Engagement and Satisfaction in Team-Based Learning and Generative AI for Medical Terminology Learning

Abstract

While medical terminology is commonly taught to healthcare or science-based students, business and management students in health-related programs often struggle due to the complexity of medical word roots, prefixes, and suffixes. The incorporation of diverse teaching approaches helps bridge the learning gap by supporting students in mastering essential medical terms that are critical for their future roles in health administration and management. The objectives of this study were to assess the satisfaction, engagement, and academic achievement of students in medical terminology course using Team Based Learning (TBL) and artificial intelligence (AI) powered learning versus lecture-based method. Methods: A cross-sectional study was conducted from March to July 2025 within a health administration undergraduate program at a major public university in Malaysia, evaluating three instructional delivery modes across a 14-week course using paired sample t tests. A total of 79 students taking the Health Terminological course participated in the study. Results: Academic achievement and engagement were significantly higher under TBL and AI powered learning compared to traditional lectures. However, these gains were not reflected in the affective domain, as overall student satisfaction levels remained comparable across all three instructional methods. Conclusion: These findings reveal that a significant engagement-satisfaction trade-off is at play. To capture the full value of active methods, instructional design must address this imbalance, prioritizing not only cognitive engagement but also efforts to strategically mitigate the perception of excessive workload so that increased rigor fully translates into positive affective and learning outcomes.