Exploring factors influencing university students’ intentions to use ChatGPT: analysing task-technology fit theory to enhance behavioural intentions in higher education

Abstract The increasing integration of AI technologies such as ChatGPT in educational systems calls for an in-depth understanding of the factors influencing students’ intentions to use these tools. This study explores the factors shaping university students’ intentions to use ChatGPT by analysing three key dimensions: task characteristics, technology characteristics and individual characteristics. Using the task-technology fit (TTF) framework, the research examined how these elements impact the alignment between educational tasks and ChatGPT’s capabilities, ultimately driving students’ behavioural intentions. A survey of 393 students from a Saudi Arabian university was conducted, and structural equation modelling was applied to assess the relationships among the variables. Results indicated that all three characteristics significantly influenced TTF, which in turn had a positive impact on students’ intentions to use ChatGPT. The study highlighted the importance of achieving a strong TTF to encourage the effective use of AI tools in academic settings. The implications of this research suggest that educational institutions should focus on aligning AI technologies with students’ learning tasks to enhance their intent to use these tools, thereby improving academic performance. Furthermore, this study extended the TTF model to the context of AI-powered educational tools, particularly in line with Saudi Arabia’s Vision 2030. This research is one of the first to investigate the factors influencing students’ intentions to use ChatGPT within the unique cultural and technological context of Saudi Arabia’s higher education system. By integrating the TTF framework with local and regional factors, the study provides novel insights into the drivers of AI usage in education, offering guidance for regional policy and broad educational practices.