Integrating Technology Acceptance Model and Task-Technology Fit Theory: Investigating Students’ Intention to Use AI Chatbots in Public Universities

Technology has significantly enhanced access to education, enabling students to benefit from lower costs and higher productivity. This advancement has been further expedited by the incorporation of AI, especially through applications like AI chatbots. Despite these advancements, students in higher education- especially those enrolled in public universities in developing countries- often exhibit scepticism toward adopting AI chatbots for academic assistance. Thus, the main goal of this study is to find out how students in public universities in developing nations plan to use AI chatbots as teaching aids. The study will use a purposive sampling strategy to gather data from students at different public universities, with a focus on Malaysia. To construct a robust conceptual model that explains students' behavioural intentions toward AI chatbot usage, this research adopts an integrated framework combining the Technology Acceptance Model (TAM) and the Task–Technology Fit (TTF) theory. For data analysis, the study will use the PLS-SEM approach, a well-regarded regression technique suitable for examining complex relationships in structural models. The research's conclusions are anticipated to provide theoretical and practical insights. This study may theoretically add to the body of knowledge on AI chatbot adoption by illuminating the complex human behaviours linked to the adoption of new technologies. Practically, the findings may give governments, university administrators, and AI chatbot developers important information on how to work together to create AI chatbots that are more dependable, user-friendly, and attractive to students, especially in developing countries.