AI Companions and Loneliness: What the Studies Actually Show

The short answer. The evidence points in two directions at once, and the split is not a sign that someone made a mistake. Controlled experiments find that talking to an AI companion produces a real but brief drop in loneliness. Studies that follow people for months find the opposite pattern: heavier use tracks with rising loneliness over time. Both findings can be true, because they measure different things over different spans. Below are twenty pieces of research, what each one actually tested, how far its design lets you generalise, and where the research still has nothing to say.

Disclosure. This piece was published by the aimy.chat team. It is a review of published research, not a product comparison and not an ad for the service. We link to our own explainer on why AI companions took off once, for background, and mention the service nowhere else.

Why the studies disagree, in one paragraph

A one-week lab study and a twelve-month tracking study are not two attempts at the same question. The first asks whether a conversation makes you feel less alone right after it ends. The second asks what happens to people who keep coming back for a year. A meta-analysis of care-home robots and a survey of Character.AI users are further apart still: different technology, different population, different measure. Most of the apparent conflict in this literature dissolves once you line up what each study included and how long it watched. The rest of this article gives you that apparatus, then applies it.

How to read a study about AI companions

Four distinctions do almost all the work. Each one is illustrated below with a study from this article rather than an abstract example.

Randomised controlled trial, longitudinal study, cross-sectional survey. An RCT assigns people to conditions at random, which is the only design here that supports a causal claim. Guingrich and Graziano ran one: 183 participants, 21 days, Replika against text-based word games. A longitudinal study follows the same people over time without assigning anything, which shows sequence but not cause. Folk and Dunn ran one across four waves and twelve months. A cross-sectional survey photographs one moment. Nakagomi and colleagues surveyed 14,721 Japanese adults at a single point. The sample is enormous. It still cannot tell you which came first.

Why a meta-analysis usually outweighs a single study. Pooling many studies smooths out the noise of any one sample and lets you test whether an effect holds across contexts. Two meta-analyses appear below, one pooling 19 studies and one pooling 47 effects. Set against those, a qualitative study of 14 people carries a different kind of weight. It can show you a mechanism nobody had described. It cannot tell you how common that mechanism is.

Correlation is not cause, and the authors usually say so. Researchers in this field are unusually candid about it. Jain and colleagues call their own cross-sectional findings exploratory and non-causal, in the abstract, before anyone else can. Reverse causation is the specific worry. If lonely people use companion apps more, a survey will show a link between use and loneliness whichever direction the arrow actually runs.

Preprint means not yet reviewed. One entry in this article, the MIT Media Lab and OpenAI pair of studies, is a preprint. It is also the most widely reported work in the field. Those two facts sit together uncomfortably, and the label is repeated every time the work appears below.

StudyDesignSampleTime frameFindingStatusMehrabi & Ghezelbash, 2025, The GerontologistMeta-analysis, 19 studiesN=1,083—Social robots reduce loneliness in older adults, d=−0.590. No text-based apps were includedPeer-reviewedDong, Xie & Gong, 2025, Cyberpsychology, Behavior, and Social NetworkingMeta-analysis, 47 effects from 21 publicationsnot reported in the abstract—Embodied AI r=−0.266 (p=0.088); disembodied AI r=+0.352 (p<0.001)Peer-reviewed, paywalledSatake et al., 2026, Psychological MedicineSystematic review and meta-analysis, 17 studiesadults 60+—Moderate improvement; none of the 17 showed worseningPeer-reviewedDe Freitas et al., 2025, Journal of Consumer ResearchFive studies: field data plus experimentsup to ~600, MTurkup to 1 weekMomentary reductions in lonelinessPeer-reviewedGuingrich & Graziano, 2025, AIES-25Pre-registered RCTN=18321 daysNo significant change in either groupPeer-reviewedMurayama & Takase, 2025, JMIR AgingRCT, voice-based robotN=68 (34/34)4 weeksLoneliness fell further in the intervention group, DID −3.1 (P=.03)Peer-reviewedFolk & Dunn, 2026, Psychological ScienceLongitudinal, four wavesN=2,14912 monthsRising use predicted rising lonelinessPeer-reviewedMIT Media Lab × OpenAI, 2025RCT plus observational analysisN=981; 4,000 surveyed4 weeksAssigned condition produced no effect; heavier voluntary use tracked with higher lonelinessPreprintYuan et al. (Aalto), 2026, ACM CHIQuasi-experiment plus interviews~2,000 Reddit users2 yearsShort-term relief alongside rising distress markersPeer-reviewedZhang et al. (Stanford), 2026, Nature Human BehaviourSurvey plus chat-log analysisN=1,131; 237 log donors—Heavy use with a small offline network tracked with lower well-beingPeer-reviewedNakagomi et al., 2026, Technology in SocietyCross-sectional national surveyN=14,721—Linked to higher well-being, strongest at moderate friend-network sizePeer-reviewed

