Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage, there is a scarcity of comprehensive evaluations of their impact on mental health and well-being. This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health and factors influencing their effectiveness and user experience.
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Han Li, Renwen Zhang, Yi-Chieh Lee, Robert E. Kraut, David C., Mohr. Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being. npj Digit. Med. 6, 236 (2023). https://rdcu.be/dudOi
AI chatbots are permeating the socio-emotional realms of human life, presenting both benefits and challenges to interpersonal dynamics and well-being. Through computational analysis of a multimodal dataset with over 35,000 screenshots and posts from r/replika, we identified seven prevalent types of human-AI social interactions: intimate behavior, mundane interaction, self-disclosure, play and fantasy, customization, transgression, and communication breakdown, and examined their associations with six basic human emotions. Our findings suggest the paradox of emotional connection with AI, indicated by the bittersweet emotion in intimate encounters with AI chatbots, and the elevated fear in uncanny valley moments when AI exhibits semblances of mind in deep self-disclosure. Customization characterizes the distinctiveness of AI companionship, positively elevating user experiences, whereas transgression and communication breakdown elicit fear or sadness.
Han Li, Renwen Zhang. Finding Love in Algorithms: Deciphering the Emotional Contexts of Close Encounters with AI Chatbots. Journal of Computer-Mediated Communication (In-print). https://osf.io/preprints/osf/xd4k7
Across online support communities, community cohesion varies by platform and can impact the self-disclosure of members and their exchanges of social support. Through a comparative study of forum and chat-based mental health communities, this research examines how technical features of both platforms influence community cohesion by affecting communication patterns among members, including evenness of communication, speed of communication, and number of participants.
Han Li, Robert E. Kraut, Haiyi Zhu, Technical Features of Asynchronous and Synchronous Community Platforms and their Effects on Community Cohesion: A Comparative Study of Forum-based and Chat-based Online Mental Health Communities, Journal of Computer-Mediated Communication, Volume 26, Issue 6, November 2021, Pages 403–421, https://doi.org/10.1093/jcmc/zmab016
Although research shows that the Covid-19 pandemic has led to declines in mental health, the existing research has not identified the pathways through which this decline happens. The current study identifies the distinct pathways through which COVID-induced stressors (i.e., social distancing, disease risk, and financial stressors) trigger mental distress and examines the causal impact of these stressors on mental distress. This research provides evidence consistent with the thesis that the COVID-19 pandemic harmed the mental well-being of adults in the United States and identifies specific stressors associated with the pandemic that are responsible for increasing mental distress.
Robert E. Kraut, Han Li, Haiyi Zhu (2022). Mental health during the COVID-19 pandemic: Impacts of disease, social isolation, and financial stressors. PloS one, 17(11), e0277562.
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