WONG Lik Wei,*, Embang Johann Emilio GONZALES2, Valerie Ying Hui TAN2, Luis Angelo MORALES3, Amanda Huee-Ping WONG1, and HOOI Shing Chuan1
1Department of Physiology, Yong Loo Lin School of Medicine (YLLSOM), National University of Singapore (NUS)
2Alice Lee Centre for Nursing Studies, YLLSOM, NUS
3NUS College
Sub-Theme
Building Technological and Community Relationships
Keywords
AI chatbot, cognitive, motivation, Self-determination Theory, learning
Category
Paper Presentation
Introduction
The rapid advancements in Artificial Intelligence (AI) technologies have prompted us to re-evaluate the future of our education. Although AI has great potential to enhance teaching and learning, its role in pedagogy and instruction has not been fully studied. Motivation has been shown to influence students’ learning approaches, their engagement level, persistence in accomplishing goals, and thinking processes (Chiu, 2022). Ryan and Deci (2017; 2020) propose Self-Determination Theory (SDT), suggesting that autonomous motivation is the preferred type of motivation for learning as it can lead to greater engagement and persistence. Studies have found that university students who engaged with an AI chatbot demonstrated greater intrinsic motivation, possibly because they felt more comfortable and engaged during these interactions. This may lead to increased expression of ideas (Yin et al., 2021) and higher levels of motivation (Fryer et al., 2019).
Rationale of Study
As AI technology continues to advance, its impact on the education of medical and health professionals will be significant. While some argue that it may have negative implications for students’ learning, educators should consider incorporating AI technology into their teaching methods to enhance students’ learning experiences. The aim of this study is to investigate the potential of AI chatbots as a pedagogical tool for enhancing learning and motivation among medical students.
Methodology
First-year undergraduate medical students enrolled in the Cardiovascular System module during Academic Year 2023/24 participated in this study. As part of a flipped classroom approach, students engaged in self-directed learning using eBooks, online lectures, and quizzes before attending in-person discussions. They were encouraged to submit questions through a designated Question and Answer (Q&A) link and use ChatGPT to obtain answers, with teachers offering clarification as needed. To evaluate the AI chatbot’s impact on motivation, we used the established SDT and Intrinsic Motivation Inventory (IMI) in a post-course anonymous survey questionnaire. The survey included both Likert-scale and open-ended items on the perceived strengths and limitations of ChatGPT. Additionally, 31 student-generated questions were analysed using Bloom’s taxonomy to evaluate cognitive engagement.
Key Findings
Out of the 57 students who completed the survey, 46 (80.7%) used ChatGPT during their studies. The overall satisfaction score was 3.81 ± 0.89, with autonomy rated highest (4.07 ± 0.77), followed by competence (4.00 ± 0.78) and relatedness (3.35 ± 1.11). Students reported high levels of interest (3.80 ± 0.88) and perceived value (4.25 ± 0.69) in using ChatGPT. Analysis of the student questions showed that 58.06% (18/31) fell under the “Apply” or “Analyse” categories of Bloom’s taxonomy, while 41.94% (13/31) were at the “Understand” level. Students appreciated ChatGPT for providing fast, accessible, and easy-to-understand answers that supported comprehension and sparked further inquiry. However, concerns were raised regarding its accuracy, reliability, and lack of critical thinking. The findings suggest that ChatGPT helped students experience greater autonomy (freedom to ask questions) and competence (receiving clear explanations). High ratings of usefulness indicate an increase in task value. While ChatGPT supported surface to intermediate cognitive engagement, higher-order thinking appeared to require more instructor facilitation.
Significance of the Study
This study demonstrates that AI chatbots like ChatGPT can foster intrinsic motivation, encourage idea generation, and support cognitive engagement among medical students. When integrated thoughtfully, such tools can supplement traditional educational methods and enhance learning outcomes. Importantly, AI should not replace instructors but rather serve as part of a collaborative human-AI teaching model. While ChatGPT effectively promotes basic and moderate-level engagement, educators play a crucial role in guiding students toward deeper critical thinking. The study supports the relevance of SDT theory in digital learning environments, while also emphasising the need for pedagogical scaffolding to optimise higher-level learning.
References
Chiu, T. K. F. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(sup1), S14-S30. https://doi.org/10.1080/15391523.2021.1891998
Fryer, L. K., Nakao, K., & Thompson, A. (2019). Chatbot learning partners: Connecting learning experiences, interest and competence. Computers in Human Behavior, 93, 279-289. https://doi.org/10.1016/j.chb.2018.12.023
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press.
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Yin, J., Goh, T.-T., Yang, B., & Xiaobin, Y. (2021). Conversation technology with micro-learning: The impact of chatbot-based learning on students’ learning motivation and performance. Journal of Educational Computing Research, 59(1), 154-177. https://doi.org/10.1177/0735633120952067