Sukiman, Hamzah and Amjad, Nasser Mohammad (2024) Assessing the impact of AI-driven adaptive learning platforms on student engagement, motivation, confidence and perception on feedback in clinical history-taking. Medicine & Health, 19 (Suppl. 9). p. 20. ISSN 1823-2140 E-ISSN 2289-5728
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Abstract
Introduction: Undergraduate medical students typically report challenges in history-taking during their clinical years. Artificial Intelligence (AI)-driven adaptive learning platforms have the potential to enhance the experience by providing personalized feedback. Aim/Purpose/Objective: This study aims to determine whether AI-driven adaptive learning platforms improve medical students’ engagement, motivation, confidence, and perception on feedback in clinical history-taking. Method: A mixed-methods pre- and post-intervention design was used with 56 undergraduate medical students in their 3rd and 5th year of study. A pre-intervention survey was administered, assessing engagement, motivation, confidence, and feedback. Students are then given access to a ChatGPT4o-based platform that records their historytaking sessions with actual patients. The GPT is configured to only refer to verified surgical textbooks and the faculty’s marking rubrics when assessing and giving feedback to students. A post-intervention survey was then administered after 6-9 weeks, assessing the same domains. Results: Quantitative results showed significant improvements in engagement (pre: 3.43, post: 3.95, p=0.002), motivation (pre: 3.39, post: 3.73, p=0.043), and confidence (pre: 2.59, post: 3.3, p<0.001) for students using the AI platform. Additionally, they reported higher perceptions of feedback usefulness (pre: 3.61, post: 4.13, p=0.006) and scoring accuracy compared to peer feedback. Qualitative data revealed themes of enhanced engagement due to real-time feedback and increased confidence from personalized, structured guidance. Conclusion: AI-driven platforms significantly improve students’ engagement, motivation, confidence, and perception on feedback in clinical history-taking. This study’s main limitations are the small sample size and heterogeneity of students’ proficiency in technology. The platform has the potential to complement traditional bedside teaching methods and has the added benefit of enabling students to practice in a self-directed manner.
| Item Type: | Article (Abstract) |
|---|---|
| Uncontrolled Keywords: | Keywords: Artificial intelligence; engagement; history-taking; medical students; motivation |
| Subjects: | L Education > L Education (General) R Medicine > R Medicine (General) R Medicine > RD Surgery |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Medicine > Department of Surgery |
| Depositing User: | Dr. Hamzah Sukiman |
| Date Deposited: | 26 Jan 2025 10:03 |
| Last Modified: | 26 Jan 2025 10:03 |
| URI: | http://irep.iium.edu.my/id/eprint/118731 |
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