Currently accepted at: JMIR Medical Education
Date Submitted: May 16, 2025
Open Peer Review Period: May 16, 2025 - Jul 11, 2025
Date Accepted: Sep 2, 2026
(closed for review but you can still tweet)
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/77606
The final accepted version (not copyedited yet) is in this tab.
Learning clinical reasoning with virtual patients: A randomized controlled trial of adaptive vs static feedback in individual and collaborative learning modes
ABSTRACT
Background:
Clinical reasoning is a key skill of the medical profession. In many virtual patient environments, the students enter the diagnoses, and all students receive the same feedback with an explanation why a certain diagnosis is considered correct. Results of meta-analyses highlight the benefits of feedbacking information to students based on their individual answers. Such adaptive feedback is time and resource demanding.
Objective:
We propose computer-supported adaptive feedback as an interactive, resource-optimised and scalable alternative.
Methods:
In the current study we compare static expert feedback agains computer supported adaptive feedback in two learning modes, individual and collaborative learning modes. Overall 105 students completed a pre and post test, consisting of 10 multiple choice items and 12 key feature items. In the meantime they diagnosed 8 virtual patients with either adaptive feedback or static feedback, either in the collaborative or individual learning mode.
Results:
Results indicate that students who received computer supported adaptive feedback outperformed students who received static feedback in the posttest independent from the learning mode. Students who worked in the collaborative learning mode had a higher diagnostic accuracy in the learning phase, but not in the posttest, independent from the feedback given.
Conclusions:
Considering the novelty of the system in itself and the presentation of the adaptive feedback to the students the results are promising. With future development and implementation of artificial intelligence in the generation of answers the learning of medical students. Until then an NLP-based system, such as the one presented in this study, seems to be a viable solution to provide a large number of students with elaborated adaptive feedback. Clinical Trial: 17-250
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.