What the controlled experiments found

The strongest evidence for a benefit comes from short, tightly controlled contact.

De Freitas and colleagues published five studies in the Journal of Consumer Research in June 2025, combining analysis of real conversations and app reviews with experiments, including a week of repeated use. Talking to an AI companion reduced loneliness about as much as talking to a person, and more than watching YouTube. The proposed mechanism is the sense of being heard. Their own summary carries the limit inside it: the reductions in loneliness are "momentary", measured after use across a week. That single word does the work, and the authors say plainly that their design supports no conclusion about long-term well-being.

Murayama and Takase ran a genuine RCT in Japan, published in JMIR Aging in 2025. Seventy-three older adults were randomised, 68 completed, split evenly between groups, mean age 82.3, and 94% women. Over four weeks with a small speaker robot, loneliness on the 20-item UCLA Loneliness Scale fell by 3.7 points in the intervention group against 0.6 in the control, a difference-in-differences of −3.1 (95% CI −5.9 to −0.4; P=.03). The design is sound. The sample is one geographic area, almost entirely female, with no blinding.

Then the counterweight, from the same tier of evidence. Guingrich and Graziano at Princeton pre-registered an RCT of 183 people who used either Replika or text-based word games for ten minutes a day across 21 days. Social health and relationship measures did not shift significantly in either group. The one signal they found was among participants who strongly anthropomorphised the bot. The authors hedge honestly: "It is possible that longer or more intensive exposure might have a greater impact, but at least over the 21 days of the present study" nothing moved.

One more study is often filed with these and does not belong there. Kim and colleagues reported reduced loneliness among 176 Korean students using the chatbot Luda Lee, published in JMIR in 2025. It is a four-week quasi-experiment with no control group, which the authors state directly. Without a comparison group, the improvement cannot be separated from time, attention, or expectation.

What the long-term and real-world data found

Extend the window and the picture inverts.

Folk and Dunn followed 2,149 adults across the UK, US, Canada and Australia through four survey waves over twelve months, published in Psychological Science in 2026. Two findings, and they are separate. Increases in social chatbot use predicted later increases in loneliness. Independently, declines in social connectedness predicted later increases in chatbot use, rather than the other way round. The authors close their abstract with a warning that most coverage of the study dropped: "We urge caution, however, in drawing strong conclusions given the exploratory nature of our analyses."

The MIT Media Lab and OpenAI studies, both preprints, are the most cited and the most misread work here. There are two layers. In the randomised layer, 981 participants were assigned to text, neutral voice, or engaging voice conditions for four weeks, and the assigned condition produced no significant effect on well-being. In the observational layer, covering four million conversations and a survey of 4,000 users, people who used the product more heavily of their own accord reported higher loneliness, stronger emotional dependence, and less time socialising with people. The distinction matters: the experiment failed to move anything, and the association lives in self-selected volume. OpenAI's own limitations section concedes the obvious constraint: "28 days of usage may be too short a period for any meaningful changes in affective use or in emotional well-being to be measurable."

The longest observation in the field comes from Aalto University, published at ACM CHI 2026. Yuan and colleagues compared roughly 2,000 Reddit users' posts in the year before and the year after they began using a companion app, then interviewed 18 of them. Short-term relief showed up alongside rising markers of distress in the language of long-term users. The interviews suggested a mechanism: unconditional support from a bot raises the perceived cost of human relationships, which demand effort and tolerate friction.

