Look What AI Made Me Do: AI-Assisted Qualitative Inquiry for Deeper Insights at Scale

Qualitative research excels at revealing the nuance of human behavior, yet the volume, multilingual scope, and cultural diversity of contemporary datasets increasingly strain its hallmark depth. Drawing on generative artificial intelligence (genAI) capabilities, this paper proposes a hybrid, human‑in‑the‑loop framework that reconciles scale with interpretive rigor. We first establish a manually coded “ground‑truth” subset to anchor subsequent analysis, then employ an AI platform (myRA) to extend deductive and inductive coding across large corpora. Iterative cycles of human validation refine AI‑generated themes, ensuring contextual accuracy and theoretical coherence. We address ethical imperatives, data anonymization, transparent reporting of AI use, and model bias mitigation. We also advocate for privacy‑preserving deployments and locally hosted models where feasible. Looking ahead, we identify four trajectories for AI‑enhanced qualitative inquiry: multimodal analysis that fuses text, audio, and video; multilingual comparison at scale; AI‑driven theory building; and the co‑design of ethically grounded AI systems. Ultimately, we argue that the future of qualitative research lies not in automating interpretation but in strategically augmenting human expertise with AI’s speed and pattern recognition power, thereby achieving deeper insights without sacrificing methodological integrity.