Psychopathology-AI-interaction (PAII) framework: Disorder-specific dynamics in conversational AI
Year: 2026
Type: preprint
Abstract: Chatbots based on large language models (LLM-CBs) are increasingly used for mental health support by both individuals with and without mental health conditions. Simultaneously, potential risks and harms of LLM-CB use for mental health are widely discussed, typically in disorder-unspecific terms, evaluated through single-turn benchmark and vignette designs, and rarely linked to established models of psychopathology. The current work presents the Psychopathology-AI-interaction (PAII) framework, which conceptualizes harmful human-chatbot interaction as arising from three interacting components: human factors (disorder-specific pathology, individual vulnerabilities, and personality dispositions), chatbot factors (model-specific safety failures and design principles), and interaction factors. Drawing on established models of technology-associated psychopathology, LLM-CBs are presented as a new domain in which disorder-specific maladaptive patterns emerge. A roadmap for future research is presented, focusing on psychopathology-based multi-turn benchmarks, experimental induction of vulnerabilities and manipulation of chatbot risk factors, and analysis of naturalistic conversation logs.
Source: PsyArXiv (OSF Preprints)
Language: English
Subfield: Applied Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7214802955