Assessing Students’ Acceptance of Generative AI Tools in Education: A Technology Acceptance Model Approach

The increasing use of generative artificial intelligence (AI) in education has created opportunities to enhance teaching and learning, highlighting the need to understand the factors influencing students' acceptance of these technologies. This study examined the acceptance of generative AI tools among junior and senior high school students using the Technology Acceptance Model (TAM). A cross-sectional survey was conducted among 205 students, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results supported all hypothesized relationships in the proposed model. Perceived ease of use positively influenced perceived usefulness and attitude toward using, while perceived usefulness significantly affected attitude toward using and behavioral intention. In addition, attitude toward using positively influenced both behavioral intention and actual system use, with behavioral intention emerging as the strongest predictor of actual system use. These findings support the applicability of the Technology Acceptance Model in explaining generative AI acceptance among junior and senior high school students, extending its application within the basic education context. The study provides empirical evidence that can assist educational institutions in developing policies and instructional strategies that promote the effective, ethical, and responsible integration of generative AI to enhance student engagement and learning outcomes.