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Engagement and Disclosures in LLM-Powered Cognitive Behavioral Therapy Exercises: A Factorial Design Comparing the Influence of a Robot vs. Chatbot Over Time

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A physical robot, but not a chatbot, increased engagement and intimate disclosure over two weeks of daily CBT exercises.

desk verdict Real longitudinal data, but the 'embodiment' claim is confounded by speech; the study deserves review, not blind acceptance. read the letter →

arxiv 2506.17831 v1 pith:ULC756SV submitted 2025-06-21 cs.RO

Mina Kian , Mingyu Zong , Katrin Fischer , Anna-Maria Velentza , Abhyuday Singh , Kaleen Shrestha , Pau Sang , Shriya Upadhyay
show 5 more authors
Wallace Browning Misha Arif Faruki Sébastien M. R. Arnold Bhaskar Krishnamachari Maja Matarić
This is my paper · ORCID
classification cs.RO
keywords LLMsociallyassistiverobotschatbotcognitivebehavioraltherapyself-disclosureintimacyengagementembodiment
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

The paper claims that the physical form of an AI therapy agent changes how people open up over time. In a two-week study of daily cognitive behavioral therapy exercises, participants speaking with an embodied robot increased their active engagement and their disclosure of opinions, judgments, and emotions from the first day to the last, while participants using a text chatbot decreased on both measures. The statistically significant interaction between embodiment and time is the paper's central evidence that physical presence matters for sustaining therapeutic self-disclosure, not just for initial reactions. If the finding holds, it points toward socially assistive robots, rather than chatbots, as the more promising LLM-based format for at-home mental health support.

What carries the argument

The argument runs on a two-by-two factorial mixed ANOVA with embodiment (between subjects) and time (first vs last day, within subjects) as factors, applied to transcripts hand-annotated by four coders. The annotation scheme, based on Morton's intimacy framework, separates descriptive intimacy (facts about oneself) from evaluative intimacy (opinions, judgments, emotions), and codes engagement as active or passive; intercoder reliability was substantial (Cohen's kappa = 0.603). The embodied condition used the handcrafted Blossom robot with AWS Polly speech and synchronized head motion, while the chatbot condition presented the same LLM-generated CBT exercises in a web application without a physical body or spoken voice. This setup is what lets the interaction term in the ANOVA carry the claim that embodiment changes how disclosure evolves over time.

What would settle it

Run the same two-week protocol with four conditions: robot with voice, robot with text, chatbot with voice, and chatbot with text; if voice rather than physical presence drives the temporal effect, the robot-with-text condition should show the chatbot-style decline and the chatbot-with-voice should show the robot-style rise.

Watch

Extended reading notes

Core claim

In the paper's own terms, the discovery is an interaction: over a two-week daily CBT program, the embodied robot condition improved while the disembodied chatbot condition deteriorated. For evaluative intimacy, the robot group's mean high-intimacy disclosure percentage rose from 0.27 to 0.42, while the chatbot group fell from 0.20 to 0.12, yielding F(1,24)=7.30, p=0.01, partial eta-squared 0.09. For active engagement, the robot group rose from 0.84 to 0.90 and the chatbot group fell from 0.79 to 0.69, yielding F(1,24)=5.14, p=0.03, partial eta-squared 0.03. The main effect of embodiment was also significant for evaluative intimacy (robot 0.35 vs chatbot 0.16, p=0.006). The authors interpret these results as support for the claim that embodiment of an LLM-powered agent is critical for encouraging engagement and intimate disclosure in longitudinal therapeutic exercises.

Load-bearing premise

The robot condition differs from the chatbot condition in both physical presence and spoken voice, so the paper's credited embodiment effect may actually be an effect of speech output, and the groups' day-one intimacy scores are not shown to be statistically equivalent.

Editorial extensions

If this is right

  • Developers of LLM-based therapeutic chatbots should consider adding physical embodiment if the goal is sustaining engagement over repeated sessions.
  • Longitudinal chatbot studies may show declining engagement and disclosure, matching the paper's chatbot arm, so embodiment may counteract that decline.
  • Evaluative intimacy, not descriptive intimacy, is the measure that responded to embodiment, suggesting emotional disclosure is the sensitive outcome for embodied agents.
  • At-home CBT homework supported by a robot could be a viable complement to human therapy, consistent with the paper's proposal.
  • Eight CBT sessions have been shown sufficient for outcomes; the two-week divergence observed here suggests embodiment may help maintain homework participation through that window.

