Home⇛Journal of Interdisciplinary Perspectives⇛vol. 4 no. 8 (2026)

Integrating Generative AI Tools for Personalized Science Instruction: A Mixed-Method Study in Vocational School

Farrah L. Aminulla | Jocelyn J. Munez | Al-fahad Jadjuli

Discipline: Education

 

Abstract:

Limited empirical evidence explains how generative artificial intelligence (AI) supports personalized science instruction in Philippine vocational education, particularly within competency-based learning environments characterized by diverse student readiness, digital inequities, and constrained instructional resources. This study examined the integration of generative AI tools for personalized science instruction among vocational students through a convergent parallel mixed-methods design grounded in Constructivist Learning Theory and the Technology Acceptance Model, contributing to Sustainable Development Goal 4 (Quality Education). Quantitative data were collected from 200 Senior High School students using a validated five-point Likert-scale questionnaire (Cronbach's α = 0.91), while qualitative data were obtained from 15 purposively selected students through focus group discussions and semi-structured interviews following an eight-week AI-supported intervention. Findings indicated positive exposure to AI-assisted instruction, with content delivery receiving the highest mean score (M = 3.81). Perceived effectiveness was strongest for conceptual understanding and motivation (M = 3.78); however, exposure was not significantly associated with perceived instructional effectiveness (ρ = .055, p = .443). Qualitative findings showed that generative AI simplified complex concepts, enhanced learning efficiency, and promoted independent learning, while raising concerns regarding information reliability, learner overdependence, and unequal digital access. The study recommends strengthening pre-service and in-service teacher training on AI-enhanced pedagogy, ethical AI use, and instructional design to support effective integration of generative AI in vocational science education.



References:

