Structuring Human-AI Productive Interdependence by Strategic Level of Automation Selection for Qualitative Inquiry

2026-05-26

AI Summary

This study addresses the risk that overreliance on artificial intelligence automation in qualitative research may undermine the depth of meaning-making. Drawing on interdependence theory, the authors propose a novel “productive interdependence” paradigm and develop a framework integrating levels of automation (LoA), task risk, and validation cost to guide human–AI collaboration across analytical stages. The framework establishes three design principles to ensure the central role of human researchers in interpretive sensemaking. Case studies demonstrate that this approach effectively calibrates trust between humans and AI, enhancing analytical efficiency while preserving the rigor and interpretive depth essential to qualitative inquiry.

Abstract

While Large Language Models (LLMs) offer a solution to the scale-versus-depth dilemma in qualitative analysis, the paradigm of maximizing automation is fundamentally 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 reframes the design of "co-data" systems through the lens of Interdependence Theory, proposing a formal framework to structure human-AI productive interdependence. The framework guides the selection of an appropriate Level of Automation (LoA) for different stages of the qualitative analysis process by assessing task risk and the cost of validation. We present a case study where this framework led to a deliberately interdependent workflow, fostering the calibrated trust necessary for rigorous analysis. We conclude by presenting three design principles that instantiate this framework, demonstrating how to leverage AI as a powerful partner while preserving the human researcher's irreplaceable role in the transformation process of meaning-making.

Problem

• Human-AI collaboration
• qualitative inquiry
• Level of Automation
• productive interdependence
• interpretive analysis

Innovation

• Human-AI collaboration
• Level of Automation
• Interdependence Theory
• qualitative inquiry
• co-data systems