Examining the Intention of Undergraduate Mathematics Education Students to Use Ai in Their Academic Work: an Application of the Utaut Model
Year: 2025
Type: article
Study design: Observational Study
Abstract: The emergence of generative Artificial Intelligence (AI) is disrupting every sector of the global economy and the information society. The education industry, similarly, has both educators and students exploring ways to utilize AI. This study investigates the adoption of AI in higher education among undergraduate mathematics education students using the four constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research focuses on Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, and their influence on students’ Behavioural Intention to adopt AI tools. Gender was included as a moderating variable in the relationships between these constructs and behavioural intention. A sample of 142 undergraduate mathematics education students participated in an online survey to measure their perceptions and intentions of AI adoption in their academic work. The findings revealed that all four independent variables and gender had significant direct effects on Behavioural Intention to use AI. This indicates that students perceive AI tools as valuable for enhancing academic performance, easy to use, influenced by social factors such as peers, and supported by adequate facilitating conditions such as technical infrastructure. However, gender did not emerge as a significant moderator in any of the relationships between the UTAUT constructs and Behavioural Intention. This suggests that male and female students exhibit similar adoption patterns toward AI technologies in this context. These results contribute to the growing body of literature on technology adoption in education by confirming the applicability of the UTAUT model within a mathematics education-focused cohort of students while highlighting that gender differences may not play a critical role in shaping intentions toward AI adoption. Future research could explore additional moderating variables or extend this analysis across other disciplines for broader generalizability. Article visualizations:
Author: Philip K. Mwei
Institution: Moi University
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7111106563
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