Reading between the lines

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

  • The design confounds physical embodiment with voice output: only the robot spoke aloud. A follow-up with robot-plus-text and chatbot-plus-voice arms is needed to isolate whether the temporal effect comes from the body or the voice.
  • Because only the first and last days were analyzed, the shape of the trajectory is unknown; daily or session-level modeling could show whether the divergence is steady or driven by a single point.
  • With partial eta-squared values of 0.09 and 0.03, the effects are small in variance-explained terms, and the clinical significance for depression or anxiety outcomes is not established.
  • The sample is 26 university students with PHQ-9 below the depression threshold, so generalizing to clinical populations or older adults remains an open question.
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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. The paper reports a two-week deployment study with 26 university students who completed daily CBT exercises with either an LLM-powered Blossom robot (with AWS Polly speech and synchronized head movements) or a disembodied chatbot accessed via a web application. Transcripts were manually annotated for descriptive intimacy, evaluative intimacy, and engagement. Two-way mixed ANOVAs on first-day versus last-day sessions found significant time-by-condition interactions for evaluative intimacy (F(1,24)=7.30, p=.01) and engagement (F(1,24)=5.14, p=.03), with sample means suggesting increases in the robot condition and decreases in the chatbot condition. The authors conclude that physical embodiment drives greater engagement and intimate disclosure over time.

Significance. If the causal claim about physical embodiment were supportable, this would be a useful longitudinal contribution to HRI and digital mental health: it is one of the few comparisons of a physically present SAR versus a disembodied chatbot for daily CBT exercises, conducted in participants' residences over two weeks. Strengths include manual annotation with a published intimacy scheme, reported inter-coder reliability, and transparent reporting of ANOVA statistics. The core interaction findings are plausible and worth reporting, but the interpretation requires substantial revision because of the embodiment/speech confound and the absence of within-condition simple-effects tests.

major comments (4)
  1. [Section III-A3 and Section IV-A] The robot condition confounds physical embodiment with speech output: Section III-A3 states that the Blossom robot verbalized the LLM-generated messages with the AWS Polly 'Joanna' voice and made synchronized head movements, while the chatbot condition is described only as a web application and the paper does not state whether it presented text, speech, or an animated character. The observed interactions for evaluative intimacy and engagement therefore cannot be attributed to physical embodiment per se; they may reflect speech, multimodal presentation, or other social cues. The causal language in the Abstract, Section V, and Conclusion ('embodiment is critical', 'the SAR's physical embodiment') goes beyond what this design can establish. Please reframe the manipulation as a physically present speaking robot versus a text-based chatbot, or provide evidence that speech was controlled across conditions.
  2. [Abstract and Sections IV-A and IV-C] The directional claims that engagement and intimacy 'increased over time in the physical robot condition, while both measures decreased in the chatbot condition' are not directly supported by the reported analyses because no simple-effects tests within each condition are reported. A significant interaction (F(1,24)=7.30, p=.01 for evaluative intimacy; F(1,24)=5.14, p=.03 for engagement) establishes only that the time trends differ between conditions, not that either trend is significantly different from zero. Please report within-condition paired comparisons between first and last day, with means, standard deviations, test statistics, and effect sizes, and adjust the abstract and conclusions to match those results.
  3. [Section V, H2b] H2b ('CBT exercises will affect evaluative intimacy outcomes more than descriptive intimacy outcomes') is declared supported, but the paper never performs a statistical comparison between the two dependent variables. Separate ANOVAs on evaluative and descriptive intimacy cannot establish that the two outcomes differ in magnitude or trajectory; a proper test would require a within-subjects comparison of the two intimacy measures in a single model or an explicit test of the difference between their effect sizes. If such a test is not available, H2b should be described as an informal observation rather than a supported hypothesis.
  4. [Section IV-A and IV-C] The two conditions show day-1 baseline differences in evaluative intimacy (robot M=0.27 vs chatbot M=0.20) and engagement (robot M=0.84 vs chatbot M=0.79), but no baseline equivalence test is reported. With group sizes of 14 and 12, chance imbalance or regression to the mean could contribute to the observed interaction. Please report a test of day-1 differences between conditions and, if possible, a sensitivity analysis based on change scores or baseline-adjusted models.
minor comments (4)
  1. [Section III-B2] The inter-coder reliability section reports average percent agreement and an average Cohen's kappa of 0.603, described as 'substantial'; please clarify whether this is Cohen's kappa or another variant, how the average is computed across annotator pairs, and whether kappa was computed on a per-item or per-transcript basis.
  2. [Section IV-A and IV-C] The 'effect size' values (0.21, 0.09, 0.03) are reported without stating that they are partial eta-squared, which is the standard measure associated with these F-tests; please label them explicitly.
  3. [Section III-A1 and Section VI] The study is described as a 'two-week' study in the Abstract and as a '15-day' study in the Conclusion; please harmonize these descriptions and clarify whether 'last day' refers to a fixed study day or to each participant's final completed session, especially since exercises were optional after day 8.
  4. [Section IV] Three separate ANOVAs are presented without any correction for multiple comparisons; given the small sample and secondary nature of descriptive intimacy and engagement analyses, please acknowledge this or report adjusted p-values.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the engagement and intimacy claims rest on independently annotated transcripts and standard factorial ANOVA, not on fitted parameters or self-referential definitions.