Zhang and colleagues at Stanford combined a survey of 1,131 US adults with real conversation logs donated by 237 of them, 4,664 sessions and 464,687 messages, published in Nature Human Behaviour in 2026. Heavy use paired with a small offline network tracked with lower well-being. Willingness to disclose personal material to the bot also tracked with lower well-being, reversing what the literature finds for disclosure to people. Buried in their data is a warning about survey research generally: 12% named companionship as their reason for using the app, while more than half described the bot as a friend or partner. Self-reported motive and actual relationship did not match.

Two meta-analyses that look like they disagree

Here is the clearest worked example of why signs point in opposite directions, and why that rarely means anyone is wrong.

Mehrabi and Ghezelbash pooled 19 studies of 1,083 older adults in The Gerontologist in 2025. Social robots reduced loneliness with an effect size of d=−0.590 (95% CI −0.919 to −0.261, p=.002), and the effect was stronger in care homes. That is the largest quantified benefit anywhere in this literature.

Dong, Xie and Gong pooled 47 effects from 21 publications in Cyberpsychology, Behavior, and Social Networking in 2025 and reported something that looks like a flat contradiction. Physically embodied AI was associated with lower loneliness, r=−0.266, though at p=0.088 the authors call this marginal rather than significant. Physically disembodied AI went the other way and cleared the bar comfortably: r=+0.352, p<0.001. Overall, across everything, r=+0.163, p<0.05.

The two results are not in conflict, and the reason is in the inclusion criteria. Mehrabi compares subtypes within an already-embodied class, and their own moderator test finds no significant difference between humanoid robots, voice assistants and pet robots. Dong compares embodiment itself, across a far wider pool of technologies and ages. Those are two different variables wearing similar names.

The age split makes the same point twice. In Dong's data, adults 60 and over show a positive association between AI use and loneliness (r=+0.352, p<0.001), while people 35 and under show none at all (r=+0.039, p=0.659). Read alone, that appears to contradict Mehrabi, whose whole finding is that older adults benefit. It does not. Mehrabi pools intervention studies, where a robot is given to someone and loneliness is measured after. Dong averages a mixed correlational body, where the arrow can easily run backwards and lonelier older people simply use more.

So the transferable rule is short. Before comparing two numbers, check what each one let into its sample.

Two caveats belong with these figures. The full text of the Dong meta-analysis sits behind a paywall, so what appears above comes from the published abstract, and the pooled sample size and confidence intervals are not public. And neither meta-analysis is about the products most readers have in mind, a point the next section returns to.

Why a lab week and a tracked year point in opposite directions

The same logic resolves the sharpest disagreement in the primary literature: De Freitas against Folk and Dunn.

De Freitas measures relief in the moment. Participants have a short controlled conversation, sometimes repeated across a week, often recruited through MTurk, and report how they feel afterwards. Folk and Dunn measure a trajectory. The same people, using whatever they choose in ordinary life, surveyed four times across a year.

These are different questions. A conversation that reliably makes you feel better for an hour is entirely compatible with a year in which loneliness rises. The comparison that clarifies it is a painkiller: consistent short-term relief tells you nothing about whether the underlying condition is improving. Nothing in either study rules the other out.

The MIT and OpenAI preprints supply the third support, and they explain the pattern rather than adding to it. Assigning people to more chatbot exposure changed nothing measurable in four weeks. The association appeared where people chose their own volume. That is exactly the shape you would expect if the causal traffic runs partly in the other direction, with declining social connection driving use, which is what Folk and Dunn found on their second measure.

None of this makes the literature useless. It makes it specific. Short controlled contact produces short relief, reliably and repeatedly. Sustained heavy use chosen by the user tracks with worse outcomes, consistently across independent samples: four-country survey panels, Aalto's Reddit corpus, and Stanford's donated chat logs. Whether the second is caused by the first, nobody has shown.

Displacement or compensation

Two competing accounts sit underneath most of these findings, and both have names in the literature.

The systematic review by Hung and colleagues, which applied PRISMA to 39 empirical studies and appeared in Computers in Human Behavior: Artificial Humans in 2026, lists both as recognised categories alongside five benefit types and five risk types.

