BACKGROUND: Traditional orthopedic teaching methods have inherent limitations in conveying complex three-dimensional anatomical relationships essential for surgical planning and execution. Three-dimensional (3D) printing technology offers a potential solution to these educational challenges, but systematic evaluation of its specific educational impact in orthopedic residency training remains limited. PURPOSE: This study aimed to evaluate the educational efficacy of in-house 3D-printed patient-specific anatomical models in orthopedic training through assessment of three core domains: anatomical comprehension, surgical planning proficiency, and clinical teaching utility. METHODS: In this analytical observational study, paper-based questionnaires were distributed to 145 orthopedic residents at Hubei University of Medicine who participated in clinical teaching sessions using 3D-printed anatomical models between January 2025 and March 2025. Participants rated their experiences on a 10-point Likert scale. Data were analyzed using descriptive statistics. RESULTS: The response rate was 81.4% (n=118). A majority (85.6%) of residents reported enhanced understanding of complex anatomical structures. First-year residents demonstrated higher satisfaction (mean score 7.9) compared to more advanced trainees (mean scores 7.3 and 6.9). Small group settings (4-6 participants) were preferred by 76.3% of respondents. Physical manipulation of models received the highest educational value rating (mean score 8.1). Primary limitations included production time (45.8%), material durability (38.6%), and limited model varieties (35.6%). Nearly half (43.2%) of residents requested more frequent practice sessions. CONCLUSION: 3D-printed anatomical models significantly enhance orthopedic resident education, particularly for complex structures and junior trainees. Small-group, instructor-guided implementation maximizes educational benefits. When strategically integrated into existing curricula, in-house production enables widespread access across training levels with minimal resource constraints.