Rehearsal: Simulating Conflict to Teach Conflict Resolution

TL;DR

This work introduces Rehearsal, a language-model–driven system for practicing conflict resolution through simulated dialogues that are grounded in the IRP (Interests, Rights, Power) framework. A novel IRP Prompting pipeline constrains LLM generations, enabling counterfactual explorations and targeted feedback to teach cooperative strategies. Technical evaluation shows strong classification and scoring performance (e.g., average IRP accuracy around $82\%$) and that the full IRP pipeline outperforms ablations, while a $N=40$ participant study demonstrates substantial shifts toward cooperative strategies and away from competitive ones in unaided live conflicts. The results highlight the potential of theory-grounded AI tutors for teaching complex social skills, while also acknowledging limitations in transfer, diversity, and ethical considerations for real-world deployment.

Abstract

Interpersonal conflict is an uncomfortable but unavoidable fact of life. Navigating conflict successfully is a skill -- one that can be learned through deliberate practice -- but few have access to effective training or feedback. To expand this access, we introduce Rehearsal, a system that allows users to rehearse conflicts with a believable simulated interlocutor, explore counterfactual "what if?" scenarios to identify alternative conversational paths, and learn through feedback on how and when to apply specific conflict strategies. Users can utilize Rehearsal to practice handling a variety of predefined conflict scenarios, from office disputes to relationship issues, or they can choose to create their own setting. To enable Rehearsal, we develop IRP prompting, a method of conditioning output of a large language model on the influential Interest-Rights-Power (IRP) theory from conflict resolution. Rehearsal uses IRP to generate utterances grounded in conflict resolution theory, guiding users towards counterfactual conflict resolution strategies that help de-escalate difficult conversations. In a between-subjects evaluation, 40 participants engaged in an actual conflict with a confederate after training. Compared to a control group with lecture material covering the same IRP theory, participants with simulated training from Rehearsal significantly improved their performance in the unaided conflict: they reduced their use of escalating competitive strategies by an average of 67%, while doubling their use of cooperative strategies. Overall, Rehearsal highlights the potential effectiveness of language models as tools for learning and practicing interpersonal skills.

Figures (11)

• Figure 1: An example interaction trace with Rehearsal, where an employee practices conflict with a simulated customer. The employee quickly realizes that Rights and Power-based strategies result in heightened conflict. The conflict is eventually resolved using an Interests-oriented approach.
• Figure 2: Rehearsal's UI.Rehearsal's interaction covers two broad goals: providing users with in-context feedback (left) on a conflict simulation, and allowing users to directly interact with the simulation (right) itself. Users can specify a conflict premise or select from presets under the "Select Scenario" input (top right). In this figure, the user engaged in conversation with the simulation ("I'm sick and tired...") and was presented in the feedback bar with three alternative options of messages to use instead. Before deciding to use an alternative, they press the fast forward button on the first one which projects the conversation one turn in the future (final message in the simulation panel).
• Figure 3: Recalling and Recognizing Conflict Resolution Strategies with Rehearsal. Before revealing the simulation's strategy, Rehearsal's Feedback interaction prompts users to recall and recognize conflict resolution strategies from IRP. To prevent frequent context switching, users are only asked to recall and recognize for the simulated interlocutor---not for their messages. The figure above outlines how users might practice this interaction (progresses from left to right).
• Figure 4: The IRP planning component supports three different modes: it classifies a user's response, generates counterfactual user messages using a pre-planned conflict resolution strategy (e.g. Interests), or plans and generates a simulated response.
• Figure 5: The Full IRP Prompting Pipeline. Our conditional generation process is split into contextualization, counterfactual input generation, and response generation. To contextualize simulated conflict, we first encode a pre-defined conflict premise (e.g. arguing about job performance). If a conversation has at least one turn, we additionally include history in the contextualization step. To generate counterfactuals, we pre-plan messages across the IRP framework, allowing users to compare different strategies. A corresponding response for each strategy is planned, generated, and scored by its effectiveness.