Can Virtual Agents Care? Designing an Empathetic and Personalized LLM-Driven Conversational Agent

2026-04-22

AI Summary

Traditional mental health services are often scarce and costly, while existing large language model (LLM)-driven conversational systems commonly suffer from insufficient empathy, lack of personalization, and low factual reliability. To address these limitations, this work proposes a lightweight virtual conversational agent framework that integrates retrieval-augmented generation (RAG), structured user memory, and multimodal interaction to enable high-quality, cross-culturally personalized empathetic dialogue—even on smaller-scale models. Experimental results demonstrate significant improvements in retrieval accuracy and response quality on objective metrics. Furthermore, user studies confirm that the system substantially outperforms pure LLM baselines in coherence, factual accuracy, and perceived empathy, with a clear majority of participants expressing a strong preference for the proposed approach.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Conversational AI/Dialog Systems

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Mental health challenges are rising globally, while traditional support services face limited availability and high costs. Large language models offer potential for conversational support, but often lack personalization, empathy, and factual grounding. A virtual agent framework is introduced to provide empathetic, personalized, and reliable wellbeing support through retrieval-augmented architecture, structured memory, and multimodal interaction. Objective benchmarks demonstrate improved retrieval and response quality, particularly for smaller models. A cross-cultural study with university students from Vietnam and Australia shows the system outperforms LLM-only baselines in coherence, perceived accuracy, and empathy, with most participants clearly preferring the proposed approach.

Problem

• empathy
• personalization
• mental health support
• conversational agent
• factual grounding

Innovation

• retrieval-augmented generation
• structured memory
• empathetic AI
• personalized conversational agent
• multimodal interaction