Methodological Foundation for Research on Human Cognitive Contribution in AI-Assisted Work

Abstract

Research on human-AI interaction increasingly relies on naturalistic conversation data, interaction logs and process measures to draw inferences about human cognitive contribution, reliance and capability. This working paper sets out a methodological foundation for such research. It separates five analytic levels: observable interaction behavior, inferred cognitive processes, human capabilities, capability formation over time, and educational interventions. It states that evidence at one level cannot stand as direct evidence at a higher level, and that the absence of an observable cognitive contribution in a conversation is not evidence that no cognitive work took place. It distinguishes human cognitive contribution from AI offloading, proposes the contrast between structural initiation and post-hoc adaptation as a research hypothesis, and describes validation, calibration, novelty and anti-confirmation safeguards. The paper closes with a twelve-step standing protocol for proposing new measures, facets or classifications. Its claims are methodological, and its contribution is a set of explicit constraints on inference.