Factors Affecting Students' Continuance Usage Intention of Generative Artificial Intelligence (GenAI) in Higher Education

Tan, Peixin (2024) Factors Affecting Students' Continuance Usage Intention of Generative Artificial Intelligence (GenAI) in Higher Education. Masters thesis, Tunku Abdul Rahman University of Management and Technology.

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Abstract

The integration of Generative Artificial Intelligence (GenAI) in higher education is transforming the landscape of teaching and learning by offering innovative tools or technologies that enhance educational quality and experiences. These technologies facilitate personalised learning, immediate feedback, self-directed learning, and improved research capabilities, making them as a learning supporting tool for student. In Malaysia, the adoption of GenAI is viewed as a strategic move to advance digital learning initiatives across all educational levels, including universities. Likewise, the education sector in Malaysia has started to integrate these innovative technology courses into students’ learning materials. More universities are offering courses that specialise in AI-related courses and programs, which can seem that GenAI has offered benefits where they have been slowly integrated into education methodologies. Previous research has mostly focused on the technology-centric models such as TAM, UTAUT3, and ISSM, or motivation theory, to predict higher education students’ continuance usage intention of ChatGPT, while a task-technology fit (TTF) centric model has yet to be thoroughly addressed in predicting the continuance usage intention of GenAI among higher education students. Thus, this research aims to investigate the relationship between TTF and the continuance usage intention within the GenAI context among higher education institution students. Also, this research extends the task-technology fit (TTF) theory with the technology acceptance model (TAM) by incorporating factors like perceived ease of use and perceived usefulness and aims to study how a task-technology fit factor with perceived ease of use and perceived usefulness can influence students’ decisions to use GenAI continuously. This research will examine the direct, indirect (simple mediation), serial mediation, and moderated mediation relationships among task characteristics, technology characteristics, task-technology fit, perceived ease of use, perceived usefulness, AI self-efficacy, and continuance usage intention. To achieve the research objectives, 426 responses were collected through an online self-administered questionnaire survey. The data will be analysed using Statistical Package for Social Sciences (SPSS) and Partial Least Squares Structural Equation Modelling (PLS-SEM) software. The results reveal that all direct, indirect, serial mediation and moderated mediation relationships are significantly supported. The research findings will contribute to the theoretical and managerial implications by illustrating the influence of these factors on the continuance usage intention of GenAI. This research may provide insight into how students’ task characteristics can fit with GenAI functionalities that lead to continuance usage intention, while also exploring the interrelations between studied factors. The current research will be concluded by underpinning some limitations and recommendations for future research to delve deeper into the research area of students’ continuous usage intention of innovative technologies.