The Art of Socratic Inquiry: A Framework for Proactive Template-Guided Therapeutic Conversation Generation

2026-02-02

AI Summary

This work addresses the limitation of existing large language models in psychotherapy, which typically operate passively without actively guiding cognitive restructuring. The authors propose the Socratic Inquiry Framework (SIF), which for the first time decouples theory-driven Socratic questioning into a plug-and-play module. SIF employs strategy anchoring to determine optimal questioning时机, retrieves templates to generate question content, and integrates a lightweight intent-planning architecture—enabling proactive therapeutic guidance without requiring end-to-end retraining. A high-quality, strategy-aligned Socratic-QA dataset is also introduced. Experimental results demonstrate that SIF significantly increases the frequency of proactive questioning, enhances dialogue depth, and improves therapeutic alignment, thereby achieving a paradigm shift from passive empathy to active cognitive guidance.

Technology Category

• Natural Language Processing: Question Answering
• Machine Learning: Active Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web question answering
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

Abstract

Proactive questioning, where therapists deliberately initiate structured, cognition-guiding inquiries, is a cornerstone of cognitive behavioral therapy (CBT). Yet, current psychological large language models (LLMs) remain overwhelmingly reactive, defaulting to empathetic but superficial responses that fail to surface latent beliefs or guide behavioral change. To bridge this gap, we propose the \textbf{Socratic Inquiry Framework (SIF)}, a lightweight, plug-and-play therapeutic intent planner that transforms LLMs from passive listeners into active cognitive guides. SIF decouples \textbf{when to ask} (via Strategy Anchoring) from \textbf{what to ask} (via Template Retrieval), enabling context-aware, theory-grounded questioning without end-to-end retraining. Complementing SIF, we introduce \textbf{Socratic-QA}, a high-quality dataset of strategy-aligned Socratic sequences that provides explicit supervision for proactive reasoning. Experiments show that SIF significantly enhances proactive questioning frequency, conversational depth, and therapeutic alignment, marking a clear shift from reactive comfort to proactive exploration. Our work establishes a new paradigm for psychologically informed LLMs: not just to respond, but to guide.

Problem

• proactive questioning
• cognitive behavioral therapy
• large language models
• Socratic inquiry
• therapeutic conversation

Innovation

• Socratic Inquiry Framework
• proactive questioning
• template-guided generation
• therapeutic intent planning
• psychological LLMs