Determinants of students’ decision to adopt generative AI in higher education: An extended technology acceptance model with information quality

Purpose: This study examines the determinants influencing students’ decisions to adopt Generative Artificial Intelligence (GenAI) in higher education by extending the Technology Acceptance Model (TAM) with the inclusion of information quality. Method: A quantitative approach was employed through an online survey targeting university students who have experience using GenAI tools. A total of 204 valid responses were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess both the measurement and structural relationships among variables. Findings: The findings reveal that perceived usefulness, perceived ease of use, enjoyment, self-efficacy, and information quality significantly influence students’ decisions to adopt GenAI. Among these factors, perceived usefulness and information quality demonstrate the strongest effects. Conversely, computer anxiety does not show a significant influence on adoption decisions. Significance: This study contributes to the existing literature by integrating information quality into an extended TAM framework, providing a more comprehensive perspective on decision-making in the adoption of emerging AI technologies. The results offer practical implications for higher education institutions and developers to enhance adoption by improving usability, user competence, and the reliability of AI-generated information.