Towards enhanced conversational dynamics for effective virtual therapist-assistive counseling

Abstract

Mental health issues are now the leading global cause of disability, with conditions such as anxiety and depression escalating, particularly following the COVID-19 pandemic. However, traditional mental health support methods remain heavily constrained by a severe shortage of trained professionals, making it difficult to meet the increasing demand for mental health support systems. In response, we propose Virtual Mental Health Assistants (VMHAs) as a scalable and accessible alternative, offering instant, anonymous, and stigma-free method of support. Despite their potential, existing automated counseling systems are limited by rigid, scripted dialogues and fail to replicate the nuanced adaptability and therapeutic depth of human therapists. To address this, we focus on dialogue systems on modular levels, spanning understanding, summarization, generation, and evaluation of therapeutic conversations. This thesis studies the counseling interaction pipeline, refining its core components to enhance the efficiency and effectiveness of professionals. The goal is to improve dialogue understanding, enabling VMHAs to interpret users’ implicit psychological intents through directive recognition. To maintain coherence and continuity across conversations, we incorporate domain knowledge infusion into counseling summarization, allowing the system to retain relevant memory. Additionally, we advance dialogue generation by integrating clinically informed, emotionally adaptive response models, surpassing traditional rule-based and purely generative approaches to ensure more human-like and therapeutic interactions. Next, we propose a dialogue evaluation framework that centers on therapeutic bond assessment via trust modeling. Recognizing that mental health support extends beyond one-on-one counseling, we further analyze peer interactions in online communities and group therapy, positioning AI as a facilitator of collective support environments. Through rigorous experimentation, user studies, and exhaustive analysis, this thesis establishes a carefully designed, context-aware, and psychologically informed understanding of counseling interactions. Rather than approaching VMHAs as standalone interventions, this research emphasizes their role as an augmentative approach, proposed to enhance the efficiency of mental health professionals. We conduct each phase of this study in close collaboration with domain experts, ensuring that the proposed methodologies are novel and practically viable in real-world settings. Moreover, the findings presented here extend beyond novel methodological contributions, positioning this thesis’ findings not just as novel solutions, but as supportive alternatives that alleviate the burden on mental health professionals.