Artificial Intelligence Integration in Lesson Planning and Differentiated Instruction as Predictors of Student-Centered Learning: A Multiple Regression Analysis

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{"abstract":"Artificial Intelligence (AI) is increasingly transforming educational practices by enhancing instructional planning, personalization, and learner engagement. This study examined the predictive influence of AI integration in lesson planning and differentiated instruction on student-centered learning among teachers in North Cotabato. Specifically, the study assessed the levels of AI integration, differentiated instructional planning, and student-centered learning and determined their relationships and predictive effects. A descriptive-correlational research design was employed involving 356 elementary and secondary school teachers. Data were collected using a validated survey questionnaire and analyzed using descriptive statistics, Pearson correlation, and multiple regression analysis. The findings revealed that teachers demonstrated high levels of AI integration, differentiated instructional planning, and student-centered learning practices. Correlation analysis showed significant positive relationships between AI integration and student-centered learning, as well as between differentiated instructional planning and student-centered learning. Furthermore, multiple regression analysis confirmed that both AI integration and differentiated instructional planning significantly predicted student-centered learning, with differentiated instructional planning exerting a stronger influence. Among the dimensions examined, automation and efficiency, flexible grouping strategies, and process differentiation emerged as the strongest predictors. The results suggest that integrating AI technologies with differentiated instructional practices can effectively support learner engagement, participation, collaboration, and inclusivity. The study highlights the importance of educational technology integration and teacher innovation in fostering personalized learning experiences and strengthening student-centered classrooms. The findings provide valuable insights for educators, school leaders, and policymakers seeking to promote technology-enhanced and inclusive teaching practices aligned with sustainable educational development and SDG 4 (Quality Education).","abstract_source":"original","authors":["Angel Lhi Alcalde","Cinly Genella","Marlon Marcelino","Fritz Biongan","Joemar Tacuel","Juvelyn Valdez","Myra Nietes","Leorence Tandog"],"date":"2026-10-06","doi":"10.70838/PEMJ.630904","id":11014,"issue":9,"journal":{"issn":"2822-4353","name":"Psychology and Education: A Multidisciplinary Journal"},"pdf_sha256":"0961534fa598f8b7fac6fb8554a9b17578a799f9f0656e263dbe76601d5774ba","schema":"scimatic-article/1","title":"Artificial Intelligence Integration in Lesson Planning and Differentiated Instruction as Predictors of Student-Centered Learning: A Multiple Regression Analysis","volume":63}

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