Perceived Usefulness, Perceived Ease of Use, and Self-‎Reported Critical Thinking Among Indonesian Vocational ‎Students: The Indirect Role of Self-Regulated Learning

  • Fani Okta Srinawati Universitas Negeri Padang, Indonesia
  • Rose Rahmidani Universitas Negeri Padang, Indonesia
Keywords: Artificial intelligence, critical thinking, perceived ease of use, self-regulated learning, vocational education

Abstract

The spread of generative artificial intelligence (AI) in vocational classrooms has raised a concern that easy, on-demand answers may displace the reasoning vocational graduates are expected to master. This cross-sectional survey examined whether students' perceptions of AI are associated with their self-reported critical thinking, and whether self-regulated learning accounts for any such association. Drawing on social cognitive theory and the Technology Acceptance Model, questionnaire data were collected from 365 students in nine state vocational schools (SMK) in Pesisir Selatan Regency, West Sumatra, and analysed with PLS-SEM. All four constructs, including critical thinking, were measured by self-report rather than by performance tasks. Perceived usefulness and perceived ease of use were not associated with critical thinking directly (β = -.026, 95% CI [-.163, .111]; β = .064, 95% CI [-.072, .200]) but were weakly associated with self-regulated learning (β = .161; β = .158), which was in turn the strongest correlate of critical thinking (β = .383, 95% CI [.284, .482]). Both indirect paths were small and positive (β = .062 and .061), a configuration indicating indirect-only mediation. Explanatory power was weak, with R² = .085 for self-regulated learning and .157 for critical thinking, and f² values for all four technology paths below .02. A clustering sensitivity analysis showed that the technology paths lose significance at an intraclass correlation above about .01, so these associations are provisional. Because all constructs were measured on one occasion, the ordering is a theoretical assumption rather than an observed sequence.

References

Akmal, Ambiyar, Usmeldi, & Fadillah, R. (2025). Developing and assessing the impact of an integrated STEM ‎project-based learning model in vocational education for enhanced competence and employability. ‎Salud, Ciencia y Tecnología, 5. https://doi.org/10.56294/saludcyt20251786‎

Armiati, Susanti, D., Dalimunthe, H. L., & Rahmidani, R. (2026). From emotional intelligence and digital ‎mindset to academic achievement: The mediating role of self-regulated learning and the moderating ‎effects of technology acceptance and digital resilience. Journal of Pedagogical Research. ‎https://doi.org/10.33902/jpr.202639628‎

Aulia, N. S., & Marsasi, E. G. (2024). The role of perceived usefulness, perceived ease of use, and task ‎technology fit to increase perceived impact on learning. Sentralisasi, 13(1), 163–181. ‎https://doi.org/10.33506/sl.v13i1.3031‎

Chetradevee, S. L., Xavier, K. A., & Jayapandian, N. (2022). Artificial intelligence technological revolution in ‎education and space for next generation. In H. Sharma, V. Shrivastava, K. Kumari Bharti, & L. Wang ‎‎(Eds.), Communication and intelligent systems. Springer. https://doi.org/10.1007/978-981-19-2130-‎‎8_30‎

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods ‎approaches. SAGE Publications.‎

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

Entwistle, J., Smith, R., & Moore, T. (2025). Cognitive coaching and teacher efficacy: Evidence from school-‎based mentoring. Professional Development in Education, 51(2), 233–248. ‎https://doi.org/10.1080/17521882.2025.2570687‎

Esiyok, E., Gokcearslan, S., & Kucukergin, K. G. (2025). Acceptance of educational use of AI chatbots in the ‎context of self-directed learning with technology and ICT self-efficacy of undergraduate students. ‎International Journal of Human–Computer Interaction, 41(1), 641–650. ‎https://doi.org/10.1080/10447318.2024.2303557‎

Gonida, E. N., Karabenick, S. A., Stamovlasis, D., Metallidou, P., & Greece, C. T. Y. (2018). Help seeking as a ‎self-regulated learning strategy and achievement goals: The case of academically talented ‎adolescents. High Ability Studies, 30(1–2), 147–166. ‎https://doi.org/10.1080/13598139.2018.1535244‎

Hair, J. F., Jr., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated ‎guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. ‎https://doi.org/10.1504/IJMDA.2017.087624‎

