Generative Artificial Intelligence in Medical Education and Participatory Research for Social Action: A Human and AI Comparative Analysis

Participatory qualitative methods such as Photovoice are increasingly used to link research with social action. Recent advances in artificial intelligence (AI) may enhance data analysis, inference, and action planning within such participatory approaches. This study explored medical students’ perceptions of social justice using conventional Photovoice analysis and assessed the potential contribution of generative AI (genAI). Nine students joined a six-week seminar, “Exploring the Concept of Social Justice Using Photovoice.” An initial two-hour session covered ethics, the Photovoice framework, and photography techniques. Participants then captured images reflecting their views on social justice, wrote narratives, and engaged in guided group discussions. Human researchers and students conducted a three-stage Photovoice analysis: 1) selecting photographs, 2) contextualizing them with participant narratives, and 3) inductively coding themes. To explore how AI might support data analysis, the research team analyzed the same data with five generative tools, including Sonix, ChatGPT, and Copilot. AI-generated themes and visual representations were compared with human-derived results for congruence, depth, and suggested action steps. Conventional analysis identified five major themes: (1) Social Justice and Inequality, (2) Contradictions and the Costs of Justice, (3) Community and Collective Action, (4) Environment and Environmental Justice, and (5) Perception, Subjectivity, and Perspective. AI-assisted analysis yielded six unified themes that closely aligned with human findings. Traditional Photovoice images conveyed authentic, lived experiences and strong emotional meaning, providing a powerful foundation for advocacy. AI-generated images and thematic summaries offered efficiency, creativity, and reduced researcher bias, improving generalizability. However, they lacked the emotional depth and contextual nuance present in participant-created visuals.