Exploring key factors influencing the adoption of Generative AI in computing education: A study of Saudi Arabian universities

Abstract

Generative Artificial Intelligence (GenAI) is rapidly transforming computer science education. This paper aims to explore the factors potentially influencing computer science instructors’ and students’ behavioural intentions to adopt GenAI tools in Saudi Arabia. This study employs a two-phase research design to examine and validate the factors influencing GenAI adoption among educators and students in Saudi universities. The study used a mixed method methodology. Data were collected by semi-structured interviews on students and instructors’ perspectives and behavioural intentions on the use of GenAI tools in learning or teaching.  A sample of seven instructors, based in Saudi Arabia, formed the sample for the study and they expressed satisfaction with their use of AI. The results were examined in relation to secondary literature. The findings proposed seven factors that influence GenAI adoption: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, AI Self-Efficacy, AI Trust, and Perceived Risk. These were all found to be important when it came to the teachers’ adoption of GenAI.  The paper proposes a framework encompassing all UTAUT factors, in addition to AI Self-Efficacy, AI Trust, and Perceived Risk, to encourage computer science teachers and students to embrace GenAI technologies in Saudi Arabian universities.