AI emotional support is better only when chosen, but shifts preferences even when it is not

Amit Goldenberg; Cathy Mengying Fang; Guy LabanPattie Maes; Yaoxi Shi

REVIEW

AI emotional support is better only when chosen, but shifts preferences even when it is not

Yaoxi Shi, Cathy Mengying Fang, Guy LabanPattie Maes, Amit Goldenberg

Artificial Intelligence · Submitted 24 Aug 2026 · 43 references

Pith's verdict: A genuinely new moderation result with a real length-imbalance confound; worth refereeing but not accepting as-is.

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arXiv ID2608.23196 (version 1)

Pith IDIQ42VGB4, Pith's permanent record of this paper and its review

CitationAmit Goldenberg, Cathy Mengying Fang, Guy LabanPattie Maes, Yaoxi Shi. "AI emotional support is better only when chosen, but shifts preferences even when it is not." arXiv:2608.23196 (2026). https://pith.science/paper/IQ42VGB4

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Editor's note

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Bottom line: this is one of the few papers that crosses choice with random assignment for AI vs human emotional support, and it finds that AI's perceived superiority is confined to people who chose AI. That is a real contribution. The 28-day field study with OpenAI adds something the lab studies can't: topic-dependent preference drift toward AI for personal conversations. The path-dependence result—assigned partner moves future choice even when incongruent with initial preference—is also credible and consistent across two models and 1,951 lab participants plus 981 longitudinally. Preregistrations, data, code, and transparent handling of the Study 2 randomization bug all count in its favor. The LLM-as-judge check showing the AI's empathy content did not differ by congruence is a nice touch.

The soft spots, in proportion. The response-length imbalance is real: AI messages averaged 56.6 words/turn, human listeners 14.7. The stress-test note worries this undermines the headline. It does not outright kill it, because the moderation pattern is not what a simple 'longer is better' confound predicts: people who chose human but got AI received the same long messages and did not rate them better than human support. Still, the comparison 'AI vs human' is simultaneously 'rich vs terse,' so the claim that the effect is specifically about source is underevidenced. A length-matched or length-controlled condition would settle it. The abstract also overstates Study 4: there is no non-AI control group, so the overall pre-post shift from 25.4% to 31.9% could include repeated-measurement or demand effects. The within-study contrast between personal/open-ended and non-personal conditions is more persuasive and is what should be featured. Study 2's post-hoc correction after a randomization bug is disclosed and the fix was random, so I'd call it minor, but it does slightly tarnish the preregistration story.

Who this is for: anyone working on AI empathy, human-AI interaction, or choice effects in support seeking. It deserves a serious referee. I'd send it out with a request for a matched-length control or a clear acknowledgment that the source effect is confounded with message richness, plus a no-control caveat in Study 4. Not a desk reject.

Referee report

Significance

If the results hold, the paper makes a meaningful contribution by identifying choice congruence as a moderator of the previously reported AI superiority in empathic support and by documenting path dependence in support-source preference. The authors should be credited for preregistering all studies, using large samples, reporting combined and per-study analyses with mixed-effects models including random study intercepts, and making data and code available on OSF. The replication of Studies 2 and 3 with two different LLMs (GPT-4o and Claude Opus 4.6) and the incorporation of a longitudinal field experiment in collaboration with OpenAI are substantial strengths. The core claims are, however, threatened by a confound in the human-AI comparison that is reported in the manuscript but not addressed analytically.

