Trust and Usefulness in Students’ Acceptance of AI-Based Learning: Evidence from Jakarta High Schools

Artificial intelligence (AI) is increasingly used as a learning medium in secondary education, yet students’ acceptance of AI tools is not uniform. This study examines high school students’ behavioral intention to use AI in learning by extending the Technology Acceptance Model (TAM) with perceived trust. A quantitative explanatory design was employed with a cross-sectional survey of 98 high school students in Jakarta who had experience using AI for learning. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to evaluate the measurement model and test the structural relationships among Perceived Ease of Use, Perceived Usefulness, Perceived Trust, and Behavioral Intention to Use. The results show that Perceived Usefulness (β = 0.243, p < 0.01) and Perceived Trust (β = 0.457, p < 0.001) have significant positive effects on Behavioral Intention to Use, while Perceived Ease of Use has a positive but statistically non-significant effect (β = 0.184, p > 0.05). These findings indicate that students’ intention to use AI in learning is driven more by perceived learning benefits and confidence in the reliability and safety of AI than by usability alone. The study refines TAM applications in AI-supported education by underscoring the central role of trust and offers practical guidance for schools and developers to design AI tools that are not only easy to use but also demonstrably useful, transparent, and secure for student learning.