Training Empathetic Communication Skills in Medical Students With a Role-Prompted GPT-4o Chatbot: Quasi-Experimental Intervention Study

BACKGROUND: There is growing concern that artificial intelligence (AI) may diminish the quality of human relationships. However, in a context of widespread social importance (empathetic conversations between doctors and patients), AI can actually improve human conversational skills, potentially enhancing professional relationships. Recent advances in AI allow for realistically role-prompted counterparts for practicing professional conversations, enabling relational learning without the need for human counterparts. OBJECTIVE: This study aimed to show the effectiveness of AI chatbots for learning professional communicative skills in medical education. Specifically, we hypothesized that a single conversation with an AI chatbot improves communication skills in medical students across 4 different conversational competencies. METHODS: We conducted a quasi-experimental intervention study involving 4 distinct role-prompted scenarios (ie, shared decision-making, motivational interviewing, sexually transmitted diseases, and breaking bad news)-each designed to elicit in-depth empathic conversational skills aligned with key learning objectives in medical curricula. Students rated their competence for the 4 scenarios before and after a conversation with GPT-4o (OpenAI) using default settings, without fine-tuning. We expected higher perceived communication competence (PCC) in their conversation topic after the interaction compared with before the interaction in a 2-sided paired t test. Participants received AI-generated feedback, which they rated regarding adequacy. Post hoc analyses addressed gender and case effects, feedback adequacy, and prevalues in PCC. RESULTS: This study shows that a role-prompted GPT chatbot improves PCC in 162 medical students after a single conversation with mean of 13 (SD 4.8; 95% CI 12-14) prompt-response pairs. We found an increase in PCC with a mean difference of 0.94 (SD 1.64; 95% CI 0.69-1.20; Cohen d=0.58) from 5.89 (95% CI 5.55-6.23; scale 0-10) before the conversation to 6.83 (95% CI 6.55-7.12) after the conversation across 4 different patient role prompts. Furthermore, we found participants rating AI feedback of their conversation to be useful (mean 7.92, SD 1.61; 95% CI 7.67-8.17; scale 0-10), but feedback adequacy did not correspond to PCC increase (r=0.08; P=.32). CONCLUSIONS: Our results demonstrate how role-prompted GPT increases self-assessed communication competencies, introducing a novel tool for teaching relational learning. Our results present a starting point for using AI in education, particularly teaching communication in professional roles. On the basis of our findings in medical education, we anticipate further studies to investigate conversational training between lawyers and clients, marketers and customers, or managers and employees. Our research thus has implications for any field with a need for conversational training and relational learning.