Why AI is Not the Enemy: Opportunities to Strengthen Core Commitments of Qualitative Inquiry Through Trustworthy AI-in-the-Loop Analysis

This article reframes the potential of generative AI in qualitative analysis, shifting from a focus on efficiency and automation toward opportunities to deepen analysis and strengthen core commitments of qualitative inquiry. We introduce “AI-in-the-loop analysis” as a term to describe the intentional incorporation of computational capabilities into analytic processes that remain grounded in human sensemaking, interpretation, and reflexive judgment. Building from foundational commitments of qualitative inquiry such as sustained attention to fine-grained data in relation to its larger context and intentional engagement of positionalities to support noticing and interpretation, we examine how properties of large language models (LLMs) can be mobilized to extend these practices. We focus on affordances provided by AI’s large-scale pre-training, rich semantic representations, attention mechanisms, long-context capacities, and interactive prompting, and describe ways that thoughtful engagement with these capabilities can help researchers maintain close attention to the details of the data across multiple iterations while situating interpretations in context, expand interpretive perspectives in dialogue with each other to layer meaning, and surface both confirming and disconfirming evidence across complex datasets. We connect these possibilities to established criteria for trustworthiness such as credibility, dependability, confirmability, transferability, and authenticity, showing how AI-in-the-loop approaches can offer new mechanisms for achieving and demonstrating analytic rigor. Rather than replacing human interpretive labor, generative AI can be used to augment researchers’ capacity for noticing, questioning, and synthesizing across large and complex qualitative data sets. When used critically and transparently, AI-in-the-loop analysis offers the possibility to expand the methodological repertoire of qualitative researchers for supporting rigorous, trustworthy, reflexive, contextually grounded analyses.