Abstract:

This paper explores how Artificial Intelligence (AI) can transform Arts-Based and Participatory Research (ABR) by expanding accessibility, inclusion, and creativity in education. Building on the evolution of the FotoDialogo Method©, originally developed by the author in the 1990s, it examines how AI-generated imagery can function as a dialogic tool for reflection and meaning-making in multilingual and multicultural contexts. Rather than replacing human creativity, AI is reframed as a visual co-storyteller—a collaborative instrument that democratizes access to participatory visual inquiry.

Grounded in Paulo Freire’s Thematic Investigation Model and Henry Murray’s Thematic Apperception Test (TAT), the FotoDialogo Method integrates visual “codifications” and narrative dialogue to help participants examine their lived experiences and social realities. The method originated in community-based research with Latina women in Springfield, Massachusetts, where hand-drawn charcoal sketches inspired collective reflection, self-expression, and empowerment. These early studies confirmed the potential of visual storytelling to reveal hidden narratives and promote consciousness-raising in marginalized groups.

However, the method’s replication was limited by the researcher’s artistic skill. The current study investigates whether AI image-generation tools can extend FotoDialogo’s reach while preserving its interpretive and ethical integrity. The AI-enhanced FotoDialogo employs a four-stage process:
(1) narrative collection through interviews;
(2) crafting descriptive text prompts;
(3) AI image generation and cultural validation; and
(4) group dialogue and collective interpretation. This hybrid process merges digital literacy, visual semiotics, and participatory reflection, enabling researchers and educators without artistic training to apply ABR methods effectively.

Comparing hand-drawn and AI-generated images reveals both tensions and opportunities. While artist-made drawings convey empathy and contextual depth, AI imagery introduces accessibility and scalability, making participatory research more inclusive. Yet, ethical vigilance remains essential to prevent cultural distortion and ensure participant authorship and consent.

The paper situates this innovation within the theoretical frames of Freire’s dialogic pedagogy, Bruner’s meaning-making theory, and Eco’s semiotics, emphasizing that meaning arises through the interaction between image, viewer, and context. A pilot application with immigrant women’s groups demonstrated that AI-generated visuals—though sometimes perceived as “too perfect” or “too Western”—stimulated emotional engagement and intercultural dialogue.

Ultimately, the AI-supported FotoDialogo Method illustrates how technology can serve as a partner in participatory knowledge creation. By fusing artistic imagination with machine learning, this approach broadens the scope of visual inquiry in education and social research. It contributes to ongoing debates on equity, digital transformation, and inclusion, inviting educators and researchers to reimagine dialogue, authorship, and creativity in the age of AI.

Keywords:

Arts-Based Research (ABR), FotoDialogo Method, Artificial Intelligence, Visual Dialogue, Paulo Freire, Participatory Action Research (PAR), Photovoice, Meaning-Making, Digital Inclusion, Multilingual Education, Feminist Pedagogy.