Kind words count for less when people think AI wrote them

A label changed how heard people felt

In a 2024 experiment, Yidan Yin, Nan Jia and Cheryl Wakslak asked 455 people in the United States to describe, out loud, a complicated situation they were dealing with. The next day each person read a short written reply. Half the replies came from another participant in the study who had been asked to make them feel understood. The other half came from Bing Chat, Microsoft's chatbot, given the same request and a word limit set to the human replies' median, 52 to 63 words. [S01]

The team then assigned each reply a label at random, from a person or from Bing Chat, whoever had written it. On a seven-point scale, AI-written replies made people feel more heard (5.74 against 5.17), and an AI label made them feel less heard (5.13 against 5.81). The two effects were about the same size and did not interact, so a Bing Chat reply labeled as Bing Chat scored about the same as a human reply labeled human. The highest scores went to AI replies that people believed a person had written. The study did not test why the label had this effect. [S01]

The same kind of reply scored lower when people were told a chatbot wrote it. That pattern recurs in the studies below, within limits that apply to all of them. Each used short written replies, usually one message and one answer. Participants took part over the internet, and the outcome was how heard, cared for or satisfied people said they felt, minutes to a day later. [S01, S03, S04, S05, S07]

The same kind of reply scored lower when people were told a chatbot wrote it.

Blind raters preferred the chatbot's longer answers

Much of the interest traces to a 2023 study by John Ayers and colleagues. They drew 195 questions that people had posted to Reddit's r/AskDocs forum, each already answered there by a verified physician, and put each question to ChatGPT. Three of five licensed clinicians on the team rated each pair of answers without knowing which was which, and they preferred the chatbot's answer in 78.6 percent of the 585 ratings. Nobody saw a label, so the study measured the words alone. [S02]

Of the chatbot's answers, 45.1 percent averaged a rating of empathetic or very empathetic, against 4.6 percent of the physicians' answers, a share the authors put at 9.8 times higher. The chatbot's replies were about four times longer, 211 words against 52. [S02]

The authors list the weak points themselves. The raters were coauthors, the empathy scale had not been validated, no patients rated anything, and the extra length "could have been erroneously associated with greater empathy." Against the longest quarter of physician answers, the chatbot's lead fell to 62.0 percent. [S02] Yin's team matched reply lengths and still found an AI-written advantage, in a different setting: personal situations in place of medical questions. [S01]

The chatbot's replies were about four times longer, 211 words against 52.

Labels shrank the chatbot's lead and sometimes erased it

Dariya Ovsyannikova, Victoria Oldemburgo de Mello and Michael Inzlicht ran four studies in which 556 paid participants rated replies to other people's experiences. ChatGPT's replies were rated more compassionate than those of selected human responders in the first two studies, and than those of five volunteers from a Toronto crisis line in the other two. Telling raters which reply came from AI roughly halved its lead. In one study the gap fell from 0.65 to 0.27 points, and in another from 0.83 to 0.38, so the AI replies stayed ahead. [S03]

Moritz Reis, Florian Reis and Wilfried Kunde showed 2,280 people identical medical advice, drafted with ChatGPT and checked by a physician, labeled as coming from a human physician, from AI, or from a physician using AI. Advice labeled human was rated more empathic and more reliable. Advice labeled as AI-assisted was marked down about as much as advice labeled AI, and people said they were less willing to follow either. [S04]

A survey at Duke University Health System, run in late 2023 and published in 2025, moved the test to patients. Joanna Cavalier and colleagues showed 1,455 members of its patient advisory panel replies to made-up messages sent through a patient website, written by physicians or by ChatGPT and labeled as from AI, from a human, or not labeled at all. Respondents preferred the AI drafts, most of all on feeling cared for. An AI label lowered satisfaction by 0.13 points on a five-point scale against a human label, and a human label scored no differently from no label. [S05]

That last result bears on a gap Yin's team named. Their study had no unlabeled condition, so it could not say whether the AI label lowered ratings or the human label raised them. In the Duke survey the AI label did the lowering, at least for health messages read by an older, highly educated panel. [S01, S05]

A nine-study paper by Matan Rubin and colleagues, with 6,282 participants, reports the same direction for replies to people's own emotional situations: AI-written replies labeled human were rated more empathic and supportive than the same replies labeled AI. The abstract of its preprint, the version posted before peer review, adds that people's own unprompted suspicion that AI had helped write a "human" reply also lowered how empathic it seemed. We read only that abstract, because the published article was closed to us. [S06]

Telling raters which reply came from AI roughly halved its lead.

Most people still picked a person, by a modest margin

Joshua Wenger, Daryl Cameron and Michael Inzlicht asked what people choose when they can. In four studies, participants picked from a deck marked HUMAN or one marked AI and then received an empathic reply written by that source, so every label was true. Across the studies they chose a human 57 to 62 percent of the time. In two of the studies, about a quarter of participants chose AI most of the time. [S07]

Their fourth study used people's own lives. Each participant described six real situations, good and bad, from work, relationships and personal goals. About eight hours later they read replies to two of them, written by another paid participant or by ChatGPT. Knowing which was which, they rated the AI replies as more empathetic and said those replies made them feel more heard. [S07]

In Yin's study, an honestly labeled AI reply did no better than an honestly labeled human one. The human writers, the chatbots and the situations all differed between the two studies, and any of them may explain the gap. [S01, S07]

Across the studies they chose a human 57 to 62 percent of the time.

A kindness rating scores the words and the sender

We think a rating of how empathic a reply feels scores two things at once: the words, and the reader's belief about whether someone chose them. Blind studies such as Ayers's measure the words alone. Labeled studies add the belief, and the belief moves the score. So the answer to whether AI is more empathetic depends on whether the reader knew, who did the rating (the person helped or an onlooker), and who the human comparison was. [S01, S02, S03]

Wenger's data offer one clue about the belief. The more effort people thought had gone into a source's replies, the more they chose that source and the higher they rated its replies. [S07] That suggests people read kindness partly as effort someone spent on them. We hold the idea loosely, because the same participants rated the AI replies as the more effortful ones, and no study here varied effort on purpose.

Every human in these comparisons was a stranger to the person reading: physicians answering on a forum, paid study participants, research assistants or crisis-line volunteers. Nothing here compares a chatbot with a friend, a partner or a doctor who knows the patient. [S01, S02, S03, S05, S07]

Labeled studies add the belief, and the belief moves the score.

The ratings moved, and behavior was barely measured

These are ratings of short written exchanges, collected within a day. None of the studies followed people across repeated conversations, in voice, or with a chatbot they already use. [S01, S07]

Only one measure came close to behavior. In Reis's second study, people could save a link to the advice platform, and they did so at about the same rate whatever the label: 19.3 percent for human, 18.5 percent for AI and 22.9 percent for a human with AI. [S04]

Health systems have begun using AI to draft replies to messages patients send through hospital websites, which makes the label a policy choice. In the Duke survey, disclosing AI cost 0.09 points on the five-point satisfaction scale against saying nothing, and at least three quarters of respondents were satisfied whatever the label said. The authors concluded that disclosure should stay. [S05]

Only one measure came close to behavior.