PURPOSE: Residency programs must continue to restructure teaching and assessment of surgical skills to improve the documentation of Accreditation Council for Graduate Medical Education competencies. To improve teaching and documenting resident performance we developed a computer enhanced visual learning method that includes a curriculum and administrative reports. The curriculum consists of 1) study of a step-by-step surgical tutorial of computer enhanced visuals that show specific surgical skills, 2) a checklist tool to objectively assess resident performance and 3) a log of postoperative feedback that is used to structure deliberate practice. All elements of the method are repeated with each case performed. We used the Accreditation Council for Graduate Medical Education index case of orchiopexy to pilot this project. MATERIALS AND METHODS: All urology residents who trained at our institution from January 2006 to October 2007 performed orchiopexy using the computer enhanced visual learning method. The computer enhanced visual learning tutorial for orchiopexy consisted of customized computer visuals that demonstrate 11 steps or skills involved in routine inguinal orchiopexy, eg ligate hernia. The attending urologist rated resident competence with each skill using a 5-point Likert scale and provided specific feedback to the resident suggesting ways to improve performance. These ratings were weighted by case difficulty. The computer enhanced visual learning weighted score at entry into the clinical rotation was compared to the best performance during the rotation in each resident. RESULTS: Seven attending surgeons and 24 urology residents (resident training postgraduate years 1 to 8) performed a total of 166 orchiopexies. Overall the residents at each postgraduate year performed an average of 7 cases each with complexity ratings that were not significantly different among postgraduate year groups (average 2.4, 1-way ANOVA p not significant). The 7 attending surgeons did not differ significantly in assessment of skill performance or case difficulty (1-way ANOVA p not significant). Of the 24 residents 23 (96%) showed improvement in computer enhanced visual learning score/skill performance. In the entire group the average computer enhanced visual learning weighted score increased more than 50% from entry to best performance (137 to 234 orchiopexy units, paired t test p <0.0001). CONCLUSIONS: Computer enhanced visual learning is a novel method that enhances resident learning by breaking a core procedure into discrete steps and providing a platform for constructive feedback. Computer enhanced visual learning, which is a checklist tool, complies with Accreditation Council for Graduate Medical Education documentation requirements. Computer enhanced visual learning has wide applicability among surgical specialties.