Understanding Generative AI Adoption in Higher Education: A Task-Technology Fit Perspective

The rise of GenAI (Generative AI), such as ChatGPT, is transforming academic engagement, providing innovative learning opportunities and interactive ways for sharing knowledge. The potential of GenAI is fully realized when students specify the aspect of the academic task, enabling the tools to achieve the desired outcomes. Although the role of GenAI in the workplace has been studied, little is known about its impact on student-driven academic tasks. While research on technology adoption highlights general factors such as perceived usefulness it overlooks the task-dependent nature of GenAI adoption. Students often use GenAI to meet specific academic needs and performance tasks such as brainstorming to support idea generation and automation to enhance efficiency in repetitive academic work. Therefore, understanding how these tasks align with the features of GenAI can offer novel insights in academic settings. In addition, GenAI can tailor responses to students’ cognitive levels and provide iterative feedback, enhancing engagement. This highlights the critical role of GenAI’s technical features such as personalized responses and interactivity. However, the extant body of research has not fully explored how these features influence task-technology fit and facilitate academic learning. This work extends Task-Technology Fit (TTF) theory to examine GenAI use in education. We aim to investigate the task and technology characteristics influencing task-technology fit in students' use of GenAI tools, the impact of task-technology fit on students' continuous use, and the roles of attitude and organizational support in moderating the relationship between task-technology fit and GenAI usage. A longitudinal survey will be conducted to collect data from university students over two academic semesters. The survey will measure task characteristics, technology fit, and continuous use intentions. Additionally, students will rank educational tasks based on their alignment with GenAI features. This approach enables tracking changes in usage patterns and assessing the impact of GenAI adoption over time. This study can guide policymakers in deciding whether to introduce GenAI in academic settings and assist technology investors in identifying opportunities to enhance user adoption. Aligning GenAI with academic tasks might enhance productivity, satisfaction, and performance, leading to sustained use. This alignment is influenced by students’ perceptions, emphasizing the role of individual attitudes in adoption. The research offers insights into integrating GenAI into education, optimizing academic tasks, and fostering effective learning environments.