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.

Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker’s intentions and the listener’s perceptions in both cases.