Hanafi, W. N. W., & Toolib, S. N. (2020). Influences of perceived usefulness, perceived ease of use, and ‎perceived security on intention to use digital payment: A comparative study among Malaysian ‎younger and older adults. International Journal of Business Management, 3(1), 15–24.‎

Harianto, A., Buwani, Faradina, E., & Tuwoso. (2025). Factors affecting work readiness of vocational school ‎graduates: A systematic literature review. International Journal of Studies in International Education, ‎‎2(2), 90–106. https://doi.org/10.62951/ijsie.v2i2.281‎

Irfan, D., Watrianthos, R., & Yunus, F. A. N. B. (2025). AI in education: A decade of global research trends and ‎future directions. International Journal of Modern Education and Computer Science, 17(2), 135–153. ‎https://doi.org/10.5815/ijmecs.2025.02.07‎

Jeilani, A., & Abubakar, S. (2025). Perceived institutional support and its effects on student perceptions of AI ‎learning in higher education: The role of mediating perceived learning outcomes and moderating ‎technology self-efficacy. Frontiers in Education, 10, Article 1548900. ‎https://doi.org/10.3389/feduc.2025.1548900‎

Jeong, S., Kim, S., & Lee, S. (2024). Effects of perceived ease of use and perceived usefulness of technology ‎acceptance model on intention to continue using generative AI: Focusing on the mediating effect of ‎satisfaction and moderating effect of innovation resistance. In Conceptual modeling (pp. 99–106). ‎Springer. https://doi.org/10.1007/978-3-031-75599-6_7‎

Jia, W., & Huang, X. (2023). Digital literacy and vocational education: Essential skills for the modern ‎workforce. International Journal of Academic Research in Business and Social Sciences, 13(5). ‎https://doi.org/10.6007/ijarbss/v13-i5/17080‎

Kholifah, N., Kurdi, M. S., Nurtanto, M., Mutohhari, F., Fawaid, M., & Subramaniam, T. S. (2023). The role of ‎teacher self-efficacy on the instructional quality in 21st century: A study on vocational teachers, ‎Indonesia. International Journal of Evaluation and Research in Education, 12(2), 998–1006. ‎https://doi.org/10.11591/ijere.v12i2.23949‎

Kock, N., & Dow, K. E. (2025). Statistical significance and effect size tests in SEM: Common method bias and ‎strong theorizing.‎

Le, S. K. (2022). 21st-century competences and learning that technical and vocational training must develop: ‎Focus 4C skills. Journal of Engineering Researcher and Lecturer, 1(1), 1–6.‎

Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of ‎generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects ‎from a survey of knowledge workers. Proceedings of the 2025 CHI Conference on Human Factors in ‎Computing Systems, 1–22. https://doi.org/10.1145/3706598.3713778‎

Martínez-Plumed, F., Gómez, E., & Hernández-Orallo, J. (2021). Futures of artificial intelligence through ‎technology readiness levels. Telematics and Informatics, 58, Article 101525. ‎https://doi.org/10.1016/j.tele.2020.101525‎

Mohammad, F., Ladin, C. A., & Shaharom, M. S. N. (2026). A recent systematic review of technological ‎advancements in art education research. Journal of Advanced Research in Applied Sciences and ‎Engineering Technology, 58(1), 145–174. https://doi.org/10.37934/araset.58.1.145174‎

Ninghardjanti, P., Umam, M. C., Subarno, Winarno, Langgi, N. R., & Widodo, J. (2025). Evaluating the impact ‎of AI on the critical thinking skills among the higher education students by combining the TAM model ‎and critical thinking theory. Frontiers in Education, 10, Article 1719625. ‎https://doi.org/10.3389/feduc.2025.1719625‎

Parma Dewi, I., Aditya Fiandra, Y., Fadillah, R., Marta, R., Rosalina, L., Azima Noordin, N., & Khan, D. (2025). ‎Explaining VR/AR learning in medical education: A comparative PLS-SEM analysis of TAM, SDT, TTF, ‎and flow theory. Seminars in Medical Writing and Education, 4, Article 799. ‎https://doi.org/10.56294/mw2025799‎

Rahmiati, Jalinus, N., Effendi, H., Fadillah, R., & Wulansari, R. E. (2024). Assessing the impact of a STEM ‎learning project model on artificial intelligence education in higher learning institutions. Data and ‎Metadata, 3. https://doi.org/10.56294/dm2024.623‎

