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

human-computer-interaction
artificial-intelligence
computer-education
social-computing
sentiment-analysis

Abstract

Generative Artificial Intelligence (GenAI) is rapidly transforming computer science education by introducing new ways to teach, learn, and assess programming and computational thinking. This paper explores the factors that could influence computer science instructors’ and students’ behavioral intentions to adopt GenAI tools. We reviewed the current work on students' and instructors’ perspectives and behavioral intentions to use GenAI tools in learning or teaching. Subsequently, seven factors were proposed to investigate their significance to gain insights into computer science and e-learning instructors’ perspectives: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, AI Self-Efficacy, AI Trust, and Perceived Risk. The proposed framework was evaluated by conducting semi-structured interviews with seven experienced and knowledgeable respondents, mainly from the perspectives of computer science and e-learning instructors. The results provide rich data and evidence supplied by the participants on the significance of each of the seven proposed factors. To the best of the researcher’s knowledge, this is the first study that proposes a theoretical framework to examine the factors influencing computer science teachers and students to embrace GenAI technologies in Saudi Arabian universities.
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