LEVERAGING AI CHATBOTS FOR SELF-DIRECTED LEARNING: A STUDY OF PRACTICES AND DRIVERS TO ADOPTION AMONG COLLEGE STUDENTS
Abstract
AI chatbots, the interactive software, utilizing natural language processing and artificial intelligence (AI), are increasingly used in various domains, including education, where they offer personalized and adaptive learning support. This study explores college students’ utilization of AI chatbots in their self-directed learning experiences, where learners initiate, plan, and regulate their learning activities themselves. Employing a cross-sectional descriptive approach, a quantitative survey was used to scrutinize the AI chatbot usage practices and the factors that influences their adoption among randomly selected 200 college students from diverse backgrounds. Findings reveal a spectrum of purposes for using the interacting agents, including information seeking, problem solving, completing assignments, feedback, and motivation. Furthermore, Principal Component Analysis (PCA) scrutinised usability, ethical considerations, and technological barriers as the factors affecting AI chatbot adoption among students in higher education. To assess the strength of relationships between these factors and participants’ adoption behaviour, regression analysis was conducted, which depicted a significant contribution to the study’s model. Contributing to the literature on AI chatbots and self-directed learning, this research offers implications for educators, developers, and policymakers in designing and implementing AI chatbots effectively and ethically for education. Considering recent tech advancements and educational needs, institutions should consider integrating AI chatbots into their learning systems to enhance overall educational experiences and outcomes. With the limitation of considering less factors, this study can be a starting point of research on leveraging AI-based technologies towards achieving self-guided learning goals.
Keywords
Artificial intelligence, AI-Chatbots, Self-Directed Learning, Technology Adoption, Educational Practices, Technological factors
Ethical Statement
I, Antara Mukherjee, hereby state that the study is the authors' own original work, which has not been previously published elsewhere neither currently being considered for publication elsewhere. The paper reflects the authors' own research and analysis in a truthful and complete manner.
References
- Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15 (3), 1-13. https:// doi.org/10.30935/cedtech/13152
- Ameri, A., Khajouei, R., Ameri, A., & Jahani, Y. (2020). Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model. Education and Information Technology, 25, 419–435. https://doi.org/10.1007/s10639-019-09965-5
- Baker, B., Mills, K. A., McDonald, P., & Wang, L. (2023). AI, Concepts of Intelligence, and Chatbots: The “Figure of Man,” the Rise of Emotion, and Future Visions of Education. Teachers College Record: The Voice of Scholarship in Education, 125(6), 60-84. https://doi.org/10.1177/01614681231191291
- Cain, C. C., Buskey, C. D., & Washington, G. J. (2023). Artificial intelligence and conversational agent evolution – a cautionary tale of the benefits and pitfalls of advanced technology in education, academic research, and practice. Journal of Information, Communication and Ethics in Society, 21(4), 394-405. https://doi.org/10.1108/JICES-02-2023-0019
- Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245-276. https://doi.org/10.1207/s15327906mbr0102_10