The Impact of Task-Technology Fit on the Intention to Use Artificial Intelligence in the Education of Information Technology Students in Universities: The Role of Self-Efficacy

ABSTRACT

This study aims to examine university students’ intentions to use artificial intelligence (AI) applications in their educational processes within the context of job characteristics (JC), technology characteristics (TC), task-technology fit (TTF), and self-efficacy (SE). The research was conducted with 965 students enrolled in Information Technology programs at four foundation universities in Istanbul. Data were collected through a structured questionnaire and analyzed using SPSS 24. Linear regression analysis was employed to interpret the relationships among the variables. According to the findings, both job characteristics (β = 0.609, p<0.01) and technology characteristics (β = 0.883, p<0.01) were found to have a positive and significant effect on TTF. Furthermore, TTF was identified as a significant predictor of AI usage intention (β = 0.644, p<0.01). Also, the self-efficacy variable moderate significantly the relationship between TTF and AI usage intention (β = 0.115, p <0.01). The independent variables in the research model explained 18% of the variance in task-technology fit and 64% of the variance in AI usage intention. The findings suggest that enhancing students' technological self-efficacy and developing user-friendly AI solutions may encourage the adoption of AI technologies in educational settings. One of the limitations of this study is that the sample was restricted to students from four foundation universities in Istanbul. Therefore, future research is recommended to include larger and more diverse samples from different regions and disciplines to improve the generalizability of the results.