The Relationship Between Performance Expectancy, Effort Expectancy, and Perceived Risk on AI Acceptance in Higher Education

The integration of artificial intelligence (AI) in higher education has rapidly expanded, offering potential to enhance student learning, streamline administrative tasks, and personalize educational experiences. However, the acceptance remains uneven due to varying perceptions and student readiness. Despite AI’s potential, its implementation faces challenges such as lack of pedagogical integration, perceived complexity, and securities concerns. This study addresses the gap by examining performance expectancy, effort expectancy, and perceived risk that shape student attitudes toward AI in academic contexts. The study examines the relationship of those key factors that influencing AI acceptance in higher education among students at Universiti Teknologi MARA (UiTM), Malaysia. Literature review reveals that student perceptions especially regarding usability and securities are critical in determining AI acceptance. A quantitative research design was employed using a structured questionnaire distributed to 300 students across multiple faculties. Data were analyzed through Pearson correlation and multiple regression. The findings indicate that performance expectancy and effort expectancy significantly influence AI acceptance, with performance expectancy being the most impactful. Regression analysis revealed that perceived risk had no significant effect while low correlated. The result suggest that students are motivated by AI’s potential to enhance efficiency and learning outcomes, provided tools that are accessible and institutionally supported. It concludes that fostering a supportive technological environment is key to successful AI acceptance. Recommendations include increasing digital literacy training, ensuring institutional support for AI tools, and addressing privacy and ethical concerns transparently. These efforts can improve user trust and drive meaningful AI adoption in higher education.