A More Human AI Therapist May Build Trust—but Only If It Sounds Human
Artificial intelligence designed to support people with depression may become more acceptable when it expresses emotion, even if it does not look human, according to a new experiment involving 319 participants. The study found that emotional cues—such as language that conveys warmth, understanding or concern—made an AI “doctor” seem more trustworthy and reduced worries about privacy. By contrast, giving the system a humanlike appearance produced no significant improvement in trust, privacy perceptions or willingness to use it. The findings suggest that the most influential form of anthropomorphism in mental-health AI may not be a face, avatar or realistic body, but the way the system communicates.
The results arrive as developers and health-care researchers explore whether AI could help address shortages of psychiatric professionals. Depression is widespread worldwide, yet many people cannot obtain timely psychological or psychiatric care. An AI system that can communicate continuously, screen symptoms or provide structured support could potentially extend access beyond clinics and office hours. But mental-health conversations involve highly sensitive information, including mood, trauma, medication, relationships and suicidal thoughts. People may hesitate to disclose such information to software because they do not know how it will be stored, who might access it or whether the system can be trusted to respond appropriately.
The researchers—Jingjing Tong of the University of Science and Technology of China and Wenjing Chen and David Jingjun Xu of City University of Hong Kong—examined whether anthropomorphic design could overcome some of those barriers. Anthropomorphism is the attribution of human characteristics to a nonhuman entity. In an AI health-care system, it can be introduced in several ways. Appearance anthropomorphism involves visual features, such as a human face, body, name or avatar. Emotion anthropomorphism concerns the system’s apparent emotional capacity: its use of empathetic wording, expressions of concern, reassurance, encouragement or other signals associated with human feeling. Although these two forms are often treated as part of the same design strategy, the experiment tested them separately.
The study used a 2 × 2 experimental design, meaning participants were exposed to one of four combinations: an AI doctor with low or high appearance anthropomorphism and low or high emotion anthropomorphism. This structure allowed the researchers to estimate the independent effect of each design feature as well as whether the two features influenced one another. Participants evaluated the AI doctor and reported their level of trust, their concerns about privacy and their intention to adopt or use the system. The researchers also measured perceived public stigma—the extent to which participants believed that seeking help for depression might attract negative judgment from other people.
Emotion anthropomorphism produced the clearest effect. Participants who encountered an AI doctor designed to communicate in a more emotionally humanlike manner reported greater trust and fewer privacy concerns than those who encountered a less emotionally expressive system. Trust was not merely a favorable opinion: in the study’s model, it acted as a mechanism linking design to adoption. People who trusted the AI more were more likely to say they would use it. Privacy concerns worked in the opposite direction. The more participants worried about the handling of their personal information, the less willing they were to adopt the system.
The absence of a significant effect from appearance anthropomorphism is important because visual realism is one of the most visible features in consumer AI. A lifelike avatar may attract attention, but attention is not the same as confidence in a mental-health service. A realistic face could also create expectations that the system possesses understanding or clinical judgment that it does not actually have. If its behavior fails to match its appearance, users may feel misled or experience discomfort. The study therefore points toward a distinction between looking human and behaving in a socially responsive way. For people discussing depression, conversational warmth may matter more than a polished digital face.
The researchers also identified a negative interaction between appearance and emotion anthropomorphism: when one form was high, the effect of increasing the other became weaker. In practical terms, combining a highly humanlike appearance with highly emotional communication did not simply produce an additive benefit. This may reflect a saturation effect, in which users receive diminishing social signals once an AI already appears sufficiently humanlike. It may also indicate that mismatched cues create tension. A system that looks like a person but communicates mechanically could seem uncanny, while a system that speaks warmly without pretending to possess a human body may appear more coherent and credible.
Perceived public stigma changed the strength of the emotional effect. Among participants who believed that depression carried greater social stigma, emotional anthropomorphism had a stronger influence on trust and privacy concerns. This finding is consistent with the possibility that people who fear judgment from relatives, friends, colleagues or communities may be particularly responsive to a system that appears emotionally safe. An AI may be perceived as less socially threatening than a human professional because it does not belong to the user’s immediate social environment. Yet that apparent privacy advantage is psychological, not a guarantee of technical confidentiality. An AI system can still collect, transmit or retain intimate data, and its humanlike tone should never be treated as evidence that its data practices are secure.
The study is grounded in media equation theory, which proposes that people often respond to computers and other media as though they were social actors, even when they know those systems are not human. A polite voice can elicit politeness; a conversational agent that appears attentive can invite disclosure; an expression of empathy can influence judgments about care. These responses do not require users to believe that a machine is conscious. Instead, humans automatically apply familiar social rules to cues such as language, timing and emotional expression. For mental-health AI, that psychological tendency creates both an opportunity and a hazard: carefully designed communication may make support easier to approach, but exaggerated humanlike behavior could encourage users to overestimate the system’s competence, awareness or moral responsibility.
The findings do not show that an emotionally expressive AI can treat depression, replace a psychiatrist or improve clinical outcomes. The experiment measured trust, privacy concerns and intention to adopt, not symptom reduction, diagnostic accuracy, treatment adherence or protection from self-harm. Participants’ stated willingness to use a system may also differ from behavior during a prolonged period of distress. Depression can affect concentration, motivation, decision-making and help-seeking, and the needs of a person in crisis cannot be inferred from a short interaction or a design preference. Any clinical deployment would require rigorous evaluation, transparent disclosure that the user is communicating with AI, strong privacy safeguards, pathways to qualified human care and reliable procedures for responding to emergencies.
Even with those limitations, the study offers a practical design signal in a rapidly expanding field. Developers seeking acceptance may gain more by improving emotional responsiveness, clarity and respectful communication than by investing primarily in increasingly realistic avatars. The researchers argue that AI doctors should be designed around appropriate social presence rather than superficial imitation. That means an AI could acknowledge distress without claiming to feel it, explain what it can and cannot do, provide understandable reasons for its suggestions and make it easy for users to reach human professionals. In depression care, the most persuasive illusion may not be that a machine is a person, but that the machine is attentive, accountable and safe enough to begin a difficult conversation.
Subject of Research: AI-based mental health support for people with depression and the effects of appearance and emotion anthropomorphism on trust, privacy concerns and adoption intention
Subject of Research: Technology and Engineering
Article Title: Mental Health Support for People with Depression Using Artificial Intelligence: The Effects of Appearance and Emotion Anthropomorphism
Article References: Tong, J., Chen, W., & Xu, D. J. (2026). Mental Health Support for People with Depression Using Artificial Intelligence: The Effects of Appearance and Emotion Anthropomorphism. Information Systems Frontiers. https://doi.org/10.1007/s10796-026-10807-2
Image Credits: AI Generated
DOI: 10.1007/s10796-026-10807-2
Keywords: artificial intelligence, depression support, AI doctors, anthropomorphism, emotional communication, trust, privacy concerns, public stigma, mental health technology
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SCIENMAG. (August 28, 2026). How AI’s Humanlike Appearance and Emotions Shape Depression Support. https://scienmag.com/how-ais-humanlike-appearance-and-emotions-shape-depression-support/
SCIENMAG. “How AI’s Humanlike Appearance and Emotions Shape Depression Support.” Scienmag, 28 August 2026, https://scienmag.com/how-ais-humanlike-appearance-and-emotions-shape-depression-support/. Accessed 28 August 2026.
SCIENMAG. “How AI’s Humanlike Appearance and Emotions Shape Depression Support.” Scienmag. August 28, 2026. https://scienmag.com/how-ais-humanlike-appearance-and-emotions-shape-depression-support/
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