Abstract:

This paper explores the evolution of the FotoDialogo Method©, an arts-based and participatory research approach that integrates visual storytelling and dialogue to elicit life-based narratives in multilingual and multicultural community settings. Originally developed in the 1990s within a community-based organization in Massachusetts, the method combined oral histories and hand-drawn illustrations inspired by the life stories of Latina women participating in health and social education programs. These codifications—black-and-white drawings depicting daily experiences of work, migration, motherhood, and resilience—served as visual catalysts for collective reflection, self-discovery, and empowerment.

Grounded in Paulo Freire’s Thematic Investigation Model and Henry Murray’s Thematic Apperception Test (TAT), the FotoDialogo Method uses images as projective techniques to stimulate storytelling and uncover the sociocultural meanings embedded in lived experience. The original study demonstrated how marginalized women could use these visual dialogues to construct personal and collective identities while confronting structural inequalities in health, education, and social participation.

The current iteration of the project examines how Artificial Intelligence (AI) can extend this participatory process by generating culturally adaptive images derived from participants’ narratives. Using AI tools such as ChatGPT and DALL·E, the researcher produces visual prompts based on thematic elements drawn from interviews and focus groups conducted with migrant and underserved populations in community health education contexts. These AI-generated images—depicting multilingual dialogue scenes, intergenerational caregiving, or cross-cultural encounters—are juxtaposed with the original hand-drawn illustrations to analyze how participants perceive, interpret, and emotionally respond to each type of image.

The work presents comparative examples of both hand-drawn and AI-generated visuals used in participatory sessions with migrant community educators and health workers. Through iterative dialogue, participants engage with these images to reflect on health literacy, gender roles, cultural belonging, and access to care. Early findings indicate that while hand-drawn images evoke familiarity and authenticity, AI-generated visuals offer inclusivity and flexibility, enabling multilingual and multicultural adaptation. Together, they expand the range of visual entry points for community-based dialogue and education.

This research underscores the transformative potential of combining traditional arts-based inquiry with digital co-creation tools to democratize participation in health education. By situating AI as a collaborative visual interlocutor rather than a replacement for human creativity, the study reaffirms the FotoDialogo Method’s enduring goal: to bridge art, education, and social justice through dialogic visual inquiry.

Keywords:

Arts-Based Research (ABR), FotoDialogo Method, Artificial Intelligence (AI), Health Education, Participatory Action Research (PAR), Freirean Codifications, Multilingual and Multicultural Communities, Migrant Populations, Gender Equity and Social Inclusion (GESI).