Integrating Artificial Intelligence (AI) Tools in Teaching: Its Influence on Instructional Quality, Productivity, and Student Motivation

This study examined teachers’ integration of artificial intelligence (AI) tools in teaching and its influence on instructional quality, teacher productivity, and students’ motivation in selected public secondary schools in Congressional District I of Bohol Division during the School Year 2025–2026. Specifically, it investigated the AI tools teachers use, their skill levels, the extent of AI integration in instruction, and the perceptions of school heads and teachers on instructional quality, productivity, and student motivation. A descriptive-inferential design was employed, utilizing survey questionnaires administered to school heads, teachers, and Grade 10 students. Data were analyzed using frequency counts, weighted means, and multivariate analysis of variance (MANOVA) to determine the overall and individual effects of AI integration on the measured outcomes. The overall multivariate results indicate a significant effect of teachers’ level of AI integration on the combined dependent variables, as shown by Pillai’s Trace (V = 0.527, F = 35.6, p < .001), Wilks’ Lambda (Λ = 0.473, F = 35.6, p < .001), Hotelling’s Trace (1.11, F = 35.6, p < .001), and Roy’s Largest Root (1.11, F = 35.6, p < .001), suggesting that AI integration collectively influences the measured outcomes. Individually, AI integration significantly improved instructional quality (F = 86.791, p < .001) and teacher productivity (F = 40.691, p < .001), while its effect on students’ motivation was not significant (F = 0.504, p = 0.480). The study concludes that AI integration enhances teaching effectiveness and efficiency but may require complementary strategies to sustain learner motivation. Based on these findings, it is recommended that teachers engage in professional development on AI-supported assessment and lesson planning, school administrators provide guidance and support for underutilized AI tools, students utilize AI-assisted learning resources, and future researchers explore factors influencing student motivation and innovative approaches to support AI integration in instruction.