readiness. Teacher-education programs should embed benefit-driven demonstrations and AI-literacy activities to foster effective and sustainable GenAI integration in vocational training.