ABSTRACT
Educational technologies in veterinary medicine aim to train veterinarians faster and improve clinical outcomes. COVID-19 pandemic, shifted face-to-face teaching to online, thus, the need to provide effective education remotely was exacerbated. Among recent technology advances for veterinary medical education, extended reality (XR) is a promising teaching tool. This study aimed to develop a case resolution approach for radiographic anatomy studies using XR technology and assess students' achievement of differential diagnostic skills. Learning objectives based on Bloom's taxonomy keywords were used to develop four clinical cases (3 dogs/1 cat) of spinal injuries utilizing CT scans and XR models and presented to 22 third-year veterinary medicine students. Quantitative assessment (ASMT) of 7 questions probing 'memorization', 'understanding and application', 'analysis' and 'evaluation' was given before and after contact with XR technology as well as qualitative feedback via a survey. Mean ASMT scores increased during case resolution (pre 51.6% (±37%)/post 60.1% (± 34%); p < 0.01), but without significant difference between cases (Kruskal-Wallis H = 2.18, NS). Learning objectives were examined for six questions (Q1-Q6) across cases (C1-4): Memorization improved sequentially (Q1, 2 8/8), while Understanding and Application (Q3,4) showed the greatest improvement (26.7%-76.9%). Evaluation and Analysis (Q5,6) was somewhat mixed, improving (5/8), no change (3/8) and declining (1/8).Positive student perceptions suggest that case studies' online delivery was well received stimulating learning in diagnostic imaging and anatomy while developing visual-spatial skills that aid understanding cross-sectional images. Therefore, XR technology could be a useful approach to complement radiological instruction in veterinary medicine.