Development of Internal Supervision Model Using Professional Learning Communities Integrated with Artificial Intelligence to Enhance Educational Achievement

Abstract

This research aimed to develop an internal supervision model by integrating professional learning communities (PLCs) with artificial intelligence (AI) and to examine the effects of the model on enhancing educational achievement. A mixed-methods approach with a developmental research design was employed. The main participants were 30 teachers from Prot Pittayapayat School, together with school administrators, heads of learning areas, and experts in supervision and educational technology, who were involved in evaluating and validating the model. Research instruments included a survey, an expert evaluation form, an AI-based system for data collection and analysis, a satisfaction questionnaire, and in-depth interviews. The findings revealed that the developed supervision model consisted of five key components: an integrated conceptual framework for supervision, a four-step supervision process, an empowering PLC system, user-friendly AI tools, and mechanisms for continuous evaluation and improvement. Expert evaluation indicated that the model was highly appropriate (x̄ = 4.52, S.D. = 0.54). A pilot implementation with teachers showed statistically significant improvements in supervision knowledge, technology skills, and PLC collaboration, along with very high satisfaction (x̄ = 4.25, S.D. = 0.61). Moreover, the AI system, particularly the Dashboard System, was widely accepted and effectively utilized, making supervision more continuous, timely, and development-oriented. The qualitative findings further confirmed the acceptance of AI technology, the positive impact on PLC functioning, and a shift in supervision practices from inspection toward developmental support. Although challenges such as training needs and system stability remain, the model demonstrates strong potential for sustainably enhancing educational achievement.