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Abstract In contemporary medical education, learners are increasingly exposed to vast amounts of information while simultaneously relying on AI systems to generate explanations on demand. Yet AI can provide information, not understanding; without internal conceptual structures, even the most accurate explanation remains cognitively inert. The didactic models presented in this manuscript aim to bridge this gap by offering a standardized visual language for representing physiological feedback. Through conceptual compression and intuitive graphical structure, these models allow learners to “see” the underlying regulatory patterns of the musculoskeletal system—patterns that are often obscured in traditional text‑based instruction. By making causal architecture explicit, the framework supports both teaching and learning, enabling students to build the foundational mental models required to interpret, integrate, and apply complex physiological knowledge in the era of AI‑augmented reasoning.

Methodology Notice: All diagrams in this manuscript were created using the Dr Mihăilă Graphic System®, a standardized visual method for transforming causal relationships into intuitive geometric models (multi‑axis structures and circular‑arc feedback systems). This graphical methodology has been fully detailed in our foundational work and is applied here to the specific domain addressed in this manuscript.

Trademark Notice: Dr Mihăilă Graphic System® is an officially registered trademark under the European Union Intellectual Property Office (EUIPO).

Keywords: Musculoskeletal modeling, Osteoarthritis, Overuse injury, Muscle tension, Homeostasis, Negative feedback, Positive feedback.   

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