Major objections (3)

[Methods (conversation descriptives) and Results (Experienced Quality of the Conversation, Fig. 2)] The comparison between AI and human support is confounded by a large difference in response length. The Methods report that human listeners produced an average of 14.67 words per turn (SD = 5.78), while the AI chatbot produced 56.63 words per turn (SD = 14.85), almost four times as much text, despite both receiving instructions to offer supportive, empathetic responses. The experienced-quality analyses (e.g., the comparisons underlying Fig. 2) attribute differences to the source of support, but the source manipulation simultaneously manipulates message length and elaborateness. A participant who chose AI and then received long, polished messages may be rating quantity and perceived effort; a participant assigned to a human listener receiving terse replies may be rating brevity. This confound affects not only the moderation claim (AI superior only among AI choosers) but also the path-dependence claim, because future preference for AI could reflect a preference for the richer messages rather than for AI as a source. The manuscript reports the discrepancy but does not treat it as a confound, does not include response length as a covariate, and does not provide a length-matched comparison. The authors should either (a) reanalyze the data controlling for listener words per turn or using a matched subsample, (b) add a follow-up experiment with length-matched human and AI responses, or (c) explicitly reframe the conclusions as applying to AI as normally configured versus typical human listeners, rather than to source independent of content richness.
[Methods (Participants, Study 2)] The handling of the Study 2 randomization error introduces a potential selection bias. The authors describe that, due to a survey-flow error, all 50 participants in the final batch were assigned to the AI condition, and they then 'randomly discarding 34 observations from that misassigned batch so that the final dataset reflected the intended allocation.' This post-hoc discard from a batch that may differ systematically (e.g., recruitment time, cohort characteristics) from earlier batches is a deviation from the preregistered random assignment protocol. Since Study 3 provides a substantive replication with a different LLM, this issue is not fatal, but the paper should report robustness analyses that include the full misassigned batch or treat this deviation more transparently, for example by showing that results are unchanged when the discarded participants are included.
[Study 4 (Results, first subsection; Discussion)] The overall pre-post preference shifts in Study 4 (AI preference from 25.4% to 31.9%; human preference from 82.1% to 76.5%) are interpreted as effects of daily AI interaction, but the design lacks a no-AI or non-interaction control condition. The within-study contrast between the Personal/Open-ended conditions and the Non-personal condition is a useful control for time and repeated measurement, and the paper correctly emphasizes this differential pattern. Nevertheless, the summary statistics quoted in the abstract and results, and the phrase 'daily conversations shifted preferences,' could be read as causal without the appropriate benchmark. The authors should either add an explicit statement that the overall shift is only interpretable relative to the Non-personal condition, or acknowledge that the design cannot rule out demand characteristics or simple time effects for the marginal change.

Minor objections (5)

[Abstract] The abstract states 'personal topics only' in the reader's version, but the actual abstract says 'only when conversations turned personal'; the final sentence 'Emotional support choices are thus path-dependent, progressively redirecting away from human connection' is a strong inference from a one-month intervention and should be tempered with 'may' or 'can.'
[Results (Pre-Conversation Beliefs and Choice)] The sentence 'This is inconsistent with H1b, in which we hypothesized no difference' refers to a significant t-test with Cohen's d = 0.06; this is a negligible effect, and the claim of inconsistency would benefit from an equivalence test or a statement that the effect is practically zero.
[Figure 4] The y-axis label in Fig. 4 is 'proportion choosing a human for future sharing,' but panels (a) and (b) show the effect of assigned partner on future human choice; the caption should clarify that higher values indicate choosing human, especially since the text focuses on AI choice.
[Methods (Main Dependent Variables)] The post-conversation alpha for perceived judgment is 0.68, which is below the conventional 0.70 threshold; the authors should acknowledge this in the text or note that the pre and post versions were analyzed together.
[References] Ref. 37 (Shi et al., 'Stumbling Into AI Emotional Dependence') is a preprint by the same group and is cited for the claim that exposure to AI emotional support often happens incidentally; this is appropriate, but the citation list contains several unpublished preprints (e.g., refs 8, 19, 35, 38) whose peer-review status should be indicated in the reference list or text.

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The paper

The paper claims

The paper argues that AI emotional support is rated as superior to human support only when the recipient initially chose AI, and that even unchosen AI interactions shift future preference toward AI.

