Two conversations can end with equally correct answers while requiring unequal user effort. One system may correct its own prior error. Another may succeed only after the user restores an instruction, referent, or evidential standard. Output-focused evaluation can assign these trajectories the same result and overlook who performed the work required for recovery. We propose a framework for measuring this difference through observable repair sequences in conversations between humans and large language models. Extending Pickering and Garrod’s Interactive Alignment Model, the framework combines four concurrent functional loops (Grounding, Continuity, Negotiation, and Epistemic) with a structural classification of who initiates and resolves repair and a temporal hypothesis about expectation stabilization. It generates testable predictions about system self-repair, epistemic signaling, and phase-dependent repair cost. In a pilot analysis of 30 dialogues, two human annotators achieved substantial agreement under an earlier turn-level rubric (Cohen’s κ = .75). The analysis revealed category-boundary problems that motivate the revised sequence-level framework presented here. Its evidential scope is limited to annotation feasibility. The resulting account makes repair burden measurable and specifies the evidence required for future validation.

Real-time communication mediated by artificial intelligence (AI) can produce an accurate output while changing how a speaker plans an utterance and what a listener receives. Output measures, such as word error rate, machine translation scores, and task accuracy, leave these process effects unobserved. We propose a dual-axis framework that aligns disfluency measures with suggestion correspondence, including lexical entrainment and semantic similarity. Disfluency type, rate, timing, and duration characterize observable production difficulty. Correspondence measures characterize whether suggestion content appears in the response. Observing both axes within the same episode distinguishes behavioral configurations that either family alone leaves ambiguous and supports explicit hypotheses about planning, uptake, and resistance. An illustrative corpus demonstrates event-detection feasibility and shows that semantic correspondence varies with coded incorporation and exceeds a comparison matched on presentation format. These checks leave cognitive interpretations open to experimental validation. We also derive a prediction concerning listeners: removing natural disfluencies may remove cues that listeners use to anticipate difficult or novel material. The resulting framework and reporting protocol extend standard evaluation to the human processes through which real-time AI-mediated communication succeeds or fails.