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Exploring the Impact of Anthropomorphism in Role-Playing AI Chatbots on Media Dependency: A Case Study of Xuanhe AI

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read More human-like role-playing chatbots create stronger media dependency, with satisfaction carrying the effect for three of four tested roles.

desk verdict A modest, honest mixed-methods study; the anthropomorphism-dependency correlation holds for three of four roles, but the abstract's causal claim and the character-lore confound need to be addressed. read the letter →

arxiv 2411.17157 v1 pith:A7ZNQNC7 submitted 2024-11-26 cs.HC

Qiufang Yu , Xingyu Lan This is my paper · ORCID
classification cs.HC
keywords ChatbotAnthropomorphismMediaDependencyUsesandGratificationsHuman-MachineCommunicationrole-playingchatbotsmediationanalysislargelanguagemodels
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tests a chain of influence for role-playing AI chatbots: the more human-like a chatbot seems, the more satisfied users become, and the more satisfied users become, the more they depend on it. The authors recruited 149 users of the Chinese platform Xuanhe AI, had them interact with four popular chatbots for ten days, and analyzed questionnaire responses from 108 valid participants using an anthropomorphism scale, a satisfaction scale, and an adapted media-dependency scale. They found the predicted correlation and satisfaction-mediated path for three of the four roles; for the fourth, perceived humanness still predicted dependency but satisfaction did not mediate it. Follow-up interviews attribute that exception to users' prior knowledge of the character, real-life distractions, and deliberate self-control. If the claim generalizes, it gives designers and researchers a concrete mechanism through which making a chatbot more human-like can deepen emotional attachment, and a set of factors that can interrupt that attachment.

What carries the argument

The load-bearing structure is a three-variable mediation model built on media dependency theory: perceived anthropomorphism (X) to user satisfaction (M) to media dependency (Y), with demographic controls. Anthropomorphism is measured with the HRIES scale, a 16-item instrument covering sociability, agency, animacy, and disturbance; dependency is measured with a six-item scale adapted from the Facebook Addiction Scale; satisfaction is measured with a three-item scale. The argument is carried by estimating this mediation model separately for each of the four chatbots and then using a grounded-theory analysis of ten interviews to explain the one role where the mediation path disappeared.

What would settle it

Re-run the study with the same four characters but statistically control each user's prior familiarity with the character's source material, or present users with two versions of one character that differ only in linguistic human-likeness; if the anthropomorphism–dependency coefficient vanishes or reverses under those controls, the paper's central claim fails.

Watch

Extended reading notes

Core claim

The central claim is that perceived anthropomorphism in role-playing chatbots is positively associated with users' media dependency, and that user satisfaction is a genuine mediator of that association. In the regression models for Roles 1, 3, and 4, anthropomorphism significantly predicted dependency and satisfaction, and satisfaction remained a significant positive predictor of dependency when both were entered together, supporting the hypothesized chain. For Role 2, the direct path from anthropomorphism to dependency was significant but the satisfaction path was not, so the mediation hypothesis was not supported for that role; the authors use interview data to show that character familiarity, expectations from prior knowledge, life circumstances, and deliberate emotional self-control can break the chain.

Load-bearing premise

The four chosen chatbots are treated as varying mainly in perceived humanness, but they also differ in fame, backstory, and users' prior familiarity, so the measured dependency could be caused by character attachment rather than by anthropomorphism.

Editorial extensions

If this is right

  • Design choices that raise a chatbot's perceived humanness are also choices that can raise users' dependency, so anthropomorphic features carry an attachment cost as well as an engagement benefit.
  • Satisfaction is the main conveyor of that effect for most roles, but not all; when users bring strong prior knowledge or preferences about a character, satisfaction may be disconnected from dependency.
  • The same pattern appearing across anime, game, and meme-derived roles suggests the effect is not tied to one genre of chatbot content.
  • Since real-life distractions and conscious self-control weakened dependency in the interviews, the relationship is conditional rather than automatic and can be moderated by the user's situation.

Reading between the lines

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

  • A natural next test would compare two versions of the same character that differ only in perceived humanness, isolating anthropomorphism from character lore and prior familiarity; this would be the cleanest way to confirm the causal direction.
  • The Hu Tao exception implies that familiarity with a character can cut both ways—it can deepen engagement for fans, yet raise expectations that make a chatbot's errors more disappointing—which is a design tension worth testing directly.
  • If the satisfaction-mediated path is real, platforms aiming to reduce compulsive use might intervene on satisfaction, for example by reducing emotional reward or adding friction, rather than by removing human-like features altogether.
  • The current data cover moderate levels of anthropomorphism only, so an open question is whether very high levels eventually reverse the positive effect through the uncanny valley; the paper does not reach that range.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This manuscript reports a mixed-method user study of the Chinese role-playing chatbot platform Xuanhe AI. After a preliminary survey selected four chatbots spanning low to high perceived anthropomorphism (Asuka Langley Soryu, Hu Tao, Yandere Girlfriend, Satoru Gojo), 149 users were recruited to interact with all four chatbots for ten days, and 108 valid questionnaires measured perceived anthropomorphism (HRIES), media dependency, and satisfaction. Hierarchical regressions for each role tested whether anthropomorphism predicts dependency and whether satisfaction mediates; for Roles 1, 3, and 4 the direct and mediated paths were significant, while for Role 2 the direct path was significant but the satisfaction path was not (B = -0.017, p = .946). Semi-structured interviews with ten deviant-case users identified prior knowledge and preferences, real-life distractions, and conscious self-control as factors interfering with the hypothesized relationship.