The reading

This paper tries to establish that the source of emotional support, human or AI, is not evaluated on its own merits but through the filter of whether it is the source the person wanted, and that the experience itself rewires future choices. Across three studies, people who had chosen AI rated AI support as superior on empathy, enjoyment, and satisfaction, while people who had wanted a human did not rate AI higher even when they received it. Yet talking with AI, chosen or not, made people more likely to choose AI next time: after one five-minute conversation, 70.1% of those assigned to AI chose AI again versus 45.5% of those assigned to a human. In a 28-day field study with daily ChatGPT use, preference for AI for personal issues rose from 25.4% to 31.9%, human preference fell from 82.1% to 76.5%, and the shift appeared only in personal or open-ended conversations. The paper concludes that emotional-support choices are path-dependent, so incidental AI exposure can progressively redirect support seeking away from humans.

What carries the argument

The engine of the design is choice-assignment congruence: participants state a preference for a human or AI partner, then are randomly assigned to either the chosen or unchosen partner, so the same conversation can be examined with and without the filter of choice. The mechanisms that carry the argument are the four belief dimensions, emotional support, nonjudgment, confidentiality, and advice quality, measured before and after the interaction, plus repeated measures of partner choice. In Study 4, the machinery is a 3x3 randomized longitudinal design crossing conversation topic (personal, non-personal, open-ended) with modality, with daily five-minute ChatGPT conversations over 28 days.

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the `AI is more empathic` effect found in prior work is conditional on choice. Choice congruence, getting the partner you initially selected, raised experienced empathy, enjoyment, satisfaction, and desire to continue, but only among participants assigned to AI; among those assigned to a human, congruence had no measurable effect. As a consequence, AI was rated superior to human support only in the cell where participants chose AI and then chatted with AI. The same conversations updated beliefs: chatting with AI raised post-conversation ratings of AI's emotional support and nonjudgment regardless of initial choice, and chatting with AI predicted choosing AI for future sharing. The longitudinal study extends this to naturalistic use: 28 days of daily ChatGPT conversations increased AI preference for personal issues from 25.4% to 31.9% and reduced human preference from 82.1% to 76.5%, with the change concentrated in conditions where conversations were personal or open-ended.

Load-bearing premise

The load-bearing premise is that the human-listener condition is a fair active control for the AI, yet human listeners averaged about 14.7 words per turn while the AI averaged 56.6, so the `AI only better when chosen` result could reflect long polished messages rather than the source itself.

Editorial extensions

If this is right

AI superiority in short empathic messages is not a general property of AI responses; it depends on the recipient having chosen AI in the first place.
A single five-minute conversation with an AI, even when the participant had wanted a human, increased the likelihood of choosing AI for a future emotional conversation.
A month of daily AI use that turns personal or open-ended shifts stated partner preference away from humans, while non-personal task use leaves preferences essentially unchanged.
Emotionally engaging behaviors, such as AI validation, expressed empathy, and users' self-disclosure and support seeking, predict the shift toward AI and away from human preference.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension of the paper's design would match human listeners' response length to the AI's, since the reported averages of roughly 15 versus 57 words per turn leave open whether the `chosen AI` advantage comes from longer, more polished messages rather than the source itself.
If the human alternative were a close friend or therapist rather than a stranger, the path-dependent shift toward AI might be weaker; that boundary is not tested in these studies.
The Study 4 preference measure is self-reported, so a natural next test is whether the observed shift in stated preference actually changes whom people turn to for support in their daily lives.

Original abstract

People increasingly face a novel decision when seeking emotional support: human or AI. In existing studies, AI's empathic messages are rated as well as or better than humans'. But these studies either assigned the support source or honored people's choice. In real life, support is often incongruent with choice, as people want one source and receive the other. Across three experiments (N = 1,951), participants chose whether to share an emotional experience with a human or an AI, then were randomly assigned to a congruent or incongruent partner. AI support was rated as superior only among those who had chosen it. Yet regardless of congruence, interacting with AI increased willingness to choose it again. In a 28-day study with OpenAI (N = 981), daily conversations shifted preferences toward AI and away from humans, but only when conversations turned personal. Emotional support choices are thus path-dependent, progressively redirecting away from human connection.

