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Home International

Commentary: AI can be more comforting than a person. Here’s what humans can learn from it

by Theinsightpost
October 3, 2026
in International
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PROVIDING “ACTIONABLE SUPPORT”

Our team at the universities of Manchester and Durham conducted five experiments comparing emotional-support messages written by people with those produced by large language models (LLMs), the systems behind chatbots such as ChatGPT.

In one experiment, 390 participants imagined situations involving anger, sadness or fear. They then read either a human-written or gen AI response without being told its source. For the anger and fear scenarios, participants rated the gen AI messages as more emotionally supportive. There was no reliable difference for sadness.

Gen AI messages also improved some of the emotions participants reported, although the pattern varied. In the fear scenario, for example, they increased calm more than human messages but did not produce a greater reduction in fear itself.

Our findings fit a recent review of 23 studies. Overall, people tended to rate gen AI messages as more empathic, meaning that they appeared understanding and caring, than human-written ones. Researchers call this the “AI advantage”.

One possible reason is consistency. Gen AI can produce a structured response on demand, while people may feel tired or unsure what to say. It also tends to include a concrete suggestion that the recipient can follow.

Our experiments tested possible explanations for this advantage. In one experiment, explicitly acknowledging and validating the recipient’s feelings did not account for the greater emotional improvement associated with gen AI.

Two further experiments found stronger evidence for what we called “actionable support”: specific and realistic suggestions. Messages containing this practical help were rated as more comforting and improved some emotional responses, regardless of whether they came from gen AI or a person. When human messages offered comparable help, they were judged just as supportive in these comparisons.

Practical support also requires restraint. A 2024 study found that excessive suggestions could be less effective at making people feel heard. Before offering solutions, it may therefore help to ask whether advice is what the person wants. If it is, one manageable step may be more useful than a list of remedies.
 



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      Clear examples do more than decorate an explanation: they show how an idea behaves under recognizable conditions. In education and software documentation alike, strong illustrations begin with a defined goal, then reveal the steps, assumptions, and result without unnecessary detail. A practical test example should state the input, expected output, and boundary condition, because these elements show whether a rule works beyond the simplest case. Including a contrasting case also helps readers distinguish a valid application from a tempting but incorrect one, while concise notes explain why the outcomes differ. When examples are updated with current data and checked against the underlying rule, they remain useful across classrooms, product guides, and professional training.