Weaving Meaning in the Digital Age: A Meta-Meta-Ethnography Enhanced by AI

Abstract

This study explores the evolving landscape of ethnographic synthesis in the digital age. It combines a pilot meta-meta-ethnographic study with an AI-augmented analysis using ChatGPT. Drawing on 14 published meta-ethnographies in education, the pilot study applies Noblit & Hare's, (1988) framework alongside post-qualitative theories of Agential Realism, Sociomateriality and Rhizomatic Thinking. The aim was to investigate methodological convergence and divergence across 14 studies. In parallel, a second analysis was conducted using ChatGPT to examine the feasibility and credibility of large language models (LLMs) in supporting thematic synthesis. This AI-assisted analysis generated open codes, clustered themes and applied theoretical lenses to offer a triangulated perspective. This was applied to validate and expand the pilot findings. Together, the two approaches reveal the complementary strengths of human-led and AI-supported meta-synthesis: the former provides depth and theoretical nuance, while the latter enhances efficiency, scalability and pattern detection. This combined study affirms the value of methodological pluralism and critically reflects on the implications of integrating AI into interpretive qualitative research.