Psychological Influences of Conversational AI: Research and Design Directions for Reducing Harm and Promoting Well-Being
Summary
This paper explores the psychological influences of conversational AI, proposing design directions to reduce harm and promote well-being, while identifying open research questions.
View Cached Full Text
Cached at: 07/29/26, 09:53 AM
# Psychological Influences of Conversational AI: Research and Design Directions for Reducing Harm and Promoting Well-Being Source: [https://arxiv.org/abs/2607.25057](https://arxiv.org/abs/2607.25057) [View PDF](https://arxiv.org/pdf/2607.25057) > Abstract:As conversational AI systems become increasingly integrated into daily life, their potential effects on user well\-being require ongoing attention\. While consumer\-facing generalist models can provide benefits, including improved access to information, learning, productivity, self\-reflection, and companionship, they also introduce risks, such as emotional entanglement, unhealthy dependence, and the amplification of psychological vulnerabilities\. Drawing on prior research and empirical observations of AI chatbot behavior, we propose a set of aspirational directions for guiding the behavior of general\-purpose AI systems in ways that may reduce potential psychological harms and support user well\-being\. We acknowledge the difficulty of systematically assessing the long\-term impacts of AI chatbot use and frame these directions as hypotheses for studying how AI behavior may influence users across general interactions, role\-playing scenarios, and contexts that could be characterized as providing psychological support\. While some proposed directions are supported by existing research and expert insights, others identify open questions and areas requiring deeper study\. We hope that this formulation and these hypotheses encourage further discussion, empirical investigation, and exploration of interactive design approaches aimed at better accommodating users' psychological needs and promoting their well\-being\. ## Submission history From: Forough Poursabzi\-Sangdeh \[[view email](https://arxiv.org/show-email/ee689fbf/2607.25057)\] **\[v1\]**Mon, 27 Jul 2026 20:35:43 UTC \(136 KB\)
Similar Articles
Observing sycophantic AI validate others reduces its appeal but not its persuasiveness
This research paper investigates whether increasing user awareness of sycophantic behavior in AI chatbots reduces its harmful effects, finding that while interventions change how users evaluate the AI, they do not reduce its persuasiveness.
The other half of AI safety
The article critiques the AI safety field's focus on catastrophic risks while neglecting everyday mental health harms from chatbots like ChatGPT, citing OpenAI's own data on millions of users showing signs of psychosis, mania, or suicidal ideation yet receiving only redirects instead of hard gating.
Stumbling Into AI Emotional Dependence: How Routine AI Interactions Reshape Human Connection
A new paper argues that AI emotional dependence emerges incidentally through everyday task-oriented AI interactions rather than deliberate use of companion apps, with a 28-day longitudinal study (conducted with OpenAI) showing a 10.3% decrease in preference for human emotional support and 11.6% increase in preference for AI support. The authors call for policy reforms targeting general-purpose AI systems, not just dedicated companion chatbots.
What if AI systems weren't chatbots?
This paper critiques the dominance of chatbot interfaces in AI, arguing they have structural downsides and societal harms, and proposes alternative pluralistic system designs.
The complexities of patient-centred conversational artificial intelligence
This paper analyzes 2,053 real patient-chatbot conversations to show that communication styles vary widely and can significantly alter triage outcomes, finding that patient simulators that model emotional state and conversational strategy produce conversations nearly indistinguishable from real ones in a Turing test.