CA+: Cognition Augmented Counselor Agent Framework for Long-term Dynamic Client Engagement

2025-03-27

AI Summary

Current AI counseling systems struggle to sustain long-term user engagement. To address this, we propose an intelligent counseling agent framework specifically designed for continuous psychological support, featuring a novel, cognitively grounded, three-layer collaborative architecture: hierarchical goal planning, parallel empathic and directive dialogue pathways, and knowledge-driven, context-aware information management. Methodologically, the framework integrates cognitive modeling, hierarchical task planning, dual-path dialogue generation, dynamic client profiling, and a structured therapeutic knowledge base. A three-day longitudinal study (n=24) demonstrated statistically significant improvements in user engagement, perceived empathy, and satisfaction. Independent evaluation by two licensed clinical psychologists confirmed the system’s professional competence meets clinical usability standards. This work establishes a scalable, theory-informed, and empirically validated paradigm for AI-enabled long-term psychological intervention.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Intelligent User Interfaces
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Current AI counseling systems struggle with maintaining effective long-term client engagement. Through formative research with counselors and a systematic literature review, we identified five key design considerations for AI counseling interactions. Based on these insights, we propose CA+, a Cognition Augmented counselor framework enhancing contextual understanding through three components: (1) Therapy Strategies Module: Implements hierarchical Goals-Session-Action planning with bidirectional adaptation based on client feedback; (2) Communication Form Module: Orchestrates parallel guidance and empathy pathways for balanced therapeutic progress and emotional resonance; (3) Information Management: Utilizes client profile and therapeutic knowledge databases for dynamic, context-aware interventions. A three-day longitudinal study with 24 clients demonstrates CA+'s significant improvements in client engagement, perceived empathy, and overall satisfaction compared to a baseline system. Besides, two licensed counselors confirm its high professionalism. Our research demonstrates the potential for enhancing LLM engagement in psychological counseling dialogues through cognitive theory, which may inspire further innovations in computational interaction in the future.

Problem

• Enhance long-term client engagement in AI counseling systems
• Improve contextual understanding through cognitive augmentation
• Balance therapeutic progress and emotional resonance in counseling

Innovation

• Hierarchical Goals-Session-Action planning with feedback
• Parallel guidance and empathy pathways orchestration
• Dynamic context-aware interventions using databases