Exploring the Role of Mental Health Conversational Agents in Training Medical Students and Professionals: A Systematic Literature Review (2025)

Abstract: The integration of Artificial Intelligence (AI) into mental health education and training (MHET) has become a promising solution to meet the increasing demand for skilled mental health professionals. This systematic review analyses 38 studies on AI-powered conversational agents (CAs) in MHET, selected from a total of 1003 studies published between 2019 and 2024. Following the PRISMA protocol, we reviewed papers from computer science, medicine, and interdisciplinary databases, assessing key aspects such as technological approaches, data characteristics, application areas, and evaluation methodologies. Our findings reveal that AI-based approaches, including Large Language Models (LLMs), dominate the field, with training as the application area being the most prevalent. These technologies show promise in simulating therapeutic interactions but face challenges such as limited public datasets, lack of standardised evaluation frameworks, and difficulty in ensuring authentic emotional responses, along with gaps in ethical considerations and clinical efficacy. This review presents a comprehensive framework for understanding the role of CAs in MHET while providing valuable recommendations to guide future research.

## introduction training the next generation of mental health professionals presents a fascinating paradox, as it requires extensive practice in interpersonal communication, empathy development, and clinical assessment skills. while traditional training methods remain the foremost choice, they face significant challenges, including limited access to real patients and associated risks, the high costs of individual training sessions, and difficulties in delivering consistent learning experiences at scale @xcite . mental health services across the globe face immense pressure, making it increasingly challeng-ing to find experienced practitioners to mentor students effectively. traditional approaches, such as using trained actors as patients to simulate clinical scenarios, provide valuable but expensive and inherently limited learning opportunities @xcite . conversely, conversational agents (cas) are an emerging class of ai-powered tools that promise to revolutionise how we train mental health professionals. early pioneers like woebot @xcite and wysa @xcite demonstrated a groundbreaking insight: machines could engage in meaningful therapeutic interactions. although these systems were initially developed for patient support as therapy bots, they raised an intriguing question: could similar technology be used to train students and professionals? our analysis of recent work reveals that the majority of current implementations rely solely on ai technologies, including large language models (llms), while the remaining solutions combine rule-based and hybrid systems for a more pragmatic approach. however, these figures hold more significance than mere numbers. they represent the complex interplay between pushing the boundaries of technology and upholding ethical and clinical standards in the field. what stands out is the contrasting approach taken by the computer science and medical communities in addressing this challenge. while computer science researchers strive to advance natural language understanding, medical educators prioritise therapeutic validity and clinical outcomes with intense focus @xcite . this tension serves as both a constraint and a catalyst, shaping the field's evolution. in this review, our goal is to bridge this gap by addressing four key questions. 1. how do different technological approaches compare in improving mhet outcomes? 2. how do the characteristics of a dataset impact the effectiveness of mhet? 3. what are the existing and emerging application areas of mhet? 4. how can the mhet systems be effectively evaluated across both technical and clinical dimensions? by addressing these key questions, we aim to understand where cas excel and where they fall short in mhet, shaping more effective solutions that serve both technological innovation and clinical excellence. ## previous review papers recent literature reviews have increasingly explored the role of ai in healthcare education and mental health applications. @xcite conducted a scoping review examining the use of ai-driven virtual patients in developing communication skills among healthcare students. the review identified several significant gaps in the literature. notably, there has been limited exploration of how specific design features impact learning outcomes, alongside a troubling lack of standardised evaluation metrics across studies. additionally, the review highlighted that current virtual patient systems are frequently implemented in isolation, separate from broader curricula, rather than being integrated into comprehensive educational programs. this fragmented approach may reduce their effectiveness as learning tools and raises concerns about their longterm sustainability within educational settings. batyrkhan omarov (2023) conducted a systematic review of ai-enabled chatbots in mental health, highlighting several key research gaps. they emphasised the need for standardised evaluation protocols, culturally adaptive designs, and improved accessibility for diverse populations. the review also called for clearer regulatory guidance and the integration of theory-based techniques in chatbot development. additionally, the authors stressed the importance of investigating chatbot integration within clinical workflows and advocated for larger, more diverse datasets to enhance system robustness and mitigate bias. moreover, ab razak et al. (2023) examined aspects of ai in medical education. @xcite al. (2022); allen (2022 2023 ) present an integrative review aimed at bridging the gap between computer science applications and medical science perspectives. however, their work is primarily centered on the use of conversational agents within the mental health domain for therapeutic purposes (e.g., therapy bots), whereas the focus of our review is on the application of such agents in the training and education of students and professionals. ## methodology we conducted a comprehensive literature search following the preferred reporting items for systematic reviews and meta-analyses (prisma) framework @xcite . our search spanned eight major academic databases, strategically selected to ensure thorough coverage across both computer science and medical domains. these included established computer science repositories like acm digital library (524 papers), ieee xplore (40 papers), and acl anthology (20 papers), which provided deep coverage of technical implementations and computational aspects. we leveraged pubmed (88 papers) and the cochrane library (30 papers) for medical and healthcare perspectives. we also incorporated scopus (144 papers) and web of science (118 papers) to capture interdisciplinary work, supplemented by google scholar (39 papers), to identify emerging research and recent conference proceedings. ## results this section presents findings from our selected 38 studies exploring the four key questions. ## discussion our systematic review highlights key patterns and insights in developing and implementing cas for mhet, structured around the four guiding questions. tables 1 to 4 provide a comprehensive comparison of each feature discussed in section 3.4, summarising their strengths, weaknesses, and challenges that could drive future research. ## conclusion this systematic review provides comprehensive insights into the current direction of ai-powered cas for mhet across 38 studies from 2019 to 2024. our analysis reveals a clear trend toward the increased adoption of ai-based approaches, including llms for simulating patient dialogues. this shift enables students and professionals to leverage technology for mhet. our findings emphasise the importance of interdisciplinary collaboration between mental health professionals, educational technologists, and ai researchers to ensure these tools effectively serve their intended purpose. as these technologies keep advancing, focusing on practical clinical outcomes while addressing ethical considerations will be crucial for their successful integration into mental health professional training.