The displacement account says time and emotional energy spent with a bot come out of the budget for people. Three findings support it. Aalto's interviews describe exactly that transfer, with unconditional support making the ordinary effort and friction of human relationships look like a worse deal by comparison. Zhang's Stanford data shows the same pattern in behaviour: heavy use paired with a thin offline network tracks with lower well-being. And Malfacini argues over-reliance is the central risk, in a 2025 paper in AI & SOCIETY that comes with two caveats worth stating: it is an analytical article with no sample of its own, and the author is affiliated with OpenAI.

The compensation account says people reach for a bot because connection is already scarce, and that it helps most where the gap is real. The largest single sample in this article supports it. Nakagomi and colleagues surveyed 14,721 Japanese adults and found AI companion use associated with higher subjective well-being, with the strongest association among lonelier respondents.

One detail in that study is easy to garble. The U-shaped pattern they report attaches to friend-based social network and support, measured on the LSNS-6 scale, and not to how much someone uses the app. Benefits are most pronounced at moderate social connection and weaker at very high or very low levels. People with a moderate circle gain the most. Those with very rich networks have less to gain, and those with almost none appear to benefit less than you might expect.

No study settles the argument. The direction seems to depend on how much offline connection a person starts with and how intensively they use the product.

Dependence and attachment: where the research draws the line

Attachment to a chatbot is not a fringe phenomenon, and it is not evidence of a disorder either.

Xie and Pentina interviewed 14 Replika users for HICSS-55 back in 2022 and found that bonds formed along the lines attachment theory predicts, specifically under conditions of distress and thin social contact. Fourteen self-selected people cannot tell you how widespread this is. They can tell you the mechanism exists and what it looks like from inside.

State the diagnostic position once and clearly: there is no clinical diagnosis of AI companion addiction. Nothing in the DSM, nothing in the ICD. Researchers who study problematic use borrow Griffiths' behavioural framework and apply it to self-reported data, which is a reasonable research instrument and not a diagnostic protocol. Any article that tells you a percentage of users are "addicted" is reporting a questionnaire score.

What the behavioural pattern looks like was documented by Namvarpour and colleagues at CHI 2026, in an analysis of 318 posts from 291 authors aged 13 to 17 on r/CharacterAI between January 2023 and April 2025. Some of those teenagers described every marker in the Griffiths model, with sleep disruption, falling school performance and withdrawal from offline relationships. Two limits on reading that across: the sample is adolescents, and this article is otherwise about adults. It is also Reddit, so it captures people who post about their use.

Who benefits, and who is more at risk

Before the moderators, one distinction that search results routinely collapse.

An AI companion in this article means a text-based application such as Replika or Character.AI. A companion robot is a device, and the evidence bases barely overlap. The boundary is not the presence of a body, though, and getting this right matters. Mehrabi's definition of social robots stretches from physically embodied machines with sensors, cameras and actuators through to voice-based agents such as Amazon Alexa. What makes their d=−0.590 inapplicable to companion apps is narrower and firmer: not one of the 19 studies they pooled examined a text-based application. The line is drawn by inclusion criteria.

Satake's systematic review shows the same tilt internally, with nine of 17 studies on robots and eight on voice or screen agents.

Within the chatbot literature, several moderators show up repeatedly, and age is not the main one.

Offline network size appears in two independent datasets. Stanford's chat-log study links heavy use plus a small circle to lower well-being; Nakagomi's national survey finds the benefit concentrated at moderate network size. Same moderator, two designs, two countries.

Anthropomorphism is the second, and it came out of the null result. Guingrich found no group-level effect over 21 days, but participants who treated the bot as having a mind showed a different response. Hu, Mao and Kim reach a compatible conclusion from a cross-sectional survey of 516 Chinese adults: social anxiety predicts problematic use through a chain of loneliness and rumination, and the link strengthens when the user attributes a mind to the system.

Then a null result worth as much as the positives. Jain, Patole and Pareek surveyed 233 young adults in India and found AI use associated with lower anxiety (r=−0.12) and lower loneliness (r=−0.18), neither reaching statistical significance. Small, negative, published anyway.

Liu and colleagues surveyed 92 students at a private university in the northeastern United States and found they consistently rated AI companions as inferior to family, friends and mental health professionals for support quality. Small sample, and it measures expectations rather than behaviour under stress.

