DEVELOPING AN EVALUATION INSTRUMENT BASED ON THE TECHNOLOGY ACCEPTANCE MODEL FOR TEACHERS' ARTIFICIAL INTELLIGENCE READINESS

Hari Din Nugraha; Agung Gumelar; Tri Bambang AK; Rani Anggrainy; Muhamad Habib Alfin Nurahman; Diandra Keisha Gunadi

doi:10.17509/jmee.v12i2.88890

DEVELOPING AN EVALUATION INSTRUMENT BASED ON THE TECHNOLOGY ACCEPTANCE MODEL FOR TEACHERS' ARTIFICIAL INTELLIGENCE READINESS

Hari Din Nugraha, Agung Gumelar, Tri Bambang AK, Rani Anggrainy, Muhamad Habib Alfin Nurahman, Diandra Keisha Gunadi

Abstract

In Indonesia, only 48% of vocational teachers demonstrate limited readiness to integrate Artificial Intelligence into learning. Addressing this issue requires a reliable instrument to evaluate prospective teachers' preparedness. This study developed and validated an instrument based on the Technology Acceptance Model to assess the digital competencies of Mechanical Engineering pre-service teachers in Indonesian vocational education. The research quantitatively evaluated four constructs: Perceived Usefulness, Perceived Ease of Use, Behavioral Intention, and Self-Efficacy. Involving 100 respondents from a community service program, the development followed three stages: preparation, expert content validation using Aiken’s V, and construct validation via Exploratory Factor Analysis. Results confirmed unidimensional structures for all constructs. Perceived Usefulness and Perceived Ease of Use showed strong validity with high eigenvalues (4.984 and 5.063) and explained variance (49.8% and 50.6%). The Kaiser-Meyer-Olkin measure confirmed sampling adequacy (0.833–0.909). Aiken’s V analysis indicated high content (0.83), construct (0.81), and linguistic validity (0.85). Key findings highlight deficiencies in Artificial Intelligence tool proficiency, emphasizing the need for curriculum alignment with Industry 4.0 demands. This study provides a validated tool to guide teacher training and policy, effectively addressing the digital divide in vocational education and bridging a critical gap between theory and practice.

Keywords

Instrument; Technology Acceptance Model (TAM); Exploratory Factor Analysis (EFA).

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References

Ambarwati, E. K., Dewi, I. P., Puspitaloka, N., Utami, P. P., & Ahmad, Y. B. (2023). Penguatan kompetensi profesional guru dalam integrasi teknologi pembelajaran. Jurnal Pengabdian Masyarakat Berkemajuan, 7(1), 1–8. https://doi.org/10.31764/jpmb.v7i4.17626

Dalawi, I., Isa, M. R., & Aimran, N. (2025). Exploratory factor analysis on the development and validation of the understanding, attitude, practice and health literacy questionnaire on COVID-19 in Malay language. Scientific Reports, 15, Article 19654. 10.1038/s41598-025-04517-z

Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), pp.319-340. DOI: 10.2307/249008

Hair, J.F., Black, W.C., Babin, B.J. & Anderson, R.E. (2019), Multivariate data analysis, 8th edn, Cengage, Andover. ISBN: 978-1473756540

Hartono, H., Hidayat, H., & Kurniawan, D. (2021). Digital literacy among prospective vocational teachers: A case study in mechanical engineering education. Journal of Technical Education and Training, 13(2), 45–60. https://doi.org/10.30880/jtet.2021.13.02.004

Huang, R., Tlili, A., Chang, T.-W., Yang, J., & Liu, D. (2022). Applying the Technology Acceptance Model (TAM) to assess vocational teachers’ adoption of digital learning tools: A systematic review. Educational Technology Research and Development, 70(3), 1021–1045. https://doi.org/10.1007/s11423-022-10102-2

Ifinedo, E., & Kankaanranta, M. (2023). Understanding the influence of context in technology integration from teacher educators’ perspective. Technology, Pedagogy and Education, 30(2), 201–215. https://doi.org/10.1080/1475939X.2020.1867231

Karimian, Z., & Chahartangi, F. (2024). Development and validation of a questionnaire to measure educational agility: a psychometric assessment using exploratory factor analysis. BMC medical education, 24(1), 1284. https://doi.org/10.1186/s12909-024-06307-z

Lase, D., Waruwu, E., Waruwu, S. (2022). Integrasi TIK dan Pengembangan Kompetensi Digital Guru Prajabatan di Perguruan Tinggi. LAURU Jurnal Ilmiah Pendidikan Ekonomi, 1(1):13-20. https://doi.org/10.56207/lauru.v1i1.12

Paryanto, P., Rahayu, R., Sutrisno, S., & Arifin, A. (2022). Career preferences of mechanical engineering graduates: a survey of Indonesian polytechnics. Journal of Vocational Education and Training, 74(3), 456–475. https://doi.org/10.1080/13636820.2021.1890315

Sahputra, M. A., Ikhsanudin, I., & Abdillah, H. (2023). Studi pengetahuan guru SMK teknik mesin di Kota Serang. Faktor: Jurnal Ilmiah Kependidikan, 10(2), 270–283. https://doi.org/10.30998/fjik.v10i2.

Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, Vol. 128,13–35. ISSN 0360-1315. https://doi.org/10.1016/j.compedu.2018.09.009.

Sigudla, J., & Maritz, J. E. (2023). Exploratory factor analysis of constructs used for investigating research uptake for public healthcare practice and policy in a resource-limited setting, South Africa. BMC Health Services Research, 23:1423. https://doi.org/10.1186/s12913-023-10165-8

Siryono, R., Santosa, B., Susatya, E. (2023). The Effect of Using a CNC Simulator in Learning the Mechanical Engineering Skills Program. International Journal of Social health, 2(10):799-815. https://doi.org/10.58860/ijsh.v2i11.117

Sobandi, A., Yuniarsih, T., Meilani, R. I., Supardi, E., Indriarti, R., & Faldesiani, R. (2022). Penerapan model TAM dalam menganalisis kesiapan guru SMK untuk mengimplementasikan pendekatan micro-learning. Journal of Business Management Education, 6(3), 321–334. https://doi.org/10.17509/jbme.v6i3.

Sufa, A. R., & Yunus, Y. (2023). Analisis faktor yang mempengaruhi minat menjadi guru pada mahasiswa Program Studi Pendidikan Teknik Mesin Universitas Negeri Surabaya. Jurnal Pendidikan Teknik Mesin, 13(2), 200–205. https://doi.org/10.26740/jptm.v13n2.p200-205

UNESCO. (2023). Global education monitoring report 2023: Technology in education. Paris, France: UNESCO Publishing.

United Nations Development Programme. (2023). Digital readiness assessment. New York: UNDP. https://www.undp.org/publications/digital-readiness-assessment

Wahyuni, E., Hidayati, D., & Romanto, R. (2022). Kesiapan guru terhadap pembelajaran berbasis teknologi. Jurnal Pendidikan dan Konseling (JPDK), 4(6), 11238–11247. https://doi.org/10.31004/jpdk.v4i6.

Wang, K., Ma, G., Ma, Y., Feng, B., & Ai, B. (2024). Spectrum Sensing based on the Difference between the Maximum Eigenvalue and the Geometric Mean of the Eigenvalue. IEEE Transactions on Vehicular Technology, 73(9), 13340-13351. https://ieeexplore.ieee.org/abstract/document/10508470/citations

Widayanti, T. (2020). Use of Google Form in Support of Data Collection for Student Scientific Work. Jurnal Inovasi Pegabdian Kepada Masyarakat, 1(1). 10.30700/jm.v1i1.1015

Widodo, A., Firmansyah, R., & Yulianto, A. (2022). Content validity threshold for Industrial 4.0 competency instruments in Indonesian vocational schools. International Journal of Evaluation and Research in Education, 11(3), 1321–1330. https://doi.org/10.11591/ijere.v11i3.

World Economic Forum. (2020). Schools of the Future: Defining New Models of Education for the Fourth Industrial Revolution. Geneva, Switzerland: World Economic Forum. https://www3.weforum.org/docs/WEF_Schools_of_the_Future_Report_2019.pdf

Yuara, Y. P., Rizal, F., & Kusumaningrum, I. (2019). Kesiapan guru vokasi SMKN 1 Sumatera Barat dalam menghadapi era Revolusi Industri 4.0. Jurnal Ilmiah Pendidikan dan Pembelajaran, 3(3), 280–288. https://doi.org/10.23887/jipp.v3i3

Zhang, Z., Sangsawang, T., Vipahasna, K., & Pigultong, M. (2024). A mixed-methods data approach integrating importance-performance analysis (IPA) and Kaiser-Meyer-Olkin (KMO) in applied talent cultivation. Journal of Applied Data Sciences, 5(1), 256-267. https://bright-journal.org/Journal/index.php/JADS/article/download/170/148

DOI: https://doi.org/10.17509/jmee.v12i2.88890