AI for education: The Adoption of Artificial Intelligence in Higher Education: Insights from the Students at Stockholm University

Sammanfattning

This paper discusses the drivers of artificial intelligence (AI) adoption among students at Stockholm University with particular attention to four determinants, namely, performance expectancy, perceived ease of use, perceived risk, and perceived organisational support. The study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and supplemented by the constructs of the Technology Acceptance Model and organisational support theory, thus having a positivist, deductive and quantitative research design. A structured online questionnaire was employed to collect data of 204 undergraduate and postgraduate students and a multiple linear regression was used to analyze the data. The model accounted 62.5% of the variance in adoption of AI and it was found to be statistically significant (F = 82.998, p < 0.001). Findings affirmed that performance expectancy (Beta = 0.463, p < 0.001) and perceived ease of use (Beta = 0.394, p < 0.001), have a positive and significant effect on AI adoption, and perceived risk (Beta = -0.119, p = 0.015) has a negative significant effect. There was no statistically significant effect of perceived organisational support (p = 0.708). These results emphasize the perceived academic utility and usability as the most influential factors in student adoption of AI, and the risk perceptions as the inhibitory factor when there is no explicit institutional direction. The research fills the gap in the existing literature regarding the adoption of AI in the Swedish higher education setting and provides evidence-based insights into how university administrators and policymakers can encourage responsible and effective AI adoption.