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

TL;DR

SpeakSoftly is a system that uses large language models to provide real-time, tailored interventions based on Nonviolent Communication principles, aiming to improve conflict resolution in intimate relationships.

Contribution

This paper introduces a novel LLM-powered intervention system with multiple modes, specifically designed to scaffold nonviolent communication in couples' conflicts.

Findings

Empathetic Guide significantly improved behavioral and cognitive changes.

Neutral Guide was effective for behavioral changes in simulated conflicts.

Neutral Guide showed advantages in real-life conflicts due to lower cognitive load.

Abstract

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.