Examining the behavioral determinants of AI adoption in higher education: a focus on perceptional factors and demographic differences

Anum Zahid Syed · Zeeshan Hyder Memon · Kamran Khan · Irfan Hameed · Midhat Nadeem

12 citations0/0 files storedOn the Horizon The International Journal of Learning Futures

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Technology Adoption and User Behaviour Artificial Intelligence in Education AI in Service Interactions

Abstract

Purpose Artificial intelligence (AI) has seen considerable growth recently and is denoted as an emerging technology, particularly in educational settings. This study aims to explain the students’ perceptions and behavioral factors in adopting AI. This paper utilized gender and country differences to explore the adoption patterns influenced by perceptual and behavioral factors, including AI literacy, AI anxiety, AI self-efficacy, relevance, and perceived enjoyment. Design/methodology/approach A survey was conducted on Malaysian students using purposive sampling. The finalized sample of 391 respondents was tested using MS Excel, SPSS and SmartPLS to obtain the results. Moreover, to observe the differences, this paper used multigroup analysis based on gender and country. Findings This paper found that the theoretical factors strongly influence the intention of students to adopt AI in higher education. AI anxiety, AI self-efficacy and perceived enjoyment affect ease of use (PEOU). AI literacy, PEOU, relevance and subjective norms influence perceived usefulness (PU). In addition, both PU and PEOU shape students’ attitudes and intentions to adopt AI in their learning. Originality/value The use of AI has garnered significant attention from both consumers and organizations. This study specifically focuses on students, shedding light on the perceptual, behavioral and social factors influencing AI adoption in higher education.

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References

1989Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology 2008Technology Acceptance Model 3 and a Research Agenda on Interventions 2000Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model 1985A technology acceptance model for empirically testing new end-user information systems : theory and results 2012Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations 1995Application of Social Cognitive Theory to Training for Computer Skills 2021An Introduction to Structural Equation Modeling 2019Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior

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