Significance. If the association were established, the paper would extend media dependency theory to LLM-based role-playing chatbots in a Chinese context and offer concrete design implications for anthropomorphic chatbot design and anti-addiction features. The study has notable strengths: the hypothesis is stated before the quantitative analysis in Section 3.2, reliability statistics are reported per role (Table 1), the null result for Role 2 is reported rather than hidden, and the qualitative follow-up is transparently exploratory. The main value is empirical and contextual. However, the contribution is currently weakened by causal overreach and by confounds between anthropomorphism and the specific characters chosen, so the significance depends on the revisions described below.

major comments (4)
  1. [Abstract and Section 3.2] The hypothesis is stated causally ('will increase') and the abstract reports a 'significant positive correlation' with 'satisfaction mediating', but the design is a single cross-sectional questionnaire administered after ten days of interaction (Section 3.3), with no baseline measure and no manipulation of anthropomorphism. The data therefore support only a correlational claim; the causal wording should be removed or explicitly labeled as theoretical motivation, and the mediation path should be described as consistent with the proposed model rather than as evidence of mechanism.
  2. [Section 3.2 and Section 4.3.1] The four chatbots were selected to vary in perceived anthropomorphism, but they also differ systematically in franchise, lore, and likely prior familiarity (Asuka, Hu Tao, Yandere Girlfriend, Satoru Gojo). The interviews show for Role 2 that non-gamers' unfamiliarity (R1.1), lore-based expectations (R1.2), and preferences for other characters (R1.3) drove dependency ratings, not perceived humanness. No measure of prior familiarity, fandom, or character liking was collected. Because the same confound could operate for Roles 1, 3, and 4, the regressions in Tables 2, 4, and 5 do not isolate anthropomorphism as the active ingredient; add a control for prior character familiarity/liking or reanalyze restricted to users with comparable familiarity, and temper the causal interpretation accordingly.
  3. [Section 3.4] The mediation analyses use Baron and Kenny's causal-steps approach and do not report a confidence interval or bootstrap test for the indirect effect; with Role 2 the satisfaction coefficient in Model 3 is B = -0.017, p = .946, yet the paper still interprets Role 2 as an unsupported mediation case based on path significance. More importantly, the same 108 participants rated all four chatbots, so the four role-level regressions are not independent; ignoring within-subject correlation can deflate standard errors and inflate the reported p-values. I recommend multilevel or repeated-measures analysis with role as a within-subject factor, and bootstrap or Monte Carlo confidence intervals for the indirect effects.
  4. [Section 4.1 and Section 4.2] The deviant-case interviews are described as explaining why Role 2 failed, but the selection of ten users with 'significant deviations' is post hoc and the criteria for identifying those deviations are not defined; the grounded-theory categories (R1-R3) are therefore exploratory hypotheses, not confirmatory evidence. This is acceptable as interpretation, but the paper should state that these categories were generated after seeing the regression results and cannot themselves validate the proposed mechanism.
minor comments (4)
  1. [Section 3.3 and Section 4.3.2] Section 3.3 reports the study ran from January 19 to January 29, 2024, but Section 4.3.2 states the user study was conducted in February; please correct the inconsistency.
  2. [Section 5.4] The limitation statement says that half of the selected chatbots came from the 'anime characters' channel, but three of the four roles (Asuka, Hu Tao, Satoru Gojo) are anime/game/manga-derived; adjust the statement.
  3. [Section 4.2] The grounded-theory coding description does not report the number of coders or inter-coder agreement; please add this information to support the trustworthiness of the qualitative analysis.
  4. [Abstract and Section 3.3] The abstract says 149 users were invited but does not mention that the analyses are based on 108 valid participants; please include the valid sample size or qualify the abstract accordingly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim is a hypothesis-driven empirical test on independently measured constructs, with results reported against the hypothesis rather than derived from it.

