Previously submitted to: JMIR Medical Education (no longer under consideration since May 20, 2026)
Date Submitted: Jan 22, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Enhancing cognitive engagement in fostering higher-order thinking during student discussion in medical education: a comparative study of GenAI-led, teacher-led, and blended models using Bloom's taxonomy
ABSTRACT
Background:
Background:
The integration of artificial intelligence (AI) with human expertise in blende learning models is widely recommended to address the limitations of using generative AI (GenAI) alone in medical education, especially in supporting high order thinking among peer discussions for case-based learning; however, empirical evidence supporting this approach remains limited.
Objective:
Objective:
This study aimed to evaluate engagement and cognitive levels in medical students' discussions for thyroid cancer cases using Bloom's taxonomy under three instructional models: teacher-led, GenAI-led, and human-AI blended.
Methods:
Methods:
In this quasi-experimental study, 120 medical students were assigned to analyze and evaluate thyroid cancer cases in the three models.
Results:
Results:
Surprisingly, quantitative analysis revealed that the GenAI-led group made significantly more frequent contributions than the blended model (Δ+133%, P<.001) and the teacher-led group (Δ+120%, P<.001). The total discussion length was highest for GenAI-led participants (403 characters vs. 245 in blended and 131 in teacher-led, P<.001). While the longest single contribution was comparable between the GenAI-led (167 characters) and blended groups (163 characters, P<.001), both significantly surpassed the teacher-led group (86 characters, P<.05). Guided by Bloom’s taxonomy, further analysis suggested that medical students engaged with materials uniquely designed by GenAI and teachers. In teacher-led group, the discussions focused predominantly on application-level content (94% vs. 76% in blended and 61% in GenAI-led, P<.001). In contrast, the GenAI-led group demonstrated significantly higher engagement than blended model in foundational knowledge (2.9 vs. 1.1%) and critical evaluation, including scrutiny of GenAI outputs (20.9% vs. 16.0%) and peer evaluation (7.5% vs. 1.7%).
Conclusions:
Conclusions:
The GenAI-led approach demonstrated superior efficacy in fostering discussions at foundational and evaluative cognitive levels, whereas blended and teacher-led models were less effective in stimulating overall discussion volume but more conducive to application-level learning.
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