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Resumo

Artificial intelligence (AI) is transforming qualitative research by streamlining data management and analysis. However, its application raises methodological, ethical, and cultural considerations, especially in large-scale, multilingual studies. We outline the step-by-step integration of AI into our qualitative data analysis of two projects, QualiECAC4 and BUMPER, guided by Bengtsson's stage content analysis framework. The first project involved 141 individual interviews conducted across nine EU Member States (MS), while the second involved 73 participants (eight individual interviews and twelve focus groups) across seven EU MS. In both projects, AI tools (ATLAS.ti for coding; DeepL Pro for translation) facilitated transcription, translation, initial coding, and theme identification, with all outputs subjected to systematic human review at each analytical phase. Integrating AI into our workflow accelerated data processing and highlighted consistent coding patterns across diverse multilingual datasets. At each phase of the content analysis framework, we pinpointed concrete benefits and tackled challenges, such as overlapping codes, nuanced interpretations, and cultural subtleties, through a structured human-in-the-loop process that combined open and intentional coding. While AI significantly enhances speed and depth, it still requires active human oversight to maintain methodological rigour and preserve contextual accuracy. Therefore, rather than reporting thematic findings, we draw on our team's practical experiences to provide clear, actionable recommendations for integrating AI into qualitative research, suggesting specific updates to reporting standards (COREQ) to ensure transparency, accountability, and ethical practice.

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