HOW CAN 3D DIGITAL VISUALISATION BE DESIGNED TO REDUCE NEUROANATOMY-SPECIFIC LEARNING BARRIERS?
Anglia Ruskin University (UNITED KINGDOM)
About this paper:
Conference name: 18th International Conference on Education and New Learning Technologies
Dates: 29 June-1 July, 2026
Location: Palma, Spain
Abstract:
Background:
Neuroanatomy remains a persistent source of difficulty in medical education, with neurophobia driven by poor spatial understanding, high cognitive load, and limited integration of structure with clinical function. Concurrently, 3D digital visualisation technologies have rapidly expanded, yet their educational value remains inconsistently demonstrated and insufficiently grounded in learning theory.
Aim:
This review critically synthesises the use of 3D digital visualisation in anatomy education and reframes its role through a theory-informed lens, with specific emphasis on addressing neuroanatomy learning challenges.
Methods:
A structured narrative synthesis was conducted using a curated dataset of contemporary studies on 3D visualisation modalities, including interactive models, virtual and augmented reality, and photogrammetry-based reconstructions. Findings were analysed thematically and interpreted using cognitive load theory and multimedia learning principles.
Results:
3D visualisation consistently enhances spatial understanding, particularly in complex domains such as neuroanatomy, and is associated with increased learner engagement and confidence. However, evidence for knowledge retention is variable, and many interventions lack pedagogical structuring. The effectiveness of 3D tools appears contingent on instructional design features, including segmentation, guided interaction, and integration with assessment. High-fidelity approaches, such as photogrammetry, remain underutilised despite their potential to improve anatomical realism and conceptual integration.
Conclusions:
3D digital visualisation is most effective when embedded within theory-informed instructional frameworks rather than used as a standalone technological enhancement. We propose an integrated model combining high-fidelity 3D representations, structured learning design, and emerging AI-supported guidance to address neuroanatomy-specific learning barriers. This approach offers a scalable pathway to reducing neurophobia and advancing anatomy education beyond technology-driven innovation toward pedagogically grounded transformation.Keywords:
Neuroanatomy, neurophobia, 3D-visualization, photogrammetry, augmented-reality, virtual-reality, medical-education, neurology.