The Causal Relationship Model of Artificial Intelligence Utilization in Academic Administration of The Secondary Educational Service Area Office Bangkok 1

Abstract

Background and Aims: By automating repetitive tasks, integrating artificial intelligence (AI) into academic administration improves operational efficiency and frees up educators to concentrate more on research and instruction. AI also makes it easier to create individualized learning experiences, which improves overall educational results by customizing instructional materials to meet the needs of each student. Thus, the objectives of this research were to analyze the causal relationship model of artificial intelligence utilization in the academic administration of schools under the Secondary Educational Service Area Office Bangkok 1. The specific objectives were to: 1) analyze components of AI utilization in academic administration, 2) analyze perception components of AI utilization in academic administration, 3) analyze AI skill components in academic administration, and 4) analyze the causal relationship model.

Methodology: The sample consisted of 200 school administrators and teachers under the Secondary Educational Service Area Office Bangkok 1, selected through two-stage random sampling. Data was collected using questionnaires and analyzed using confirmatory factor analysis and structural equation modeling.

Results: The finding showed: 1) AI utilization in academic administration comprised 3 main components: curriculum development, instruction management, and evaluation, with the highest overall mean level; 2) Perception of AI utilization consisted of 3 components: perceived usefulness, ease of use, and attitude toward use, with the highest overall mean level; 3) AI skills consisted of 3 components: basic knowledge, application, and problem-solving; 4) The causal relationship model analysis revealed: a) model fit with empirical data, b) most components had factor loadings between 0.837-0.853 with high reliability, c) all variables showed positive correlations with correlation coefficients between .007-.195, statistically significant at .01 level.

Conclusion: The study emphasizes AI's diverse role in academic administration, emphasizing how it has a big impact on evaluation procedures, instructional management, and curriculum development. In order to successfully integrate AI technologies in educational settings, it also highlights the significance of stakeholders' perceptions, including perceived usefulness, ease of use, and attitudes toward AI, as well as the need for proficiency in AI skills like fundamental knowledge, application, and problem-solving.