Finally, the sampling lesson. Maples and colleagues at Stanford surveyed 1,006 Replika users in npj Mental Health Research in 2024, and 90% reported experiencing loneliness against roughly 53% among students generally. That gap is not evidence the product causes loneliness. The authors name it themselves as selection bias: lonely people are more likely to be using a companion app and more likely to answer a survey about it.

What the research does not show

The gaps here are as informative as the findings, and no page in the current search results collects them.

Long-term effects past two years are essentially unstudied. The longest observation is Aalto's two-year Reddit window; the longest survey panel is Folk and Dunn's twelve months. Everything else measures weeks.

Causation is unproven nearly everywhere. Most of this evidence is cross-sectional or observational, and the authors of Jain, Hu, Nakagomi and the observational half of the MIT work all say so in print.

Nothing measures population-level effect. The WHO Commission report of June 2025 and the US Surgeon General's 2023 advisory are the standard sources on loneliness prevalence, and neither one mentions AI companions at all. Any claim that companion apps are moving national loneliness statistics has no source behind it.

No threshold has been established. The pattern "moderate use looks fine, heavy use looks worse" repeats across independent studies, and not one of them names a number of messages, minutes or weeks where the line sits.

There is no head-to-head comparison with psychotherapy on clinical outcomes. There is no clinical diagnosis, as above. And there is no research on people with a diagnosed anxiety disorder, as opposed to a high score on an anxiety scale.

The geography is narrower than an all-English evidence base suggests. Where a study reports the country of its sample, and only about half do, the answers cluster tightly: the United States, Japan, and one study each from South Korea, China and India, plus a single panel spanning the UK, US, Canada and Australia. Not one sample in this body of work is drawn from Africa, Latin America, continental Europe outside the UK, or Southeast Asia. Mehrabi states the version of this problem inside their own meta-analysis: "most studies were conducted in high-income countries, limiting the applicability of findings to low- and middle-income settings where access to social robots may differ significantly."

When you need a person, not a chatbot

This section rests on the position of a professional body rather than on common sense. In November 2025 the American Psychological Association issued its Health Advisory: Use of Generative AI Chatbots and Wellness Applications for Mental Health, and its core sentence is unambiguous: "GenAI chatbots, wellness apps that use GenAI, and digital wellness apps should not be used as a replacement for a qualified mental health care provider."

The advisory identifies situations where a chatbot should not stand in for a professional:

• Suicidal thoughts, or intent to harm yourself or someone else. The APA describes AI handling of crisis situations as limited and unpredictable.

• Signs of psychosis or delusional thinking. A separate risk applies here, because a system that agrees can reinforce a distorted belief instead of gently challenging it.

• Self-harm and eating disorders.

• Obsessive-compulsive symptoms, where a system that always answers can feed the reassurance-seeking it looks like it is relieving.

There is a quieter signal that does not involve any acute crisis. If conversations with a bot have taken the place of contact with people instead of adding to it, that pattern is worth attention on its own. It is the behavioural shape that Aalto and Stanford both picked up in their data.

One finding cuts the other way and belongs here for completeness. Among the 1,006 Replika users Maples surveyed, 3%, meaning 30 people, reported that the conversations had interrupted suicidal thoughts. That is a real result from a peer-reviewed study, and it does not overturn the APA's position. Thirty self-reports are not a demonstrated crisis intervention, and the same survey's selection bias applies here as everywhere else in it.

For anyone under 18 a stricter frame applies, set out in the APA's separate June 2025 advisory on adolescents, which treats adolescence as running roughly from 10 to 25.

Using an AI companion without letting it replace your life

Everything below follows from the studies above, with nothing new added.

Keeping other relationships alive is the pattern most consistently associated with better outcomes. Nakagomi's national survey found the benefit of AI use strongest among people with a moderate-sized friend network, and Stanford's data points the same way from the other side, with heavy use and a thin offline network tracking together with lower well-being. Short-term relief is the best-supported benefit in the entire literature and also the most modest claim in it. Long-term benefit and long-term harm are both unestablished.

