Vocational High School Teachers’ Readiness for Electric Vehicle Diagnostic Learning Based on Robotics and Artificial Intelligence: The Role of TPACK, Personal Factors, and Contextual Factors
DOI:
https://doi.org/10.61650/ajme.v3i1.1426Keywords:
Artificial Intelligence, Electric Vehicles, Personal Factors, Teacher Readiness, TPACK, Vocational EducationAbstract
The transformation of automotive technology toward electric vehicles, integrated electronic systems, robotics, and artificial intelligence (AI) requires vocational learning that is increasingly oriented toward data-based diagnosis. Teacher readiness is an important prerequisite for translating technological change into relevant learning experiences in vocational high schools (Sekolah Menengah Kejuruan, SMK). This study aimed to describe the levels of technological pedagogical content knowledge (TPACK), personal factors, contextual factors, and teacher readiness, and to analyze the relationships of these three factors with SMK teachers’ readiness for electric vehicle diagnostic learning based on robotics and AI in Blitar Regency and Blitar City, Indonesia. A quantitative approach with a descriptive-correlational design was employed. The population comprised 100 Light Vehicle Automotive Engineering teachers, and the analytic sample consisted of 80 teachers. Data were collected through a Likert-scale questionnaire and analyzed using descriptive statistics and multiple linear regression. The mean scores were 26.8750 (SD = 5.01990) for TPACK, 24.1750 (SD = 4.08385) for personal factors, 14.4750 (SD = 3.83843) for contextual factors, and 32.4250 (SD = 5.54333) for teacher readiness. The regression model was simultaneously significant, F = 58.731, p < .001. Partially, personal factors were positively and significantly associated with teacher readiness (B = 0.793; t = 6.234; p < .001), whereas TPACK (B = 0.278; t = 1.965; p = .053) and contextual factors (B = 0.146; t = 1.036; p = .303) were not significant at the 5% level. These findings position personal factors as the strongest partial predictor according to the reported t statistics, but they do not demonstrate that technological competence or school support is unimportant. Interpretation should take into account the limitations of the survey design, perception-based measurement, and the departure from linearity for contextual factors. The practical implication is that teacher professional development should combine the strengthening of self-efficacy, hands-on experience, electric vehicle diagnostic training, and measurable institutional support.
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References
Azizah, N. L., Cornelius, C., Eviyanti, A., Liansari, V., Wardani, G., & Diba, N. F. (2025). Digital innovation: Implementation of interactive teaching materials for vocational school teachers to support Merdeka Belajar and SDGs 4. Community Empowerment, 10(2), 236–243. https://doi.org/10.31603/ce.12263
Endot, Z., & Jamaluddin, R. (2023). Antecedent factors influencing teacher’s readiness in teaching design and technology education. Journal of Technical Education and Training, 15(3 Special Issue), 67–78. https://doi.org/10.30880/jtet.2023.15.03.007
Erlitasari, M., Putro, S. C., & Herwanto, H. W. (2025). Hubungan vocational capability dan persepsi terhadap inovasi teknologi dengan adaptasi perubahan dunia kerja di era Revolusi Industri 4.0 pada siswa SMK [The relationship of vocational capability and perceptions of technological innovation with adaptation to changes in the world of work in the Industry 4.0 era among SMK students]. Journal of Electrical Vocational Education and Technology, 8(1), 1–10. https://doi.org/10.21009/jevet.0081.01
Fongkanta, P., & Buakanok, F. S. (2025). School supportiveness and innovative abilities in vocational education: The mediating role of motivation to innovate among vocational teachers. Frontiers in Education, 10, Article 1487560. https://doi.org/10.3389/feduc.2025.1487560
Hossain, M. N., Rahman, M. M., & Ramasamy, D. (2024). Artificial intelligence-driven vehicle fault diagnosis to revolutionize automotive maintenance: A review. Computer Modeling in Engineering & Sciences, 141(2), 951–996. https://doi.org/10.32604/cmes.2024.056022
Kim, J. (2025). Perceptions and preparedness of K–12 educators in adopting generative AI. Research in Learning Technology, 33, Article 3448. https://doi.org/10.25304/rlt.v33.3448
Klimushyn, P., Svitlychny, V., Gnusov, Y., & Onyshchenko, Y. (2025). Automotive electronics and cybersecurity: A systematic review of security attacks and countermeasures. Cybersecurity Education Science Technique, 4(28), 115. https://doi.org/10.28925/2663-4023.2025.28.760
Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), Article 978. https://doi.org/10.3390/su16030978
Peng, R., Abdul Razak, R., & Hajar Halili, S. (2023). Factors influencing in-service teachers’ technology integration model: Innovative strategies for educational technology. PLOS ONE, 18(8), e0286112. https://doi.org/10.1371/journal.pone.0286112
Suhaedin, E., Oriza, W., Ambiyar, A., & Rizal, F. (2024). Analisis dampak program pelatihan guru terhadap kualitas pengajaran di SMK [Analysis of the impact of teacher training programs on teaching quality in vocational high schools]. Journal on Education, 7(1), 3629–3638. https://doi.org/10.31004/joe.v7i1.6959
Yang, X. (2024). Framework of electric vehicle fault diagnosis system based on diagnostic communication. International Journal of Engineering, Transactions B: Applications, 37(6), 1194–1207. https://doi.org/10.5829/ije.2024.37.06c.16
Yue, M., Jong, M. S.-Y., & Ng, D. T. K. (2024). Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Education and Information Technologies, 29(15), 19505–19536. https://doi.org/10.1007/s10639-024-12621-2
Yuniarti, Y., & Walajro, J. (2024). Measuring the impact of principals’ competence and facilities on teachers’ achievement in public vocational high schools in Palembang City: A multivariate analysis. Nusantara Journal of Behavioral and Social Sciences, 3(1), 25–34. https://doi.org/10.47679/202447
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Copyright (c) 2025 Cantika Yunitasari Adri Putri, Alvin Arilian, Ammar Najiibulloh An Nabhan, Ayoub Eka Bekti, Brema Ady Putra Sembiring, Fani Ikko Armalia, Akhmad Zakaria Adi Prasetyo
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