Purpose
Clinical fieldwork in genetic counseling education is constrained by limited training sites, shortages of clinical supervisors, and administrative burdens. Simulation-based learning, including role-play, has therefore been used to support development of counseling competencies. This study described the development, implementation, operational structure, and feasibility of a role-play-based practicum in a graduate genetic counseling program.
Methods
This retrospective descriptive report was based on Genetic Counseling Practicum, a spring 2024 course for 11 third-semester students in a graduate genetic counseling program in South Korea. Twenty-two role-play scenarios were developed, including 11 hereditary cancer cases and 11 pediatric or adult genetic disorder cases. The scenarios simulated pretest and posttest encounters during a 6-week, 2-block practicum. Students alternated between counselor and counselee roles, recorded sessions outside class time, and received domain-guided peer and faculty feedback.
Results
The scenarios covered patient ages from infancy to late adulthood and included somatic and germline pathogenic variants, a negative multigene panel result, and chromosomal or epigenetic abnormalities. All 22 planned sessions were completed and recorded as scheduled, and all 11 students completed their assigned sessions. The planned assignment, recording, review, and feedback procedures were completed without documented omissions. Approximately 11 scheduled class hours were devoted to feedback, and no external clinical sites or supervisors were required.
Conclusion
This study presents an operationally feasible model for a role-play-based practicum with diverse scenarios in a graduate genetic counseling curriculum. It may serve as a practical reference for programs considering similar simulation-based training.

Generative artificial intelligence (GenAI), including large language models such as GPT-4 and image-generation tools like DALL-E, is rapidly transforming the landscape of medical education. These technologies present promising opportunities for advancing personalized learning, clinical simulation, assessment, curriculum development, and academic writing. Medical schools have begun incorporating GenAI tools to support students’ self-directed study, design virtual patient encounters, automate formative feedback, and streamline content creation. Preliminary evidence suggests improvements in engagement, efficiency, and scalability. However, GenAI integration also introduces substantial challenges. Key concerns include hallucinated or inaccurate content, bias and inequity in artificial intelligence (AI)-generated materials, ethical issues related to plagiarism and authorship, risks to academic integrity, and the potential erosion of empathy and humanistic values in training. Furthermore, most institutions currently lack formal policies, structured training, and clear guidelines for responsible GenAI use. To realize the full potential of GenAI in medical education, educators must adopt a balanced approach that prioritizes accuracy, equity, transparency, and human oversight. Faculty development, AI literacy among learners, ethical frameworks, and investment in infrastructure are essential for sustainable adoption. As the role of AI in medicine expands, medical education must evolve in parallel to prepare future physicians who are not only skilled users of advanced technologies but also compassionate, reflective practitioners.

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