Surgical education reimagined - the convergence of learning theories and artificial intelligence

Sep 30, 2026

Integration of artificial intelligence (AI) into surgical education is reshaping how surgical skills and medical knowledge are learned. The article analyzes how AI can be mapped to behaviourism, cognitivism, constructivism, humanism, and connectivism to enable personalised simulations, adaptive feedback, and networked learning platforms. A literature and theory review examines AI-driven tools for structured reinforcement, cognitive optimisation, experiential learning, individual growth, and collaboration, while also highlighting ethical risks such as privacy, algorithmic bias, and inequitable access.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Purpose

Materials and methods

Results

Conclusion

Introduction

AI in surgical education

Educational theories and feedback

Lee and Wong BMC Medical Education (2026) 26:38 BMC Medical Education [https://doi.org/10.1186/s12909-025-08039-0](https://doi.org/10.1186/s12909-025-08039-0) RESEARCH Open Access Surgical education reimagined: the convergence of learning theories and artificial intelligence Frances Lee 1* and Shing Wai Wong 1,2* Abstract Purpose The integration of artificial intelligence (AI) into surgical education is transforming the way surgical skills and knowledge are developed. This article examines how AI aligns with key educational theories—behaviourism, cognitivism, constructivism, humanism, and connectivism—to enhance learning through personalised simulations, adaptive feedback, and networked platforms. Materials and methods A review of literature and theoretical frameworks was conducted to analyse AI’s applications in surgical training. Key features include AI-driven tools for structured feedback, cognitive optimisation, experiential learning, individual growth, and collaboration through interconnected networks. The article also identifies ethical challenges, including data privacy, algorithmic bias, and equitable access. Results AI has the potential to revolutionise surgical education by fostering critical thinking, improving training outcomes, and expanding access to learning resources. However, risks such as over-reliance on automation, loss of hands-on experience, and superficial AI use (“AI theatre”) highlight the need for thoughtful and ethical implementation. Conclusion With a balanced and collaborative approach among educators, technologists, and healthcare professionals, AI can create dynamic, learner-centred environments. By addressing challenges, AI can support the development of skilled, compassionate surgeons equipped to navigate the complexities of modern medical practice. Key points AI transforms surgical education by aligning with key learning theories, enabling personalised, immersive, and adaptive learning environments. Integration of AI enhances structured reinforcement, cognitive optimisation, experiential learning, individual growth, and collaboration. Ethical challenges, such as data privacy, algorithmic bias, and equitable access, must be addressed to ensure fair and effective implementation. Over-reliance on AI risks reducing critical hands-on experience, underscoring the need for a balanced approach. *Correspondence: Frances Lee [fran. m.lee1@gmail.com](fran. m.lee1@gmail.com)[ ](fran. m.lee1@gmail.com)Shing Wai Wong [sw.wong@unsw.edu.au](sw.wong@unsw.edu.au) Full list of author information is available at the end of the article © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/. Lee and Wong BMC Medical Education (2026) 26:38 Page 2 of 9 Collaborative efforts among educators, technologists, and healthcare professionals can fully harness AI’s potential to develop competent and compassionate surgeons. Keywords Artificial intelligence in education, Surgical

FAQs

How does the article connect AI with learning theories in surgical education?

It aligns AI with behaviourism, cognitivism, constructivism, humanism, and connectivism to shape the type of feedback and learning support AI should provide.

What AI-enabled learning methods are emphasized in the study?

The article highlights personalised simulations, adaptive feedback, structured reinforcement, cognitive optimisation, experiential learning, and networked collaboration platforms.

What ethical challenges does the article identify for integrating AI into surgical training?

Key issues include data privacy, algorithmic bias, and equitable access to AI-enabled educational resources.

Sep 30, 2026

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