full rationale

The paper's central claim is empirical: it reports statistically significant condition-by-time interactions for evaluative intimacy (F(1,24)=7.30, p=0.01) and active engagement (F(1,24)=5.14, p=0.03) from annotated participant transcripts. The outcome variables are coded by trained annotators using published content-analysis frameworks (Morton 1978 for intimacy; Nguyen et al. 2018 for engagement), with reported inter-coder reliability, and the statistical model is a standard 2x2 mixed ANOVA. No parameter is fitted to the outcome data and then renamed as a prediction; no equation defines the outcome in terms of the independent variable; and the cited prior work, including prior papers by overlapping authors, is used only as related work and motivation, not as the evidence establishing the interaction effects. The strongest concern raised by the skeptic is a manipulation confound: the robot condition included AWS Polly speech and head motion in addition to physical embodiment, while the chatbot condition described only a web application. That is a genuine threat to the construct-validity of the embodiment interpretation, but it is a design/confound problem, not a circularity problem: the statistical results would be equally non-circular even if the causal attribution to embodiment were unsupported. There is also no self-citation chain that forces the conclusion, and the reported decreases in the chatbot condition come from the same independently coded data rather than from the assumptions. Therefore, no circular step can be quoted or exhibited, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no free parameters or invented entities. Its load-bearing assumptions are the validity of the annotation scheme, the equivalence of the conditions apart from embodiment, the representativeness of first-vs-last-day as a time effect, and baseline comparability. These are domain assumptions, not fitted values.

assumptions (4)
  • domain assumption Annotator labels (active engagement, high evaluative/descriptive intimacy) are valid and reliable measures of the participant's true engagement and disclosure intimacy.
    The DVs are computed from human annotations; inter-coder kappa is 0.603, which is moderate. The analysis assumes these labels are good enough to support the conclusions.
  • domain assumption The two conditions differ only in embodiment, with all other presentation factors equivalent.
    The robot condition uses a spoken voice (AWS Polly) and physical robot, while the chatbot's interface is not described; the paper attributes effects to embodiment without establishing modality equivalence.
  • domain assumption First-day and last-day sessions adequately represent the time trajectory for every participant, regardless of how many sessions each participant completed.
    The mixed ANOVA uses a two-level time factor (first vs last), ignoring the number of sessions, which ranged across participants (compulsory days 1-8, optional 9-15).
  • domain assumption There is no systematic baseline difference between conditions; random assignment or its absence is not reported.
    First-day evaluative intimacy differs (robot 0.27, chatbot 0.20) and no baseline equivalence test is reported.

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Cite this review

Pith. "Pith review of Engagement and Disclosures in LLM-Powered Cognitive Behavioral Therapy Exercises: A Factorial Design Comparing the Influence of a Robot vs. Chatbot Over Time." pith.science (2026). https://pith.science/paper/ULC756SV

@misc{pith2026250617831,
  author       = {Pith},
  title        = {Pith review of: Engagement and Disclosures in LLM-Powered Cognitive Behavioral Therapy Exercises: A Factorial Design Comparing the Influence of a Robot vs. Chatbot Over Time},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ULC756SV}},
  note         = {Machine review of arXiv:2506.17831}
}
read the original abstract

Many researchers are working to address the worldwide mental health crisis by developing therapeutic technologies that increase the accessibility of care, including leveraging large language model (LLM) capabilities in chatbots and socially assistive robots (SARs) used for therapeutic applications. Yet, the effects of these technologies over time remain unexplored. In this study, we use a factorial design to assess the impact of embodiment and time spent engaging in therapeutic exercises on participant disclosures. We assessed transcripts gathered from a two-week study in which 26 university student participants completed daily interactive Cognitive Behavioral Therapy (CBT) exercises in their residences using either an LLM-powered SAR or a disembodied chatbot. We evaluated the levels of active engagement and high intimacy of their disclosures (opinions, judgments, and emotions) during each session and over time. Our findings show significant interactions between time and embodiment for both outcome measures: participant engagement and intimacy increased over time in the physical robot condition, while both measures decreased in the chatbot condition.

Figures

Figures reproduced from arXiv: 2506.17831 by the authors.

Figure 1
Figure 1. Factorial ANOVA. A two-way mixed design assessing [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Percentage of High Evaluative Intimacy Disclosures [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. V. DISCUSSION Time plays an important role in therapy; improvements are rarely immediate and meaningful change does not happen overnight, as many decades of human-led therapy have shown [41]. Our study investigated the effect of time and embodiment on LLM-led therapeutic exercises. We compared the ability of an embodied SAR and a disembodied chatbot to deliver CBT exercises over the course of two weeks. Our findings… view at source ↗

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.