Aug 2026· Frontiers in Humanities and Social Sciences· 0 citations· 11 references
TL;DR
To maximise the health benefits for the public, AI should be used to construct an auxiliary triage system that supports human clinicians and enables appropriate referrals when needed, and presents a dual-edged nature of AI.
Abstract
Artificial Intelligence (AI) is being used in the mental health field to develop generative AI chatbots and digital phenotyping; thus, the traditional psychological model has been challenged. Examine all kinds of changes in people's help-seeking behaviour and awareness of mental health due to AI applications. Although there are many mental illnesses around the world, most people are still unaware of them due to social stigma, high costs and a lack of health education. AI-led interventions provide users with an easy-to-access and private space for conversation with artificial intelligence to offer new kinds of mental health support. How interaction with AI affects the cognition of psychological problem recognition. Explainable AI (XAI) can help boost people's mental health literacy and provide a foundation for seeking professional mental health services, but there is also a risk of developing emotional dependence on Generative AI technology. Based on the latest empirical evidence for user self-disclosure, anticipated stigma and the construction of a 'digital therapeutic alliance', this study presents a dual-edged nature of AI. To maximise the health benefits for the public, AI should be used to construct an auxiliary triage system that supports human clinicians and enables appropriate referrals when needed.
A list of eight best practices was created to assist developers with designing AI systems in a way that would reduce the overall risk of harm for users attempting to use their AI for mental health cases.
Joshua Frankenfield, Briana M. Sobel, Barbara Chaparro· Proceedings of the Internati...· 0 citations
Artificial Intelligence (AI) has become a disruptive technology across industries such as healthcare, education, and finance. In psychology and mental health services, AI-assisted counselling systems are emerging to address the growing number of patients and the shortage of certified practitioners. These systems use technologies like machine learning, natural language processing (NLP), sentiment analysis, and predictive analytics to support mental health professionals and provide scalable psychological care. This study examines AI-assisted counselling as an innovative approach in psychology, focusing on its technological framework, benefits, ethical concerns, and user adoption. The proposed system integrates psychological assessment tools, conversational AI, and feedback-based learning mechanisms. System performance is evaluated using metrics such as emotional recognition accuracy, user engagement, and treatment outcome improvements through a mixed-method approach combining experimental analysis and user perception studies. The findings indicate that AI-powered counselling can improve access to mental health support, enable early detection of emotional distress, and enhance therapeutic services. However, challenges related to data privacy, ethical considerations, emotional authenticity, and clinical reliability remain important areas for further research. The study concludes that AI should complement rather than replace human therapists, supporting a collaborative human–AI approach to improve mental healthcare accessibility and effectiveness.
Chen Wei, Liu Fang· International Journal of Eme...· 0 citations
The fast-rising popularity of social media apps and artificial intelligence (AI) related tools has changed how people express emotion, get validation and take care of their mental health by using tools available in the digital world. The study explores how the use of social media, the expression of emotion, and the increasing reliance on AI natural language processing (NLP) to identify and respond to users' emotional and mental states are all connected. Mixed methods were used in the current study to analyze both qualitative data (from users aged less than 18 to 45) and quantitative data. The results show that while AI tools offer non-evaluative, at times questionably false empathetic outputs, they are available 24 hours a day and provide a short-term solution to emotional distress. There are serious ethical implications associated with using them, such as issues related to privacy, threats to users' well-being, emotional dependency on them, and the postponement of much-needed human or professional mental health services. A need for responsible AI design, privacy-protecting technologies, and a balance between AI and human-delivered mental health care being the need of the hour.
Ashmita Parida, Rajni Nair· International Journal For Mu...· 0 citations
Cognitive and mental health (CMH) disorders are increasingly prevalent worldwide and pose significant societal, clinical, and economic challenges. While conventional mental health support methods remain limited by scalability and reactivity, recent advances in artificial intelligence have opened new opportunities for scalable, proactive, and personalized mental health support. Rapid progress has been made in areas such as mental health assessment, empathetic conversational agents, and AI-assisted psychological interventions; however, these efforts remain fragmented across disciplines, and critical challenges related to reliability, interpretability, ethics, and real-world deployment persist. To address these gaps, we propose the International Workshop on AI for Cognitive and Mental Health Support (AI4Mental), a half-day interdisciplinary forum that brings together researchers and practitioners from data mining, machine learning, NLP, HCI, healthcare, and social sciences. The workshop focuses on three complementary pillars: AI as Assessment, AI as Emotional Support, and AI as Psychological Intervention, covering topics ranging from multimodal mental health detection and longitudinal risk modeling to empathetic dialogue systems and responsible interventions. By consolidating emerging research and fostering cross-disciplinary dialogue, AI4Mental aims to advance trustworthy, effective, and socially responsible AI solutions for cognitive and mental health support, aligning closely with SIGKDD's mission on data science for social good.
Xiangmeng Wang, Haoyang Li, Chen Li et al.· Proceedings of the 32nd ACM...· 0 citations
These technologies show promise in reducing human error and enhancing mental health care delivery; however, persistent challenges include data privacy, ethical considerations, and the need for diverse, large-scale datasets.
Juster Donal Sinaga· Journal of Society Counselin...· 0 citations