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Surgery: Is there an IDEAL WAY to LEARN?

Ditch the one-size-fits-all approach to learning – it's a downer, right? But hey, here's the scoop: there's a silver lining! Get ready for some edutainment as we dive into a teaching method  – adaptive learning platforms. 🚀 Studies show it's the real deal. Among 37 recent studies that examined the effects of adaptive learning on learning outcomes, an overwhelming majority of 86% (32 studies) reported positive effects (1).

PROFICIENCY is introducing an adaptive learning platform into the surgical curriculum. This platform, connected to various types of simulators, harnesses the power of computer algorithms and artificial intelligence. Its purpose is to seamlessly engage with trainees, delivering tailored resources and activities that specifically address the unique needs of each individual. Drawing insights from diverse fields such as computer science, AI, psychometrics, education, psychology, and brain science, this approach is a holistic integration of cutting-edge knowledge.  


This approach addresses the individual needs of assistant surgeon in PROFICIENCY. It creates a more inclusive and personalized learning experience and provides evidence-based feedback to ensure continuous improvement for every individual. 


In the demanding realm of surgery, where high performance is non-negotiable, this adaptive learning platform aligns expectations with the individual assistant surgeon's learning style, time constraints, preferences, and strengths. It's a step towards a more tailored and effective educational journey, acknowledging and accommodating the uniqueness of each learner within the training program. 


Get more insight into the modern learning concepts in our next post. 

Stay curious - follow #SurgicalProficiency 



(1) Wang, Shuai; Christensen, Claire; Cui, Wei; Tong, Richard; Yarnall, Louise; Shear, Linda; Feng, Mingyu (2023-02-17). "When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction". Interactive Learning Environments. 31 (2): 793–803. doi:10.1080/10494820.2020.1808794ISSN 1049-4820S2CID 225186350. 


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