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Training the morphological mindset: An educational framework for teaching morphological reasoning in the laboratory
1DIMES, Department of Experimental Medicine, Human Anatomy, MorphoLAB, University of Genoa, Genoa, Italy.
Abstract:
In contemporary anatomy and histology education, students increasingly gain access to large image repositories and advanced imaging technologies, yet explicit instruction in morphological reasoning often remains implicit. As a result, learners may become proficient at recognizing structures while lacking the interpretative skills needed to describe visual evidence, relate form to function, and generate scientifically meaningful questions from images. This discursive article presents an educational framework that makes morphological reasoning explicit within laboratory-based training. Developed in the MorphoLAB training environment, the approach integrates anatomy, histology, and imaging-based observation across health sciences curricula and within an active research workflow. Rather than prioritizing technical proficiency as an endpoint, the model foregrounds the cognitive processes and identity formation that underpin expert interpretation. At its core are 10 guiding principles-the "Ten Rules of the Morphologist"-which provide a shared vocabulary for observation, description, interpretation, integration, and critical questioning. The rules are introduced early and reinforced through structured image observation, reflective notebook practices, and guided dialog that externalizes expert reasoning. Reflective observations from routine supervision suggest that this explicit focus supports more precise descriptive language, greater interpretative autonomy, and increased tolerance for ambiguity in complex images. By shifting attention from technique-centered instruction to the cultivation of a morphological mindset, the framework offers a transferable, low-burden strategy for educators seeking to strengthen students' capacity to think with images in an educational landscape increasingly shaped by digital imaging and artificial intelligence.
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