Can Virtual Agents Care? Designing an Empathetic and Personalized LLM-Driven Conversational Agent
For users seeking mental health support, this work addresses the lack of personalization and empathy in LLM-based conversational agents, though it is an incremental improvement over existing retrieval-augmented methods.
The paper introduces a virtual agent framework that uses retrieval-augmented generation, structured memory, and multimodal interaction to provide empathetic, personalized wellbeing support. In a cross-cultural study with university students, the system outperformed LLM-only baselines in coherence, perceived accuracy, and empathy, with most participants preferring the proposed approach.
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.