Perceptions and Adoption of Generative AI Tools Among Undergraduate Students: An Integrated Utaut and Social Cognitive Theory Perspective

Abstract

The rapid emergence of tools like ChatGPT and other generative AI (GenAI) applications has transformed higher education by providing new avenues for learning, collaboration, and academic support. This study investigates the perceptions, self-efficacy, and adoption of generative AI tools among undergraduate students at the University of Kelaniya. An integrated theoretical framework combining the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT) was employed to examine how performance expectancy, effort expectancy, social influence, self-efficacy, social reinforcement, and facilitating conditions shape students' behavioral intentions to adopt AI tools. The study population comprised 14,500 undergraduate students from the University of Kelaniya, with a sample of 374 respondents. Data was collected using a structured five-point Likert-scale questionnaire. The results indicate that performance expectancy, effort expectancy, social influence, self-efficacy, and social reinforcement significantly predict students' behavioral intentions to use GenAI tools, whereas facilitating conditions were not significant, suggesting that students currently lack adequate support, guidance, and resources to use AI tools effectively for academic work. These findings highlight the need for institutional guidelines, training programs, and support mechanisms to promote responsible and effective use of GenAI tools in higher education, while fostering essential skills such as critical thinking, creativity, and academic integrity.

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