  1. Ahmad, N., Murugesan, S., & Kshetri, N. (2023). Generative artificial intelligence and the education sector. Computer, 56(6), 72–76. https://doi.org/10.1109/mc.2023.3263576
  2. Al-Cheikh, C., Tahiri, I., & Khaldi, M. (2025). Educational artificial intelligence and intelligent tutoring systems: Foundations, evolution, and contributions to adaptive learning. Global Journal of Engineering and Technology Advances, 25(3), 240–246. https://doi.org/10.30574/gjeta.2025.25.3.0356
  3. Ali, H.Y., & Ekeng, O. (2024). Balancing innovation and ethics: Educators’ perspectives on the role of AI in education. The American Journal of Social Science and Education Innovations, 6(9), 128–139. https://doi.org/10.37547/tajssei/volume06issue09-14
  4. Annam, S., Syuzita, A., Pratiwi, R., & Sarkingobir, Y. (2024). Mapping research trends on 21st-century problem-solving skills in science learning: A literature review from 2014 to 2023. Research in Education, Technology, and Multiculture, 3(1), 52–60. https://doi.org/10.61436/rietm/v3i1.pp52-60
  5. Bai, J. (2024). The application, challenges, and reflection of generative artificial intelligence in the field of education. Education Reform and Development, 6(10), 273–281. https://doi.org/10.26689/erd.v6i10.8522
  6. Bhatta, R. (2026). AI in education for sustainable development: A comprehensive framework for equitable and quality learning. International Journal of Scientific Research in ComputerScience, Engineering and Information Technology, 12(2), 109–126. https://doi.org/10.32628/cseit26121335
  7. Bonde, L. (2024). A conceptual design of a generative artificial intelligence system for education. International Journal of Research and Innovation in Applied Science, 9(4), 457–469. https://doi.org/10.51584/ijrias.2024.904034
  8. Calles, A. (2025). Why technical-vocational education must be a national priority. PINAS Gazette: Essays, Stories & Poetry, 1(1). https://doi.org/10.64591/fgrnwg05
  9. Ching Cheung, K.K., Zerouali, A., Koenen, J., & Erduran, S. (2026). Do generative artificial intelligence (GenAI) and science education mix? A systematic review of the literature. Studies in Science Education, ahead-of-print(ahead-of-print), 1–29. https://doi.org/10.1080/03057267.2025.2578091
  10. Compen, B., Verstegen, D., Maussen, I., Hülsman, C., & Dolmans, D. (2024). Good practices for differentiated instruction in vocational education: The combined perspectives of educational researchers and teachers. International Journal of Inclusive Education, 29(6), 1017–1034. https://doi.org/10.1080/13603116.2024.2305652
  11. De La Cruz, R.J. (2022). Science education in the Philippines. In: Huang, R., et al. Science Education in Countries Along the Belt & Road. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-16-6955-2_20
  12. Idowu, E. (2024). Personalized learning: Tailoring instruction to individual student needs. Preprints. https://doi.org/10.20944/preprints202411.0863.v1
  13. Juan, H., & Nagappan, R. (2025). A comprehensive literature review on AI-enhanced autonomous learning mechanisms in vocational education. Malaysian Journal of Social Sciences and Humanities (MJSSH), 10(3), e002958. https://doi.org/10.47405/mjssh.v10i3.2958
  14. Kamer, S.T. (2024). Artificial intelligence in education. Artificial Intelligence (pp. 235–248). CRC Press. https://doi.org/10.1201/9781003483571-15
  15. Kukreja, J., Morande, S., & Tewari, V. (2025). Empowering self-directed learners by exploring the role of generative AI-language models in fostering autonomy, competence, and relatedness. In M. Tariq & R. Sergio (Eds.), Convergence of AI, Education, and Business for Sustainability (pp. 93–118). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-1917-9.ch005
  16. Leong, W.Y. (2025). Artificial intelligence, automation, and technical and vocational education and training: Transforming vocational training in digital era. Engineering Proceedings, 103(1), 9. https://doi.org/10.3390/engproc2025103009
  17. Li, Y., Shan, Z., Raković, M., Guan, Q., Gašević, D., & Chen, G. (2025). When AI explains in natural language: Unveiling the impact of generative AI explanations on educators’ grading and feedback practices. Education and Information Technologies, 30(17), 24931–24964. https://doi.org/10.1007/s10639-025-13741-z
  18. Linn, M., Donnelly-Hermosillo, D., & Gerard, L. (2023). Synergies between learning technologies and learning sciences. Handbook of Research on Science Education (pp. 447–498). Routledge. https://doi.org/10.4324/9780367855758-19
  19. Luo, Z., Jiang, J., & Zhou, H. (2024). Exploration on the application of artificial intelligence technology in vocational education teaching. Journal of Higher Vocational Education, 1(2), 177–182. https://doi.org/10.62517/jhve.202416231
  20. Ogata, H., Flanagan, B., Takami, K., Dai, Y., Nakamoto, R., & Takii, K. (2023). EXAIT: Educational eXplainable Artificial Intelligent Tools for personalized learning. Research and Practice in Technology Enhanced Learning, 19, 019. https://doi.org/10.58459/rptel.2024.19019
  21. Pierce, M., & Jiang, P. (2025). Normative influence on the adoption of generative artificial intelligence in higher education. Journal of Marketing Education. https://doi.org/10.1177/02734753251397827
  22. Prajuhana, A., Sarimanah, E., Rully, T., Setiawan, M.I., Sukoco, A., Sugeng, S., & Bon, A.T. (2023). Sustainable Development Goals (SDGs), digital education and digital school. IJEBD (International Journal of Entrepreneurship and Business Development), 6(1), 163–171. https://doi.org/10.29138/ijebd.v6i1.2114
  23. Raflesia, C., Suastra, I.W., Wibawa, I.M.C., & Arnyana, I.B.P. (2026). Transformasi pendidikan sains sebagai penggerak kompetensi masa depan siswa sekolah dasar. BIOEDUSAINS:Jurnal Pendidikan Biologi dan Sains, 9(1), 27–34. https://doi.org/10.31539/q7kzsg68
  24. Short, C., & Shemshack, A. (2023). Personalized learning. EdTechnica. https://doi.org/10.59668/371.11067
  25. Singh, K.D., Singh, P., Kaur, G., Khullar, V., Chhabra, R., & Tripathi, V. (2023). Education 4.0: Exploring the potential of disruptive technologies in transforming learning. 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), Greater Noida, India, 586–591. https://doi.org/10.1109/cises58720.2023.10183547
  26. Wang, L., & Zhao, M. (2024). Can artificial intelligence technology promote the improvement of student learning outcomes?—Meta analysis based on 50 experimental and quasi experimental studies. https://doi.org/10.4108/eai.29-3-2024.2347685
  27. Wu, Y. (2025). Integrating artificial intelligence into education: Opportunities, challenges, and response strategies. AI Innovations and Applications, 1(1), 115–124. https://doi.org/10.63944/sj51.aia
  28. Ye, J.-H., He, Z., Bai, B., & Wu, Y.-F. (2024). Sustainability of technical and vocational education and training (TVET) along with vocational psychology. Behavioral Sciences (Basel,
  29. Switzerland), 14(10), 859. https://doi.org/10.3390/bs14100859
  30. Zhou, H., & Zhou, D. (2024). Transformation of vocational education based on generative artificial intelligence: Impact, opportunity and countermeasures. https://doi.org/10.4108/eai.24-11-2023.2343636

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