Basic Education Research Program (BERP) Meets Artificial Intelligence: A CIPP Evaluation into Research Innovation Among Master Teachers

Abstract. 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. Keywords: Artificial Intelligence, Basic Education Research Program, CIPP Evaluation, Mixed Methods, Educational Research Innovation