Extending the technology acceptance model with ethics, trust, and subjective norms: A PLS-SEM analysis of students’ AI adoption in higher education

The rapid integration of artificial intelligence tools in higher education has prompted critical questions regarding students’ acceptance and sustained usage patterns. While the Technology Acceptance Model (TAM) has traditionally explained technology adoption through perceived usefulness and perceived ease of use, emerging AI-driven educational contexts necessitate the incorporation of ethical, trust-based, and normative dimensions. This study extends TAM by integrating ethics, trust, and subjective norms as complementary constructs to investigate students’ adoption of AI tools in academic settings. Employing partial least squares structural equation modeling on a sample of 637 university students, we examined direct, indirect, and moderating effects within the extended framework. Results indicate that perceived usefulness (β = 0.681, p 0.001) emerged as the dominant predictor of attitude toward using AI, while trust (β = 0.245, p 0.001) and subjective norms (β = 0.172, p 0.001) significantly influenced actual use. Contrary to expectations, hypothesized moderation effects of ethics and trust on the attitude–use relationship were not supported. Importance–performance map analysis revealed that while perceived usefulness demonstrates high importance and performance, trust exhibits a notable performance gap despite its strategic importance. This study contributes to the theoretical advancement of TAM in AI contexts and offers practical insights into educational policymakers seeking to foster responsible and effective AI integration in higher education.