The compiled record

The paper's own sentences, arranged, kept beside the review and separate from it. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,710 input tokens.

What the abstract itself states, as probabilities: claims first 0.10 · states limitation 0.32 · human participants 0.98 · controlled comparison 0.61 · randomized assignment 0.97 · states quantitative result 0.91 · compares with prior results 0.89 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; show every role.

People increasingly face a novel decision when seeking emotional support: human or AI. In existing studies, AI's empathic messages are rated as well as or better than humans'. But these studies either assigned the support source or honored people's choice. In real life, support is often incongruent with choice, as people want one source and receive the other. Across three experiments (N = 1,951), participants chose whether to share an emotional experience with a human or an AI, then were randomly assigned to a congruent or incongruent partner. AI support was rated as superior only among those who had chosen it. Yet regardless of congruence, interacting with AI increased willingness to choose it again. In a 28-day study with OpenAI (N = 981), daily conversations shifted preferences toward AI and away from humans, but only when conversations turned personal. Emotional support choices are thus path-dependent, progressively redirecting away from human connection.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	People increasingly face a novel decision when seeking emotional support: human or AI.	background 1.00	0.06	0.02	asserted 0.99
s1	In existing studies, AI's empathic messages are rated as well as or better than humans'.	background 1.00	0.03	0.03	asserted 1.00
s2	But these studies either assigned the support source or honored people's choice.	gap 0.96	0.06	0.02	asserted 0.99
s3	In real life, support is often incongruent with choice, as people want one source and receive the other.	gap 0.72	0.08	0.05	asserted 0.94
s4	Across three experiments (N = 1,951), participants chose whether to share an emotional experience with a human or an AI, then were randomly assigned to a congruent or incongruent partner.	method 0.97	0.95	0.04	asserted 0.98
s5	AI support was rated as superior only among those who had chosen it.	result 1.00	0.92	0.28	asserted 1.00
s6	Yet regardless of congruence, interacting with AI increased willingness to choose it again.	result 1.00	0.92	0.17	asserted 1.00
s7	In a 28-day study with OpenAI (N = 981), daily conversations shifted preferences toward AI and away from humans, but only when conversations turned personal.	result 0.93	0.90	0.14	asserted 1.00
s8	Emotional support choices are thus path-dependent, progressively redirecting away from human connection.	interpretation 0.99	0.71	0.86	asserted 1.00

What kind of paper the classifier reports this is: empirical study 1.00 · evidence from observation or experiment 1.00.

What was examined, and what was not

sources read

arXiv listing abstract v1

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

not run

S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

References

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Discussion

Disagree with the review, or know something it missed? Leave a comment, highlight any passage above to comment on it, or challenge this review formally.

[1] [1]

Stade, E., Tait, Z., Campione, S. & Stirman, S. Current real-world use of large language models for mental health. https://osf.io/preprints/osf/ygx5q_v1 (2025)

View on Pith 2025

[2] [2]

McCain, M. et al. How people use Claude for support, advice, and companionship. https://www.anthropic.com/news/how-people-use-claude-for-support-advice-and- companionship (2025)

View on Pith 2025

[3] [3]

Phang, J. et al. Investigating Affective Use and Emotional Well-being on ChatGPT. Preprint at https://doi.org/10.48550/arXiv.2504.03888 (2025)

View on Pith Pith review arXiv doi:10.48550/arxiv.2504.03888 2025

[4] [4]

Chatterji, A. et al. How People Use ChatGPT. Working Paper at https://doi.org/10.3386/w34255 (2025)

View on Pith doi:10.3386/w34255 2025

[5] [5]

Hadley, C. N. & Wright, S. L. Employees Are Relying on AI for Personal Support. That’s Risky. Harvard Business Review (2026)

View on Pith 2026

[6] [6]

Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50, 179– 211 (1991)

View on Pith 1991

[7] [7]

Turkle, S. Who Do We Become When We Talk to Machines? MIT Explor. Gener. AI https://doi.org/10.21428/e4baedd9.caa10d84 (2024)

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