This research aimed to improve pediatric knee injury diagnosis as indicated by effusion by developing a systematic process for designing and validating biofidelic training models using small-scale manufacturing techniques. The problem addressed was clinicians' lack of exposure to pediatric populations and opportunities to practice the patellar tap test. The tap test is a subjective physical examination prone to error, especially in overweight patients. In turn leading to costly medical imaging to confirm diagnoses, potentially delaying patient care.The project developed a novel structured ten-step engineering process. First, clinical imaging data was analyzed to identify key anatomical features, creating four distinct trainer models representing healthy weight and overweight pediatric patients with either physiologic or pathologic effusions. Computational modeling was employed to optimize material properties and mechanical behaviors, ensuring the models accurately simulated pediatric knee biomechanics. Fabrication involved additive manufacturing techniques, and the models were evaluated through benchtop mechanical testing and clinician feedback. Results showed that the school-age family of knees was best represented by four subpopulations based on two characteristics: weight class (overweight/healthy weight) and effusion determinations (pathologic/physiologic). The biofidelic process improved the mechanical response of trainers, but further refinements could be made. Feedback from clinicians confirmed the trainers' effectiveness in mimicking the feel of the exam and the potential as a training tool. In conclusion, this research established a replicable design framework for developing realistic pediatric trainers. The defined engineering process offers a scalable approach for creating models that improve clinical skills, potentially reducing reliance on costly imaging procedures. Future recommendations include refining material properties for improved realism, expanding trainer design to other physical assessments, and incorporating iterative design practices to enhance model fidelity and educational outcomes.