Developing a Rhizomatic Participatory Triangulation Methodology for Qualitative Human-AI Storytelling Research

Copland, Samuel ORCID: https://orcid.org/0000-0002-7469-8320, Fiebig Lord, Sabina ORCID: https://orcid.org/0000-0003-2869-2574, El Masri, Omar ORCID: https://orcid.org/0000-0003-0554-590X and Jennings, Ros ORCID: https://orcid.org/0000-0002-5852-9420 (2025) Developing a Rhizomatic Participatory Triangulation Methodology for Qualitative Human-AI Storytelling Research. Qualitative Research. (Submitted)

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Abstract

As generative AI becomes more integrated into storytelling, it introduces new methodological questions for qualitative research. The potential for these technologies to amplify existing biases and reinforce dominant narratives highlights a need for approaches that can adequately address the complex, co-creative, and non-linear dynamics of human-AI interaction. This paper introduces Human-Machine Participatory Triangulation (HCPT), a novel methodological framework designed for this context. Drawing on Deleuze and Guattari’s concept of the rhizome, HCPT structures a tripartite dialogue between researchers, underrepresented community members, and Large Language Models (LLMs). It moves beyond triangulation as a tool for verification, reconceptualising it as a co-creative process that fosters multivocal, multimodal, and non-linear narrative exploration. By centring participation and dialogue, the HCPT framework offers a critical and practical approach for qualitative researchers to investigate and ethically navigate the emergent landscape of human-AI storytelling, challenging epistemological harms and fostering more equitable representation.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Human-Machine Participatory Triangulation; Rhizomatic Methodology; Human-AI Storytelling; Qualitative Methodology; Sociotechnical Asymmetries; Participatory Research; Human-AI Co-creation; Epistemological Harms; Multimodality; Multivocality
Related URLs:	https://uk.sagepub.com/en-gb/eur/journal...
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HT Communities. Classes. Races T Technology > T Technology (General)
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Depositing User:	Sabina Fiebig
Date Deposited:	13 Jan 2026 11:16
Last Modified:	13 Jan 2026 11:30
URI:	https://eprints.glos.ac.uk/id/eprint/15679

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