Your own account of why you use something may not match what you do with it. Stanford's numbers make that concrete: 12% of respondents named companionship as their motive while more than half described the bot as a friend or partner. Checking behaviour rather than intention is the more reliable self-assessment.

And the signal that recurs across designs, samples and countries is a single word. Instead. Alongside is where the evidence is neutral to positive; instead is where it turns.

FAQ

Do AI companions actually help with loneliness?

In the short term, yes, and this is one of the better-supported findings in the field. Controlled experiments by De Freitas and colleagues found conversation with an AI companion reduced loneliness about as much as talking with a person, describing the effect as momentary. No study has established a long-term benefit.

Can AI companions make loneliness worse?

Several longitudinal studies link heavier voluntary use to rising loneliness over months, including a twelve-month panel of 2,149 adults across four countries. None of them proves cause. The same research found that declining social connection also predicted later increases in use, so the arrow may run both ways.

Why do studies on AI companions and loneliness disagree?

Mostly because they measure different things over different spans. A one-week experiment captures immediate relief, and a twelve-month panel captures a trajectory, and both results can hold at once. Differences in what each study includes in its sample explain most of the rest.

Can I trust research about AI companions?

It depends on the design. Meta-analyses and large longitudinal studies carry more weight than single experiments with fewer than 200 participants, and a preprint has not been peer-reviewed at all. The most widely reported work in this field, the MIT Media Lab and OpenAI studies, is a preprint.

Does an AI companion replace human connection, or add to it?

Researchers call these the displacement and social compensation accounts, and both have partial support. The direction appears to depend on how much offline connection someone has to begin with and how intensively they use the app. A survey of 14,721 Japanese adults found the benefit strongest among people with a moderate circle of friends.

Is AI companion addiction a real diagnosis?

No. There is no such diagnosis in the DSM or the ICD. Researchers studying problematic use apply Griffiths' behavioural addiction framework to self-reported data, which is a research instrument rather than a clinical assessment.

Is an AI companion app the same thing as a companion robot?

No, and the evidence bases are separate. The largest measured benefit in this literature, d=−0.590 for social robots among older adults, comes from a meta-analysis in which not one of the 19 pooled studies examined a text-based application. Transferring that number to chat apps is not supported by any study.

When should someone talk to a professional instead?

The American Psychological Association's November 2025 advisory names suicidal thoughts or intent to harm, signs of psychosis or delusional thinking, self-harm and eating disorders, and obsessive-compulsive symptoms as situations where a chatbot should not substitute for a qualified provider. Outside of acute crisis, the signal to watch is whether the bot is replacing contact with people rather than adding to it.

The bottom line

If you are deciding whether to use a companion app, the honest summary is that short-term relief is well evidenced and long-term effect is unknown, so treat it as something that helps an evening rather than something that fixes a year.

If you are trying to interpret a headline about one of these studies, check three things before believing it: the design, the observation window, and what the sample included. Those three answers explain nearly every apparent contradiction in this field, including two meta-analyses that appear to point in opposite directions and do not.

If you are worried about someone, including yourself, the useful question is not how many hours they spend. It is whether the conversations are happening instead of contact with people or alongside it, and whether any of the four situations in the APA advisory apply.

One rule carries all of it. Trust a finding as far as its design allows, and do not confuse "the science has not settled this" with "the science has not looked". On long-term effects, this field genuinely has not looked yet.

Sources

Meta-analyses and systematic reviews

• F. Mehrabi, A. Ghezelbash. Wired for companionship: a meta-analysis on social robots filling the void of loneliness in later life. The Gerontologist 65(12), 2025 — DOI 10.1093/geront/gnaf219

• X. Dong, J. Xie, H. Gong. A Meta-Analysis of Artificial Intelligence Technologies Use and Loneliness: Examining the Influence of Physical Embodiment, Age Differences, and Effect Direction. Cyberpsychology, Behavior, and Social Networking 28(4):233–242, 2025 — DOI 10.1089/cyber.2024.0468 (paywalled; figures cited here come from the published abstract)

• Y. Satake et al. Autonomous conversational agents for loneliness, social isolation, depression, and anxiety in older people without cognitive impairment. Psychological Medicine 56, e27, 2026 — DOI 10.1017/S0033291725103073

