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

Used carefully, generative artificial intelligence could help us become better at supporting one another, say psychology researchers.

A person chatting with an AI friend. (Photo: CNA)

DURHAM, England: Some of us now use generative artificial intelligence (gen AI) to talk through difficult feelings. We may ask a chatbot to write an email or plan a holiday, but we also tell it that we have had a rotten day or feel anxious about a presentation.

A 2025 US survey of 1,058 people aged 12 to 21 found that 13 per cent had sought advice from gen AI when feeling sad, angry or nervous. Among those aged 18 to 21, the figure was 22 per cent. These findings suggest that turning to gen AI for emotional or mental health support is already relatively common among young people.

So is gen AI a poor substitute for a friend? Human-written replies may seem the obvious source of greater support.

However, in some experiments, including our own, people have rated gen AI responses more highly.

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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.

WHY DO WE STILL PREFER PEOPLE?

The source attributed to a message changes how we receive it. The same 2024 study found that gen AI responses made people feel more heard than human-written ones, but this benefit declined when recipients believed the message came from AI.

Across nine further studies involving 6,282 people, the same gen AI responses were rated as more empathic and supportive when described as human-written rather than attributed to AI. Participants also preferred human interaction when seeking emotional engagement, even when choosing a person meant waiting longer.

Research has yet to establish exactly why. One possibility is that a human response signals someone’s willingness to spend time and emotional effort. Support from somebody close also arrives within an existing relationship.

Our research with romantic couples offers indirect evidence of the importance of the recipient’s perception. We asked one partner what they did to help the other manage difficult emotions, and asked the recipient what they believed their partner had done. Both people also rated their relationship.

The recipient’s perception of their partner’s efforts was more consistently associated with both partners’ ratings of their relationship than the helper’s own account. A separate study found that valuing and receptive listening were associated with greater relationship satisfaction.

These studies identified associations. They did not establish that particular forms of support caused better relationships or explain why people prefer human support. They do, however, show what comparisons between isolated messages leave out. Comfort from somebody close comes with personal knowledge and the possibility of continued involvement. Gen AI can suggest going for a walk, but it cannot come along.

The wider research also has important limits. Much of it tested brief or one-off exchanges, often involving imagined everyday situations, and measured immediate reactions. It cannot tell us what happens when somebody relies on a chatbot over months or uses one during a mental health crisis.

The human messages were generally written by strangers or developed as representative examples. The experiments therefore did not compare gen AI with support from a friend who knew the recipient and understood the wider circumstances.

The practical lesson is that people can learn from what gen AI does effectively. When somebody is upset, ask whether they want advice. If they do, offer a specific next step that feels manageable.

Gen AI may help us find useful words and possible responses. Human relationships add personal knowledge and the ability to remain involved after the message has been sent. Used carefully, gen AI could help us become better at supporting one another.

Sarah A Walker is Assistant Professor of Educational Psychology at Durham University. Belen Lopez-Perez is Lecturer in Psychology at University of Manchester. This commentary first appeared on The Conversation.

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心理学研究人员表示，如果使用得当，生成式人工智能可以帮助我们更好地互相支持。

一个人正在和人工智能朋友聊天。（图片：中央社）

英国达勒姆：现在，我们中的一些人会利用生成式人工智能（gen AI）来倾诉难以排解的情绪。我们可能会让聊天机器人帮我们写邮件或规划假期，也会告诉它我们今天过得很糟糕，或者对即将到来的演讲感到焦虑。

2025年美国一项针对1058名12至21岁人群的调查发现，13%的人在感到悲伤、愤怒或紧张时曾向人工智能寻求建议。在18至21岁的人群中，这一比例为22%。这些发现表明，在年轻人中，寻求人工智能的情感或心理健康支持已相当普遍。

那么，人工智能能否很好地替代朋友呢？显然，人类撰写的回复才是更可靠的支持来源。

然而，在一些实验中（包括我们自己的实验），人们对人工智能生成的回答给予了更高的评价。

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提供“可操作的支持”

