Meaning-Making at Scale: Structuring Productive Human-AI Interdependence in Qualitative Inquiry

While Large Language Models (LLMs) offer a solution to the scale-versus-depth dilemma in qualitative analysis, the paradigm of maximizing automation is at odds with the interpretive nature of qualitative inquiry. We argue that effective Human-AI collaboration is not an automation problem, but an interdependence problem. This paper proposes a formal framework to structure human-AI interdependence to resolve the dilemma between broad scale and deep analysis by dynamically selecting an appropriate Level of Automation (LoA) for each analytical phase. Grounded in Interdependence Theory and an industry case study, we present three core design principles: 1) capability asymmetry, which defines roles based on human and AI strengths; 2) dynamic calibration, which adaptively structures workflows by weighing total risk (task consequence and error likelihood) against validation cost; and 3) bi-directional validation, which maintains analytic integrity through mutual auditing loops. By grounding AI insights in verified quotes, these principles demonstrate how to leverage AI as a powerful partner while preserving the researcher’s irreplaceable role in the interpretive process of meaning-making.