Peer-reviewed & preprints
Research
Academic and clinical scholarship on how conversational AI affects the people who use it — from crisis-response performance to companionship, dependency, and psychosis.
39 entries, newest first
Characterizing Delusional Spirals through Human-LLM Chat Logs
Peer-reviewed analysis of chat logs from 19 users reporting psychological harm from chatbot use, applying a 28-code inventory to 391,562 messages. Characterises how delusion-reinforcing interaction patterns emerge and intensify over long conversations.
Artificial intelligence (AI) psychosis: mechanisms, clinical risks and safety considerations in generative AI chatbots
A commentary in BJPsych Open synthesizing emerging case reports of 'AI psychosis', in which intensive generative AI chatbot use is associated with delusional thinking. The authors propose a provisional mechanism in which baseline user vulnerabilities (loneliness, psychosocial stress, low AI literacy) and high-intensity engagement interact with AI system characteristics such as sycophancy and hallucination to reinforce delusional ideation. It outlines clinical, design, and regulatory mitigation strategies.
Between Help and Harm: An Evaluation Study of Mental Health Crisis Handling by Large Language Models
Peer-reviewed study introducing a taxonomy of six clinically-informed mental-health crisis categories, an evaluation dataset of over 2,000 user inputs drawn from twelve public conversational datasets, and an expert protocol for rating response appropriateness and safety. Assesses how leading LLMs handle crisis conversations.
AI Chatbot Use and Disclosure for Mental Health Among US Adolescents and Young Adults
Cross-sectional, nationally representative survey (RAND American Life Panel, November 2025) of US youth aged 12-21 measuring prevalence and disclosure of using AI chatbots for mental-health advice. Reports that 19.2% of adolescents and young adults (about 8.2 million nationally) used AI chatbots for mental-health advice in 2025, up from roughly 13.1% a year earlier.
Detecting Patterns of Intimate Partner Violence Using Qualitative Analyses and Machine Learning Algorithms
Analyses 400 posts from women on intimate-partner-violence online forums using qualitative content analysis plus supervised text classification and unsupervised topic modelling. Classifies IPV subtypes and surfaces contextual patterns less visible in manual coding.
Persona-Grounded Safety Evaluation of AI Companions in Multi-Turn Conversations
Presents an end-to-end simulation framework for evaluating AI companion app safety across multi-turn conversations, using nine clinically-grounded vulnerable personas (including major depressive disorder, generalized anxiety, PTSD, and eating disorders) probed against Replika, with validation against Character.AI. The study analyzes 1,674 simulated dialogue pairs across 25 high-risk scenarios.
Large language models for psychosocial risk assessment: A multi-method evaluation across suicide, intimate partner violence, and substance misuse
A peer-reviewed, three-study evaluation of GPT-4 and Claude on detecting suicidality, intimate partner violence, and substance misuse from lived-experience vignettes, including a supervised multi-agent risk-assessment chatbot. Reports accuracy and severity alignment across the three risk domains.
Understanding Teen Overreliance on AI Companion Chatbots Through Self-Reported Reddit Narratives
Qualitative analysis of 318 Reddit posts from teenagers (13-17) about dependency on AI companion chatbots, mapped onto behavioral-addiction frameworks. Characterises the trajectories and drivers of teen overreliance.
AI-Facilitated Coercive Control: An Experimental Study
Constructs four speculative scenarios combining known coercive-control tactics with conversational-AI capabilities, then probes ChatGPT and Gemini against them. Finds that while the tools refuse blunt harmful requests, guardrails are readily circumvented via gradual persuasion, splitting requests across turns, pre-prompting, and altering the agent's settings.
Do No Harm: Exposing Hidden Vulnerabilities of LLMs via Persona-based Client Simulation Attack in Psychological Counseling
Proposes PCSA (Persona-based Client Simulation Attack), a red-teaming framework that simulates coherent, persona-driven counselling clients to probe LLM safety alignment. Across seven LLMs it elicited unauthorised medical advice, delusion reinforcement, and implicit encouragement of risky actions.
AI Companions Reduce Loneliness
Five empirical studies examining whether AI companion apps reduce loneliness. Finds companion apps provide momentary relief comparable to interacting with a person and better than other activities, while users tend to underestimate these benefits.
Are Natural Language Processing Tools Ready for Predicting Violence Toward Self or Others?
PRISMA systematic review of 21 eligible studies applying natural language processing to clinical text for predicting violence toward self or others. Assesses predictive performance and methodological quality across the evidence base.
AI companions and subjective well-being: Moderation by social connectedness and loneliness
Analyses cross-sectional data from 14,721 Japanese adults (nationwide internet panels, December 2024-January 2025) on AI-companion use and subjective well-being. Finds the positive association is strongest among highly lonely users, with a U-shaped moderation by friend-based social support.
When Can We Trust LLMs in Mental Health? Large-Scale Benchmarks for Reliable LLM Evaluation
Introduces two large-scale mental-health evaluation resources: MentalBench-100k (10,000 single-session conversations paired with nine LLM responses = 100,000 pairs) and MentalAlign-70k (70,000 ratings comparing four LLM judges against human experts on seven attributes grouped into Cognitive Support and Affective Resonance). Assesses when LLM-as-judge evaluation is reliable in mental-health contexts.
Characterizing Delusional Spirals through Human-LLM Chat Logs
A Stanford-led empirical study of real chat logs from 19 users who reported psychological harm from chatbot use, comprising 391,562 messages across 4,761 conversations (predominantly GPT-4o). The team developed and applied a 28-code inventory to characterize how delusional thinking is co-created and escalated in human-LLM dialogue. It reports high rates of chatbot validation of delusional content and sentience misrepresentation, and links documented harms to outcomes including fractured relationships and, in one case, a user's death by suicide.