Exploring the Educational Potential of a Prototype AI System for Automated Research Note Generation: A Faculty Survey
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Objectives: Traditional evidence-based medicine (EBM) education often presents research as a finished product, which can obscure the dynamic inquiry process and limit students’ understanding of longitudinal decision-making. Because authentic research notes are rarely accessible, generative artificial intelligence (AI) can simulate the thought processes of researchers. This study evaluated the educational potential of a novel AI system that generates simulated research notes based on academic papers. Methods: A cross-sectional survey was conducted with 16 EBM faculty members from Korean medicine colleges. Participants engaged in a hands-on evaluation of a web-based system (utilizing Google Gemini 1.5 Flash API) that generates simulated research notes. A mixed-methods questionnaire comprising 23 Likert-scale items across six domains and six open-ended questions was used for the evaluation. Results: Participants provided moderately positive ratings, with ‘Educational Effectiveness’ (mean 3.97±0.84) and ‘Overall Assessment’ (3.97±0.96) receiving the highest scores. The system’s ability to portray researcher concerns realistically was also well-rated (4.19±0.83), while ‘Practical Applicability’ received the lowest score (3.70±1.01). Qualitative analysis confirmed the system’s educational novelty but highlighted significant concerns regarding AI reliability, including factual inaccuracies, and generated suggestions for practical implementation. Conclusions: This proof-of-concept study demonstrates that AI-generated research note simulations address a gap in process-oriented EBM learning. Faculty acknowledged its conceptual value for teaching longitudinal research thinking; however, substantial technical reliability and implementation challenges must be addressed before it can be utilized by students. While technical limitations may improve with advancements in AI, specific educational challenges require focused pedagogical research. Future efforts should prioritize content accuracy and instructional design for effective classroom integration.
Abstract
Objectives: Traditional evidence-based medicine (EBM) education often presents research as a finished product, which can obscure the dynamic inquiry process and limit students’ understanding of longitudinal decision-making. Because authentic research notes are rarely accessible, generative artificial intelligence (AI) can simulate the thought processes of researchers. This study evaluated the educational potential of a novel AI system that generates simulated research notes based on academic papers. Methods: A cross-sectional survey was conducted with 16 EBM faculty members from Korean medicine colleges. Participants engaged in a hands-on evaluation of a web-based system (utilizing Google Gemini 1.5 Flash API) that generates simulated research notes. A mixed-methods questionnaire comprising 23 Likert-scale items across six domains and six open-ended questions was used for the evaluation. Results: Participants provided moderately positive ratings, with ‘Educational Effectiveness’ (mean 3.97±0.84) and ‘Overall Assessment’ (3.97±0.96) receiving the highest scores. The system’s ability to portray researcher concerns realistically was also well-rated (4.19±0.83), while ‘Practical Applicability’ received the lowest score (3.70±1.01). Qualitative analysis confirmed the system’s educational novelty but highlighted significant concerns regarding AI reliability, including factual inaccuracies, and generated suggestions for practical implementation. Conclusions: This proof-of-concept study demonstrates that AI-generated research note simulations address a gap in process-oriented EBM learning. Faculty acknowledged its conceptual value for teaching longitudinal research thinking; however, substantial technical reliability and implementation challenges must be addressed before it can be utilized by students. While technical limitations may improve with advancements in AI, specific educational challenges require focused pedagogical research. Future efforts should prioritize content accuracy and instructional design for effective classroom integration.