Emotional self-disclosure can also work with a chatbot

Emotional and factual disclosure were compared

The study distinguished whether participants shared emotionally significant or rather factual information. This distinction helps to examine whether emotional self-disclosure in particular has special consequences.

Additionally, participants believed their counterpart was either a chatbot or a person. This allowed the assumed social source to be varied while keeping the conversational situation comparable.

Subsequent psychological, relational, and emotional effects were measured. The experiment thus considers more than an immediate evaluation of the response text.

The provided abstract does not mention long-term observation. The results should therefore be limited to the short-term consequences examined.

The emotional impact was equivalent

The consequences of emotional self-disclosure did not differ depending on whether the counterpart was understood as a chatbot or a human. Technical mediation did not cancel out the effect of speaking out in this experiment.

This supports the idea that part of the relief can arise from one’s own formulating, structuring, and sharing. The counterpart does not necessarily have to be experienced as a human for this to happen.

Equivalence in this result does not mean that humans and chatbots are equivalent in every conversational dimension. Relationship, responsibility, professional judgment, and long-term support were not replaced by this.

For product communication, the precise statement is more valuable than a grand promise: An AI conversation can offer space for self-disclosure without posing as therapy or a human relationship.

Transparency does not have to destroy the effect

If emotional self-disclosure works even when the chatbot counterpart is known, there is no factual reason to conceal the machine identity in order to retain the benefit.

Clear AI labeling can be visible at the start and remain present in the conversation space. It does not have to begin every single response with a disruptive notice.

Transparency helps people form the right expectations: the system generates responses technically, has no human biography, and can only react to the information provided.

Natural language, a distinct name, and a warm space remain possible. What matters is that design does not obscure the AI’s role.

Labeling can alter trust depending on the context

Mozafari, Weiger, and Hammerschmidt examined in two experiments how disclosing a chatbot’s identity affects customer loyalty. The effect depended on the criticality and outcome of the service encounter.

For services with high criticality, labeling had an indirect negative effect on customer loyalty, mediated by lower trust.

When the chatbot could not resolve the service problem, however, disclosure had no negative effect and could even positively influence loyalty.

Transparency is thus not a neutral design element but shifts perception depending on the situation. A short-term loss of trust may nonetheless be ethically and legally necessary and can foster more realistic expectations in the long run.

Openness increases responsibility for data

When people confide emotional content to a chatbot, particularly sensitive conversation data is generated. The potential benefit of self-disclosure does not justify unlimited storage.

A guest mode can store content only in the browser. A voluntary account can enable continuity across devices. These differences should be explained in everyday language before the user starts.

Quality analysis requires separate agreement and data minimization. Names, rare events, timestamps, and verbatim phrasing can be re-identifiable even without direct identifiers.

The user should be able to delete individual conversations, reset all data, and understand which persistent settings are stored.

Export is just as important as deletion. Anyone who collects personal content over an extended period should be able to retain it in a comprehensible format without needing technical database knowledge.

On shared devices, the guest mode must make clear that browser data may be visible to other user profiles. Stored locally does not automatically mean private.

The effect of the telling and the effect of the response are different

The study on self-disclosure shows that the act of speaking itself can have consequences. It does not say that every subsequently generated response is helpful or harmless.

A chat can diminish the benefit of storytelling through interruption, premature interpretation, or unsolicited actions. Especially after a personal disclosure, the response should respect the desired mode.

Evaluation should therefore separate two questions: what effect does formulating and sharing have, and what additional effect does the system’s specific response produce?

A local design without AI can already enable writing. A generative partner adds resonance and must be independently assessed for this additional effect.

A comparison with a diary or a non-responding form would also be instructive. It could show what share stems from the writing itself and what share from the perceived response.

Longer trajectories should additionally capture whether the system respects openness or intensifies it through increasingly personal follow-up questions. The user must be able to determine at all times how deep the conversation goes.

Safety and trustworthiness require a life cycle

Huang, Ruan, Huang and colleagues categorize LLM risks as inherent problems, attacks, and unintended errors. Their review considers more than 370 references.

They propose four complementary perspectives: falsification and evaluation, verification, runtime monitoring, and regulation and ethical use.

For a conversational offering, this means testing not only before launch. Model updates, new error patterns, outages, and actual usage must be observed during operation.

Runtime monitoring, however, must not lead to covert full analysis of all private conversations. Protection and quality gains must be combined with voluntary, minimized procedures.

A technical counterpart can create space

The experiment by Ho, Hancock, and Miner shows that emotional self-disclosure to an alleged chatbot can have short-term consequences comparable to those with a human.

The supplementary studies show that labeling changes trust depending on context and that trustworthy operation requires more than visible disclosure.

A good AI conversational partner therefore does not need to imitate a human identity. It can be openly technical, respectful, and low-threshold, and support the benefit of telling one's story.

Emotional self-disclosure can also be effective with a chatbot. This space becomes responsible when transparency, data control, and the quality of the response are taken as seriously as the opportunity to speak at all.