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Background <p>Computer-based learning (CBL) has emerged as a transformative approach in surgical education, with remote teaching (RT) and self-directed virtual teaching (SDVT) offering scalable and, flexible alternatives to traditional face-to-face instruction. This systematic review aims to qualitatively analyse the current evidence on RT and SDVT for surgical skill training.</p> Methods <p>In July 2023, we conducted a structured search of PubMed, Cochrane Library, Scopus, and Web of Science. To enable a clear evaluation of CBL, particularly by examining retained knowledge, we included only randomized controlled trials (RCT) and prospective cohort studies. Data extraction followed a pretested Excel form, and qualitative synthesis adhered to PRISMA principles.</p> Results <p>Twenty seven studies (15 RCTs, 12 prospective cohorts) published between 2006 and 2023 met the inclusion criteria. Despite the heterogenicity of the included studies, key findings show that students completed RT achieved competency outcomes equivalent to traditional learning for basic suturing, knot-tying, and fundamental laparoscopic skills in most of the studies. SDVT matched traditional learning for basic tasks but underperformed in complex procedures. Hybrid models combining SDVT with real-time feedback enhanced learner autonomy, engagement and skill retention. Lastly, CBL modalities were well received by trainees, offering time- and cost advantages, as well as providing a teaching platform for low- and middle-income countries.</p> Conclusions <p>CBL, through RT and SDVT, provides an effective adjunct to traditional surgical training, particularly for foundational technical skills. Integrating the flexibility of SDVT with synchronous feedback during RT can optimize psychomotor learning while maintaining instructional oversight. Future research should standardize outcome measures, assess long-term skill retention, and explore implementation in resource-limited settings.</p>

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Computer-based surgical skill training: a systematic review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月26日

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  • Lars Andreas Morsund,
  • Shraddha Singh,
  • Agastya Patel,
  • Francesco Lancellotti,
  • Thomas Satyadas

摘要

Background

Computer-based learning (CBL) has emerged as a transformative approach in surgical education, with remote teaching (RT) and self-directed virtual teaching (SDVT) offering scalable and, flexible alternatives to traditional face-to-face instruction. This systematic review aims to qualitatively analyse the current evidence on RT and SDVT for surgical skill training.

Methods

In July 2023, we conducted a structured search of PubMed, Cochrane Library, Scopus, and Web of Science. To enable a clear evaluation of CBL, particularly by examining retained knowledge, we included only randomized controlled trials (RCT) and prospective cohort studies. Data extraction followed a pretested Excel form, and qualitative synthesis adhered to PRISMA principles.

Results

Twenty seven studies (15 RCTs, 12 prospective cohorts) published between 2006 and 2023 met the inclusion criteria. Despite the heterogenicity of the included studies, key findings show that students completed RT achieved competency outcomes equivalent to traditional learning for basic suturing, knot-tying, and fundamental laparoscopic skills in most of the studies. SDVT matched traditional learning for basic tasks but underperformed in complex procedures. Hybrid models combining SDVT with real-time feedback enhanced learner autonomy, engagement and skill retention. Lastly, CBL modalities were well received by trainees, offering time- and cost advantages, as well as providing a teaching platform for low- and middle-income countries.

Conclusions

CBL, through RT and SDVT, provides an effective adjunct to traditional surgical training, particularly for foundational technical skills. Integrating the flexibility of SDVT with synchronous feedback during RT can optimize psychomotor learning while maintaining instructional oversight. Future research should standardize outcome measures, assess long-term skill retention, and explore implementation in resource-limited settings.