DySRec: Dynamic Context-Aware Psychometric Scale Recommendation via Multi-Agent Collaboration

TL;DR

DySRec is an interactive multi-agent chatbot system designed to dynamically recommend psychometric scales during psychological assessments by integrating diverse user signals and maintaining context.

Contribution

It introduces a novel multi-agent conversational framework for dynamic psychometric scale recommendation with a closed-loop refinement mechanism.

Findings

System effectively models heterogeneous signals for scale recommendation.

DySRec supports dynamic assessment and risk management.

Prototype verified in real-world application.

Abstract

Choosing suitable psychometric scales is an essential and difficult step in psychological consultation, which requires clinicians to integrate patient information, behaviors, and dynamic contextual information. Existing systems mainly use static pipelines to choose scale, or directly predict symptoms according to user inputs, limiting their ability to support dynamic assessment, risk management, and transparent decision-making. To address these limitations, we propose DySRec, a multi-agent conversational system for dynamic psychometric scale recommendation. DySRec operates as an interactive chatbot that engages users in multi-turn dialogue, models scale selection as a continuous conversational decision process, and coordinates specialized agents to maintain user context, recommend assessment scales, monitor psychological risk, and log decision trajectories. In this way, DySRec can integrate and capture heterogeneous signals, including semantic, interaction behaviors, assessment history, and content state, to dynamically update user representations and calculate scale-context compatibility score for recommending most matched scales. Moreover, DySRec incorporates a closed-loop refinement mechanism. Recommendation agent will feedback the missing or uncertain attributes and guide the conversation to elicit the targeted information. In this paper, we showcase the prototype design and architecture of DySRec, and this system has been verified in a real-world application.