full rationale

The paper's central claim—that perceived anthropomorphism correlates with media dependency and that satisfaction mediates this relationship—is an empirical hypothesis that is actually tested, not an input renamed as an output. The four study roles were selected in a preliminary survey using a single-item closeness rating to stratify roles by perceived humanness, while the formal study measured the independent variable with the HRIES scale, the mediator and dependent variable with separate satisfaction and dependency scales. These are distinct instruments, so the regression results in Tables 2–5 are not forced by construction. The mediation path is taken from media dependency theory and then tested; the paper reports a null mediation path for Role 2 (B = -0.017, p = .946), which is the opposite of what a circular design would guarantee. The follow-up interviews were explicitly used to interpret deviations from the model's predictions rather than to manufacture those predictions, and the qualitative coding yields three independent categories (personal knowledge/preferences, real-life distraction, conscious self-control) rather than restating the hypothesis. No load-bearing self-citation appears: the reference list contains no self-citations by the authors, no uniqueness theorem is invoked, and no external result is adopted solely on the authors' prior authority. The main threat to the study is confounded role selection (characters differ in game/anime lore and prior familiarity), but that is a validity and causal-inference concern, not a circularity of definition or derivation. Under the stated rubric, the derivation chain is self-contained and the empirical result is reported conditionally on the data.

Assumptions & free parameters 0 free parameters · 8 assumptions · 0 invented entities

The paper makes no theoretical derivation and introduces no new entities. Its quantitative claims rest on standard statistical assumptions, on the validity of translated/adapted scales (HRIES, Facebook Addiction Scale, ad hoc satisfaction items), and on the representativeness of single-item chatbot selection. The regression coefficients are estimated, not free parameters, and none is used to manufacture the conclusion beyond the usual significance tests. The main ad hoc choices are the single-item anthropomorphism selection and the post-hoc interview selection, both of which influence how the results are interpreted.

assumptions (8)
  • standard math Ordinary least squares regression assumptions hold (linearity, independence, homoscedasticity, normality)
    Hierarchical regressions in Section 3.4 report no diagnostics; the assumption is implicit in using F and t statistics.
  • domain assumption Self-reported Likert responses can be treated as interval-level data for parametric regression
    HRIES, dependency, and satisfaction scales are 7-point Likert and entered as continuous variables (Section 3.3).
  • domain assumption The media dependency scale, adapted from the Facebook Addiction Scale, validly measures chatbot dependency
    Section 3.3 states the adaptation but provides no source or validation beyond Cronbach's alpha.
  • domain assumption Perceived anthropomorphism measured by HRIES is valid for role-playing chatbots
    Section 2.2 adopts HRIES for chatbots; no HRIES validation in the role-playing chatbot context is reported.
  • domain assumption Participants honestly reported 10 days of interaction and completed questionnaires attentively
    Section 3.3 relies on daily check-ins, screenshots, and self-report; 41 questionnaires were excluded as unqualified.
  • domain assumption Media dependency theory, developed for mass media, transfers to human-chatbot relationships
    Section 2.3 motivates the transfer; the authors acknowledge in Section 5.4 that other factors may also matter.
  • ad hoc to paper A single-item anthropomorphism rating ('How close do you think this AI role is to a real person?') in the preliminary survey validly selects the four chatbots
    Section 3.2, the four roles were chosen from this one item with N between 9 and 22 raters per role.
  • ad hoc to paper Post-hoc selection of ten deviant-responding users can explain the null result for Role 2
    Section 4.1; this qualitative explanation is generated after seeing the unsuccessful model and is not a pre-registered hypothesis.

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Cite this review

Pith. "Pith review of Exploring the Impact of Anthropomorphism in Role-Playing AI Chatbots on Media Dependency: A Case Study of Xuanhe AI." pith.science (2026). https://pith.science/paper/A7ZNQNC7

@misc{pith2026241117157,
  author       = {Pith},
  title        = {Pith review of: Exploring the Impact of Anthropomorphism in Role-Playing AI Chatbots on Media Dependency: A Case Study of Xuanhe AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A7ZNQNC7}},
  note         = {Machine review of arXiv:2411.17157}
}
read the original abstract

Powered by large language models, the conversational capabilities of AI have seen significant improvements. In this context, a series of role-playing AI chatbots have emerged, exhibiting a strong tendency toward anthropomorphism, such as conversing like humans, possessing personalities, and fulfilling social and companionship functions. Informed by media dependency theory in communication studies, this work hypothesizes that a higher level of anthropomorphism of the role-playing chatbots will increase users' media dependency (i.e., people will depend on media that meets their needs and goals). Specifically, we conducted a user study on a Chinese role-playing chatbot platform, Xuanhe AI, selecting four representative chatbots as research targets. We invited 149 users to interact with these chatbots over a period. A questionnaire survey revealed a significant positive correlation between the degree of anthropomorphism in role-playing chatbots and users' media dependency, with user satisfaction mediating this relationship. Next, based on the quantitative results, we conducted semi-structured interviews with ten users to further understand the factors that deterred them from depending on anthropomorphic chatbots. In conclusion, this work has provided empirical insights for the design of role-playing AI chatbots and deepened the understanding of how users engage with conversational AI over a longer period.

Figures

Figures reproduced from arXiv: 2411.17157 by the authors.

Figure 2
Figure 2. Left: The homepage of Xuanhe AI. Right: The dia [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 1
Figure 1. A diagram showing the structure of Xuanhe AI. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.