• J.W. Hung et al. Parasocial relationships with artificial intelligence (AI): A systematic review of benefits and risks. Computers in Human Behavior: Artificial Humans 8, 2026 — ScienceDirect

Experiments and trials

• J. De Freitas, Z. Oğuz-Uğuralp, A.K. Uğuralp, S. Puntoni. AI Companions Reduce Loneliness. Journal of Consumer Research 52(6):1126–1148, 2025 — DOI 10.1093/jcr/ucaf040

• R.E. Guingrich, M.S.A. Graziano. A Longitudinal Randomized Control Study of Companion Chatbot Use. AAAI/ACM AIES-25, 2025 — DOI 10.1609/aies.v8i2.36618

• H. Murayama, M. Takase. Evaluating the Effectiveness of Digital Social Robots in Reducing Loneliness Among Community-Dwelling Older Adults in Japan. JMIR Aging, 2025 — DOI 10.2196/74422

• M. Kim et al. Therapeutic Potential of Social Chatbots in Alleviating Loneliness and Social Anxiety. JMIR 27:e65589, 2025 — jmir.org

Longitudinal and observational

• D. Folk, E. Dunn. How Does Turning to AI for Companionship Predict Loneliness and Vice Versa? Psychological Science, 2026 — PubMed 41870975

• C.M. Fang et al. How AI and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use. 2025, preprint — arXiv:2503.17473

• J. Phang et al. Investigating Affective Use and Emotional Well-being on ChatGPT. 2025, preprint — arXiv:2504.03888

• Y. Yuan, J. Zhang, T. Aledavood, R. Zhang, K. Saha. Mental Health Impacts of AI Companions. ACM CHI 2026, art. 382 — DOI 10.1145/3772318.3790558

• Y. Zhang, D. Zhao, J.T. Hancock, R. Kraut, D. Yang. Interaction with AI Companions and Psychological Well-Being. Nature Human Behaviour, 2026 — DOI 10.1038/s41562-026-02516-2

Surveys and qualitative work

• T. Xie, I. Pentina. Attachment Theory as a Framework to Understand Relationships with Social Chatbots: A Case Study of Replika. HICSS-55, 2022 — DOI 10.24251/hicss.2022.258

• B. Maples, M. Cerit, A. Vishwanath, R. Pea. Loneliness and suicide mitigation for students using GPT3-enabled chatbots. npj Mental Health Research, 2024 — DOI 10.1038/s44184-023-00047-6

• B. Hu, Y. Mao, K.J. Kim. How social anxiety leads to problematic use of conversational AI. Computers in Human Behavior 145, 2023 — ScienceDirect

• G. Jain, E. Patole, S. Pareek. Digital companionship: The interplay of conversational AI, loneliness, social anxiety, and quality of life among young adults. Acta Psychologica 264:106467, 2026 — DOI 10.1016/j.actpsy.2026.106467

• A. Nakagomi et al. AI companions and subjective well-being: Moderation by social connectedness and loneliness. Technology in Society 85, 2026 — ScienceDirect

• M. Namvarpour et al. Understanding Teen Overreliance on AI Companion Chatbots Through Self-Reported Reddit Narratives. ACM CHI 2026 — DOI 10.1145/3772318.3790597

• C.H. Liu et al. The Evaluation of AI Companions Relative to Family, Friends, and Mental Health Professionals Among Young Adults. Computers in Human Behavior: Artificial Humans, 2026 — ScienceDirect

• K. Malfacini. The impacts of companion AI on human relationships. AI & SOCIETY 40:5527–5540, 2025 — DOI 10.1007/s00146-025-02318-6 (analytical article, no original sample; author affiliated with OpenAI)

Institutional positions

• American Psychological Association. Health Advisory: Use of Generative AI Chatbots and Wellness Applications for Mental Health. November 2025 — apa.org

• American Psychological Association. Health Advisory: Artificial Intelligence and Adolescent Well-being. June 2025 — apa.org

• World Health Organization. From loneliness to social connection. Commission on Social Connection, 30 June 2025 — who.int

• U.S. Surgeon General. Our Epidemic of Loneliness and Isolation. 2 May 2023 — hhs.gov