HCApr 7

SpeakSoftly: Scaffolding Nonviolent Communication in Intimate Relationships through LLM-Powered Just-In-Time Interventions

arXiv:2604.0538233.0h-index: 6
Predicted impact top 58% in HC · last 90 daysOriginality Incremental advance
AI Analysis

This addresses conflict resolution in intimate relationships through AI interventions, but it is incremental as it builds on existing NVC principles and LLM applications.

The authors tackled the problem of escalating conflicts in text-based communication among couples by developing SpeakSoftly, a system using LLM-powered just-in-time interventions based on Nonviolent Communication principles, which showed that the Empathetic Guide mode significantly facilitated behavioral and cognitive changes in user studies with 18 couples.

Conflicts are common in text-based communication, particularly in intimate relationships, where misunderstandings can easily escalate into verbal aggression. To address this, we present SpeakSoftly, a system that applies Nonviolent Communication (NVC) principles to scaffold couples' conflict communication through LLM-powered just-in-time interventions. Informed by formative interviews with couples and NVC principles, we designed two core features: NVC-Prompt, which detects verbal aggression and suggests revisions to prevent escalation, and NVC-Guide, which analyzes dialogues to uncover users' feelings and needs, fostering self-awareness and perspective-taking. These features were implemented across three progressive intervention modes, each varying in intervention depth and tone: Basic Reminder, Neutral Guide, and Empathetic Guide. We conducted a mixed-methods user study with 18 couples across simulated and real-life conflict settings to evaluate the effectiveness of each mode. Results showed that Empathetic Guide significantly facilitated both behavioral and cognitive changes, while Neutral Guide was effective only for behavioral changes in simulated conflicts. In real-life conflicts, Neutral Guide showed distinct advantages due to lower cognitive load demands. We discuss the mechanisms behind these findings and propose design implications for in-situ interventions in high-stakes communication contexts.

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