Search by topic, publication type, year, open access, author, journal, and DOI.
Free-text queries are sent to OpenRouter and VoyageAI only after you submit them. Privacy
Conversation quality
Recent research describes a recurring problem in personal AI conversations: models sound empathetic but fall back into the same supportive role across very different situations. The missing quality is not more warmth, but a conversational posture that fits the moment.
Relationship & impact
A study published in Science shows why uncritical affirmation in personal conflicts is not just a matter of style: it can shift judgments, weaken responsibility, and still be perceived as particularly helpful.
Tests & data
Conversation quality does not emerge in a single sentence. An overview of 125 studies and three benchmarks for multiple complete multi-turn conversation trajectories shows why technique, fit, relationship impression, responsibility, and trajectory must be examined separately.
Relationship & impact
There is no clean dividing line between helpful use, trust, and emotional dependence. Current long-term data, experiments, and usage reports show which signals should be taken seriously – and why neither intensive use nor a positive outcome alone proves dependence.
Conversation quality
A mental-health chatbot can seem cautious in its first response and still overstep its role later. Two studies show why conversational boundaries must be examined across entire conversation trajectories.
Therapy & mental health
A representative US survey shows how widespread AI chatbots for mental health questions already are among 12- to 21-year-olds – and how rarely others learn about it.
Tests & data
A CHI 2026 study compares GPT-4o, GPT-5, and highly rated Reddit advice. The results are strong – but narrower than the headline suggests.
Therapy & mental health
AI conversations can facilitate access and appear convincing. However, the lead publication does not provide an efficacy test, but rather a broad problem map. For mental health care, this implies a strict standard: linguistic quality, perceived empathy, and technical reach must not be confused with proven benefit.
Therapy & mental health
AI-based conversational systems can reduce depressive symptoms and distress. However, the research does not thereby demonstrate broad psychological benefit or equivalence with human treatment. Those who nevertheless speak of replacement are confusing a limited result with a judgment about care.
Therapy & mental health
A randomized study with 148 young adults found lower depression scores after one week for the CBT-based conversational system XiaoE than for an e-book or a general assistant. What is crucial, however, is what does not follow from this: measured working alliance is neither a human relationship nor evidence of long-term care.
Therapy & mental health
Empathetic language can create the impression of reliable help. But what is ethically decisive is not only how convincingly an AI system responds, but who takes responsibility for its use, sets boundaries, protects data, and answers for harm. It is precisely this responsibility that often remains vague in the debate.
Therapy & mental health
A large review maps the ethical conflicts of AI-assisted conversations in mental health care. Its most important finding is not a ranking of technical deficiencies. Rather, safety, confidentiality, efficacy, and fairness depend on an unresolved preliminary question: What role may a system assume at all?
Therapy & mental health
Whether generative AI improves mental health care is not decided solely by its answers. The central test lies behind that: in robust evidence, sustained use, human support, data protection, and integration into real care processes.
Therapy & mental health
Large language models can explain, assess, and respond directly to psychological distress. It is precisely these fluid transitions that are the problem: users cannot tell from the response whether they are receiving information, automated assessment, or an intervention. Each of these roles requires different evidence and responsibilities.
Therapy & mental health
Psychological AI apps can seem always available, personal, and non-judgmental. Yet it is precisely these qualities that increase the risk of overestimating their role. What is decisive is not how human a system sounds, but whether its limits and the lack of responsibility remain clear in the conversation.
Therapy & mental health
A meta-analysis finds statistically significant short-term effects of digital conversational agents for psychological distress. However, long-term benefits, clinical relevance, safety, and actual relief for the care system remain insufficiently supported. Precisely for this reason, a measurable effect must not become a comprehensive promise of care.
Therapy & mental health
A meta-analysis finds weak evidence for improvements in individual mental health complaints, but hardly any reliable safety data. At the same time, the systems are predominantly perceived positively. It is precisely this combination that is delicate: good user experiences can conceal an evidence gap that they do not close.
Therapy & mental health
Reviews show why people open psychological AI apps, confide personal matters to them, and leave them again. However, pleasant conversations do not imply clinical efficacy or reliable help in crises. Product development and research in particular must keep these levels clearly separate.