AN ARTIFICIAL INTELLIGENCE-DRIVEN SMART EDUCATION MANAGEMENT MODEL TO ENHANCE STUDENT COMPETENCIES IN SCHOOLS IN THE EASTERN ECONOMIC CORRIDOR OF THAILAND

Abstract

The objectives of this research article were 1) To examine the current conditions, problems, and priority needs, and to synthesize the design requirements; 2) To develop the model; 3) To verify the quality of the model; and 4) To implement the AI-based Intelligent Education Administration and Smart Management (AI-IEASM) Model for enhancing learner competencies in schools within the Eastern Economic Corridor (EEC). The research and development (R&D) sample consisted of 300 educational personnel selected using multi-stage sampling, 9 experts, and 8 school administrators selected through purposive sampling for focus group discussions. A pilot group of 60 participants from two pilot schools conducted a 12-week model pilot program. The research instrument had an Item-Objective Congruence Index (IOC) of 0.80 - 1.00 and a Cronbach's alpha reliability coefficient of 0.88 - 0.94. Data were analyzed using percentages, means, standard deviations, modified Priority Needs Index (PNI modified), paired-samples t-tests, and Cohen's d effect sizes. The research findings revealed the lack of a practical, data-driven, and AI-based management model. The highest priority needs were identified in teaching quality, personnel competence, and AI integration. The developed model comprises four components-inputs, processes, outputs, and a feedback loop-driven by digital readiness and an intelligent assistant system. Verification confirmed that the model's quality was rated at the highest level ( = 4.49) and achieved full (100%) consensus following the trial. Administrators and teachers demonstrated statistically significantly higher smart educational management competencies after the experiment at the .05 level. Similarly, learners' competencies were significantly higher than before the experiment at the .05 level. Additionally, the model achieved the highest level of appropriateness, feasibility, and utility ( = 4.52).