我们曼彻斯特大学和杜伦大学的研究团队进行了五项实验，比较了人类编写的情感支持信息与大型语言模型（LLM，聊天机器人（如 ChatGPT）背后的系统）生成的信息。

在一项实验中，390名参与者想象了涉及愤怒、悲伤或恐惧的情境。随后，他们阅读了由人类撰写或由人工智能生成的回复，但事先并不知道回复的来源。对于愤怒和恐惧的情境，参与者认为人工智能生成的回复更具情感支持性。而对于悲伤的情境，两者之间没有显著差异。

人工智能生成的信息也改善了参与者报告的一些情绪，尽管改善模式有所不同。例如，在恐惧情境下，人工智能生成的信息比人类生成的信息更能提升平静感，但并没有更有效地降低恐惧感本身。

我们的研究结果与近期一项包含23项研究的综述相符。总体而言，人们倾向于认为人工智能生成的讯息比人类撰写的讯息更具同理心，也就是说，它们显得更善解人意、更关心他人。研究人员称之为“人工智能优势”。

其中一个可能的原因是一致性。人工智能可以按需生成结构化的回复，而人们可能会感到疲惫或不知所措。此外，它通常还会包含接收者可以遵循的具体建议。

我们的实验检验了这种优势的可能解释。其中一项实验表明，明确地认可和肯定接收者的感受并不能解释人工智能带来的更显著的情感改善。

另两项实验为我们所谓的“可操作支持”——具体且切合实际的建议——提供了更有力的证据。包含此类实用帮助的信息被认为更令人感到安慰，并能改善某些情绪反应，无论这些信息是来自人工智能还是真人。当真人提供类似帮助时，在这些比较中，它们同样被认为具有支持作用。

提供实际帮助也需要克制。2024年的一项研究发现，过多的建议反而可能降低人们感受到被倾听的效果。因此，在提供解决方案之前，最好先询问对方是否需要建议。如果是，那么一个切实可行的步骤可能比一长串补救措施更有用。

为什么我们仍然更喜欢人？

信息的来源会影响我们对信息的接收方式。同一项2024年的研究发现，人工智能生成的回复比人工撰写的回复更能让人感到被倾听，但当接收者认为信息来自人工智能时，这种优势就会减弱。

在另外九项涉及6282人的研究中，当同一代人工智能的回复被描述为由人类撰写而非由人工智能生成时，参与者认为这些回复更具同理心和支持性。此外，当寻求情感交流时，参与者也更倾向于与真人互动，即使这意味着需要等待更长时间。

目前的研究尚未完全揭示其原因。一种可能性是，人类的这种反应表明了对方愿意投入时间和情感。来自亲近之人的支持也存在于现有的关系之中。

我们对情侣的研究间接证明了接受者的感知的重要性。我们询问其中一位伴侣如何帮助另一位应对负面情绪，并询问接受者认为伴侣做了什么。双方还对彼此的关系进行了评价。

相比于帮助者自身的描述，受助者对其伴侣努力的感知与双方对彼此关系的评价更为密切相关。另一项研究发现，重视和倾听与更高的关系满意度相关。

这些研究发现了一些关联。它们并未证实特定形式的支持能带来更好的人际关系，也没有解释人们为何更倾向于人际支持。然而，它们确实揭示了孤立信息之间的比较所忽略的问题。来自亲近之人的安慰源于个人了解以及持续参与的可能性。人工智能可以建议散步，但它无法陪同。

更广泛的研究也存在重要的局限性。其中许多研究测试的是简短或一次性的交流，通常涉及想象的日常情境，并衡量的是即时反应。这些研究无法告诉我们，当人们依赖聊天机器人数月之久，或者在心理健康危机期间使用聊天机器人时会发生什么。

这些信息通常由陌生人撰写，或仅作为代表性示例。因此，这些实验并未将人工智能生成的信息与来自认识收件人并了解更广泛情况的朋友的帮助进行比较。

实际经验是，人们可以从人工智能有效运作的方式中学习。当有人情绪低落时，询问他们是否需要建议。如果他们需要，就提供一个他们觉得可行的具体后续步骤。

人工智能或许能帮助我们找到有用的词语和可能的回复。人际关系则能提供更深入的了解，并让我们在信息发出后继续保持联系。如果运用得当，人工智能可以帮助我们更好地互相支持。

莎拉·A·沃克是杜伦大学教育心理学助理教授。贝伦·洛佩兹-佩雷斯是曼彻斯特大学心理学讲师。本文最初发表于The Conversation网站。

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