Riyanda, A. R., Parma Dewi, I., Jalinus, N., Ahyanuardi, Sagala, M. K., Rinaldi, D., Prasetya, R. A., & Yanti, F. ‎‎(2025). Digital skills and technology integration challenges in vocational high school teacher learning. ‎Data and Metadata, 4. https://doi.org/10.56294/dm2025553‎

Saftari, R., Mulyadi, H., & Supardi, E. (2025). Tracing the role of artificial intelligence in self-regulated ‎learning: A systematic review using the Winne and Hadwin framework (2007–2025). JP (Jurnal ‎Pendidikan): Teori dan Praktik, 10(1), 78–92. https://doi.org/10.26740/jp.v10n1.p78-92‎

Singh, S. V., & Hiran, K. K. (2022). The impact of AI on teaching and learning in higher education technology. ‎Journal of Higher Education Theory and Practice, 22(13). ‎https://doi.org/10.33423/jhetp.v22i13.5514‎

Siregar, Y. S., Daryanto, E., & Siman, S. (2023). Increasing the competence of vocational education teachers ‎with 4C skills-based training management: Critical thinking, creativity, communication, collaboration. ‎https://doi.org/10.4108/eai.19-9-2023.2340493‎

Sukmawati, F., Prihatin, R., & Santosa, E. B. (2024). Design and evaluation a mobile augmented reality to ‎enhance critical thinking skills for vocational high schools. Salud, Ciencia y Tecnología, 4. ‎https://doi.org/10.56294/saludcyt2024.1000‎

Sutanto, J. E., Sukardi, D., & Christiani, N. (2021). Competency based training entrepreneurship to improve ‎student’s entrepreneur mentality: Case study in East Java, for vocational high school graduates. ‎International Journal of Economics, Business and Management Research, 5(10), 28–36.‎

Tan, P. S. H., Seow, A. N., Choong, Y. O., Tan, C. H., Lam, S. Y., & Choong, C. K. (2024). University students’ ‎perceived service quality and attitude towards hybrid learning: Ease of use and usefulness as ‎mediators. Journal of Applied Research in Higher Education, 16(5), 1500–1514. ‎https://doi.org/10.1108/JARHE-03-2023-0113‎

Waliyuddin, D. S., & Sulisworo, D. (2022). High order thinking skills and digital literacy skills instrument test. ‎Ideguru: Jurnal Karya Ilmiah Guru, 7(1). https://doi.org/10.51169/ideguru.v7i1.310‎

Wang, K., Cui, W., & Yuan, X. (2025). Artificial intelligence in higher education: The impact of need ‎satisfaction on artificial intelligence literacy mediated by self-regulated learning strategies. ‎Behavioral Sciences, 15(2), Article 165. https://doi.org/10.3390/bs15020165‎

Xu, J. (2026). How generative AI enhances self-regulated learning in EFL learners: A chain mediation model ‎of “intention to use” and “learning engagement.” Frontiers in Psychology, 17, Article 1808183. ‎https://doi.org/10.3389/fpsyg.2026.1808183‎

Yuniarti, N., Rahmawati, Y., Anwar, M., Al Hakim, V. G., Hidayat, H., Hariyanto, D., Husna, A. F., & Wang, J.-H. ‎‎(2024). Augmented reality-based higher order thinking skills learning media: Enhancing learning ‎performance through self-regulated learning, digital literacy, and critical thinking skills in vocational ‎teacher education. European Journal of Education, 59(4). https://doi.org/10.1111/ejed.12725‎

Zhou, X., Teng, D., & Al-Samarraie, H. (2024). The mediating role of generative AI self-regulation on students’ ‎critical thinking and problem-solving. Education Sciences, 14(12), Article 1302. ‎https://doi.org/10.3390/educsci14121302‎

Zhou, X., Zhang, J., & Chan, C. (2024). Unveiling students’ experiences and perceptions of artificial ‎intelligence usage in higher education. Journal of University Teaching and Learning Practice, 21(6). ‎https://doi.org/10.53761/xzjprb23‎

Published
2026-08-30
How to Cite
Fani Okta Srinawati, & Rose Rahmidani. (2026). Perceived Usefulness, Perceived Ease of Use, and Self-‎Reported Critical Thinking Among Indonesian Vocational ‎Students: The Indirect Role of Self-Regulated Learning. JPI: Jurnal Pustaka Indonesia, 6(2), 494-514. https://doi.org/10.62159/jpi.v6i2.2634
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Articles