Technology enhanced neuroanatomy teaching techniques: A focused BEME systematic review of current evidence: BEME Guide No. 75.

Background: In response to growing curriculum pressures and reduced time dedicated to teaching anatomy, research has been conducted into developing innovative teaching techniques. This raises important questions for neuroanatomy education regarding which teaching techniques are most beneficial for knowledge acquisition and long-term retention, and how they are best implemented. This focused systematic review aims to provide a review of technology-enhanced teaching methods available to neuroanatomy educators, particularly in knowledge acquisition and long-term retention, compared to traditional didactic techniques, and proposes reasons for why they work in some contexts.
Methods: Electronic databases were searched from January 2015 to June 2020 with keywords that included combinations of 'neuroanatomy,' 'technology,' 'teaching,' and 'effectiveness' combined with Boolean phrases 'AND' and 'OR.' The contexts and outcomes for all studies were summarised while coding, and theories for why particular interventions worked were discussed.
Results: There were 4287 articles identified for screening, with 13 studies included for final analysis. There were four technologies of interest: stereoscopic views of videos, stereoscopic views of images, augmented reality (AR), and virtual reality (VR). No recommendation for a particular teaching method was made in six studies (46%) while recommendations (from weak to moderate) were made in seven studies (54%). There was weak to moderate evidence for the efficacy of stereoscopic images and AR, and no difference in the use of stereoscopic videos or VR compared to controls.
Conclusions: To date, technology-enhanced teaching is not inferior to teaching by conventional didactic methods. There are promising results for these methods in complex spatial anatomy and reducing cognitive load. Possible reasons for why interventions worked were described including students' engagement with the object, cognitive load theory, complex spatial relationships, and the technology learning curve. Future research may build on the theorised explanations proposed here and develop and test innovative technologies that build on prior research.