Comparative Analysis of AI-Driven Conversational Agents for Mental Health Support: Rule-Based, Transformer, and RAG Approaches

Abstract

The rapid integration of AI into mental health support systems has opened up new avenues for interventions that are scalable and accessible. In this work, we conduct a comparative evaluation of three major chatbot paradigms, namely, rule-based systems, Transformer-based models, and RAG architectures, for their performance in identifying conversational cues pertaining to depression and anxiety. Each model type is representative of a different approach toward language understanding, offering unique strengths in handling user intent, contextual reasoning, and emotional interpretation.

The proposed evaluation framework examines the models through a number of dimensions: linguistic coherence, emotional sensitivity, response accuracy, and quality of user engagement. We analyze real-world conversational data and simulated mental-health dialogues to highlight how each architecture reacts to complex psychological cues such as negative sentiment, distress patterns, and help-seeking behaviors. Additional assessment criteria include computational efficiency, interpretability, and safety constraints, all of which are crucial for real-world application in sensitive mental-health contexts.

The results demonstrate large differences in performance among the three types of chatbots. While rule-based systems are highly reliable but inflexible, Transformer-based models demonstrate improved contextual,understa nding, while RAG models offer the best balance between contextual relevance and factual grounding. These findings highlight the role that could be played by sophisticated AI-driven conversational agents in early mental health screening and support, while also pointingto the need for ethical, secure, clinically guided deployment in real-world settings.