Basic Education Research Program (BERP) Meets Artificial Intelligence: A CIPP Evaluation into Research Innovation Among Master Teachers
DOI:
https://doi.org/10.5281/zenodo.19593335Keywords:
Artificial Intelligence, Basic Education Research Program, CIPP Evaluation, Mixed Methods, Educational Research InnovationAbstract
This study evaluates the integration of Artificial Intelligence (AI) within the Basic Education Research Program (BERP) using Stufflebeam’s Context–Input–Process–Product (CIPP) evaluation model. It examines how the program strengthens research innovation, productivity, and capacity development among Master Teachers in selected public schools in the Schools Division of Baybay City, Philippines. Anchored on the growing relevance of AI in academic research, the study assesses program effectiveness in terms of contextual alignment, resource adequacy, implementation processes, and outcomes. A mixed-methods design was employed involving 50 Master Teachers selected through purposive sampling. Data were gathered using validated survey questionnaires, semi-structured interviews, focus group discussions, AI usage logs, and document analysis. Quantitative data were analyzed using descriptive statistics (mean and standard deviation), while qualitative data were examined through thematic analysis to identify patterns in AI utilization, research engagement, and institutional support. Findings show strong contextual alignment between BERP and institutional goals (M = 4.32), indicating high awareness and support for AI integration. Input evaluation revealed adequate institutional support (M = 4.09), including access to AI tools, training, and mentorship, although limited research time emerged as a constraint (M = 3.88). Process evaluation (M = 4.11) indicated structured and ethical implementation, but highlighted the need for stronger coaching and feedback mechanisms. Product evaluation (M = 4.15) showed that AI integration improved research quality, efficiency, and teacher engagement, contributing to enhanced digital literacy and instructional innovation. However, only 38% of respondents actively used AI tools in research writing, indicating low adoption despite availability. The study concludes that BERP has strong potential as an AI-supported research development program, but sustainability depends on improving AI literacy, mentorship, workload allocation, and ethical capacity-building to maximize institutional impact.
References
Alkin, M. C., & Christie, C. A. (2004). An evaluation theory tree. In M. C. Alkin (Ed.), Evaluation roots: Tracing theorists’ views and influences (pp. 12–65). SAGE Publications.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002
Dwivedi, Y. K., Kshetri, N., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., et al. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 102245. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fitzpatrick, J. L., Sanders, J. R., & Worthen, B. R. (2011). Program evaluation: Alternative approaches and practical guidelines (4th ed.). Pearson.
Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111
Gaspari, F., Almutairi, M., & Doherty, S. (2021). AI-driven machine translation and its implications for scholarly communication. Journal of Scholarly Publishing, 52(4), 235–258. https://doi.org/10.3138/jsp.52.4.01
Goh, G. B., Hodas, N. O., & Vishnu, A. (2021). Applications of artificial intelligence in literature-based discovery. Drug Discovery Today, 26(2), 570–577. https://doi.org/10.1016/j.drudis.2020.12.015
Hammad, M. (2023). The impact of artificial intelligence programs on writing scientific research. Annals of Biomedical Engineering, 51(1), 459–460. https://doi.org/10.1007/s10439-023-03140-1
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Research on Multidisciplinary Studies
This work is licensed under a Creative Commons Attribution 4.0 International License.
