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Cited by 87Artificial Intelligence in Healthcare and EducationArtificial Intelligence in EducationInnovations in Medical Education

Generative artificial intelligence in medical education: enhancing critical thinking or undermining cognitive autonomy?

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Izquierdo-Condoy, Juan S, Arias-Intriago, Marlon, Tello-De-la-Torre, Andrea, Busch, Felix, Ortiz-Prado, Esteban·2025·Journal of Medical Internet Research
DOI 10.2196/76340PMC PMC12624298

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Abstract

Generative artificial intelligence (GenAI) enables the production of coherent and contextually relevant text by processing large-scale linguistic datasets. Tools such as ChatGPT, Gemini, Claude, and LLaMA are increasingly integrated into medical education, assisting students with a range of tasks, including clinical reasoning, literature review, scientific writing, and formative assessment. Although these tools offer significant advantages in terms of productivity, personalization, and cognitive support, their impact on critical thinking—a cornerstone of medical education—remains uncertain. The aim of this viewpoint paper is to critically assess the influence of GenAI on critical thinking within medical training, examining both its potential to enhance cognitive skills and the risks it poses to cognitive autonomy. Users have reported increased efficiency and improved linguistic output; however, concerns have also been raised regarding the risk of cognitive overreliance. Current evidence presents a mixed picture, indicating both improvements in learner engagement and potential drawbacks such as passivity or susceptibility to misinformation. Without curricular integration that prioritizes ethical use, prompt engineering, and critical evaluation, GenAI may compromise the cognitive autonomy of medical students. Conversely, when thoughtfully embedded into pedagogical frameworks, these tools can act as cognitive enhancers—supporting, rather than replacing, clinical reasoning. Medical education must adapt to ensure that future physicians engage with GenAI in a critical, ethical, and context-aware manner, especially in complex decision-making scenarios. This transformation demands not only technological fluency but also reflective practice and sustained oversight by faculty and academic institutions.

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  • Appropriate use of AI devices can enhance academic achievement, boost interest in learning, and improve clinical decision-making.

    “The initial empirical data indicate that in case of appropriate use, AI devices have the ability to enhance academic achievement, boost the interest in learning, and improve clinical decision-making.”

    From HOW AI TOOLS (LIKE CHATGPT/GEMINI) AFFECT CRITICAL THINKING IN NURSING STUDENTS. A SYSTEMATIC REVIEW (2026) · cites this paper as [6]

    The Empirical Evidence section reports improved self-directed learning and engagement with a structured AI tool, while the Abstract says thoughtfully integrated GenAI can support clinical reasoning. It does not establish improved clinical decision-making, and it reports no significant exam-score difference between students who used GenAI to study and those who did not. No page numbers are available in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Current evidence consistently indicates that these positive outcomes depend heavily on pedagogical framing, assessment design, and AI literacy development rather than on the technology itself.

    “However, current evidence consistently indicates that these positive outcomes depend heavily on pedagogical framing, assessment design, and AI literacy development rather than on the technology itself (Izquierdo-Condoy et al., 2025).”

    From Between cognitive offloading and critical autonomy: A systematic review of the epistemic implications of generative AI in higher education. (2026) · cites this paper as Izquierdo-Condoy et al., 2025

    The Conclusions and Curriculum Innovation sections say that intentional pedagogical design, faculty guidance, and digital literacy matter more than simply adopting GenAI. But Curriculum Innovation describes the evidence on critical thinking as mixed, not consistent, and the cited passages do not establish assessment design as a determinant of positive outcomes. The Limitations section also cautions that the paper is a theoretical perspective without a systematic search. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • It extends existing concerns about AI dependency by identifying the specific epistemic operations that may be displaced when plausible AI-generated outputs are accepted without evidentiary scrutiny.

    “It extends existing concerns about AI dependency by identifying the specific epistemic operations that may be displaced when plausible AI-generated outputs are accepted without evidentiary scrutiny (Izquierdo-Condoy et al., 2025;Raitskaya and Tikhonova, 2025).”

    From Between cognitive offloading and critical autonomy: A systematic review of the epistemic implications of generative AI in higher education. (2026) · cites this paper as Izquierdo-Condoy et al., 2025

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Sorted by citations

Authors
Izquierdo-Condoy, J. S., Arias-Intriago, M., Tello-De-la-Torre, A., Busch, F., & Ortiz-Prado, E.
Affiliations
  • Universidad de Las Américas (EC)
  • Technical University of Munich (DE)
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2025-11-03
Volume
27
Pages
e76340
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/76340
PMC
PMC12624298
OpenAlex
W4414436578
Cited by
87
References
10
Citations by year
2026: 86 · 2025: 1
FWCI
20.94
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
Keywords
generative AI, medical education, critical thinking, cognitive autonomy, clinical reasoning, prompt engineering, AI ethics in education, misinformation, student engagement, reflective practice, large language models
MeSH terms
Generative Artificial Intelligence, Artificial Intelligence, Cognition, Education, Medical, Humans, Review Literature as Topic, Students, Medical, Thinking

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