This study evaluated GPT-based conversational agents in tasks related to healthcare provider stigma. The main finding was that GPT-4o models, using Role-Playing (RP) and Chain of Thought (CoT) techniques, outperformed other models in tasks such as defining healthcare provider stigma, identifying types of stigma, and explaining its consequences. The Personalized GPT model showed lower performance, particularly in areas related to treatment access, adherence, and stigma risk factors. These results suggest that advanced prompting techniques significantly enhance the agent’s ability to deliver complex and nuanced information about healthcare provider stigma. The study supports the potential of GPT-based agents as scalable educational tools for reducing stigma, especially in resource-limited settings.