Extended Inductive Thematic Analysis for the Digital Era: How Researchers Integrate AI and Traditional Digital Tools, and Why We Need a New Framework

This study has a dual purpose: first, to propose an extended inductive thematic analysis method for the digital era, incorporating generative AI while preserving human interpretive work centrality; and second, to examine empirically how researchers report, justify, and reflect on their use of traditional digital tools and generative AI in thematic analysis. The empirical component addresses the research question: Which methodological patterns, tensions, and paradoxes emerge in researchers’ reporting practices when integrating digital tools? Through extended thematic analysis of 56 peer-reviewed articles (2020-2025), three interconnected themes were identified: maintaining researcher accountability and proximity amid AI’s opacity, preserving creative interpretation while leveraging efficiency, and shifting from solitary toward collaborative analysis. These empirical findings corroborate and inform the proposed eight-stage methodology framework, which strategically positions AI in preliminary phases – data collection, familiarization, and category formation – while reserving interpretive synthesis, critical examination, and validation exclusively for human researchers. The framework integrates multiple qualitative methods (discourse, semiotic, textual analysis) and is theoretically grounded in distributed cognition, expertise as phronesis, and transparency principles, operationalizing a division of epistemic labor appropriate to human and computational capacities. This contribution advances the methodological discourse on AI-augmented qualitative research by providing a theoretically grounded and empirically informed framework for navigating AI integration while preserving the interpretive core of qualitative research.