An empathic GPT-based chatbot to talk about mental disorders with Spanish teenagers

Alba Mar\'ia M\'armol-Romero; Arturo Montejo-R\'aez; Manuel Garc\'ia-Vega; Miguel \'Angel Garc\'ia-Cumbreras

REVIEW 4 major objections 5 minor 45 references

An empathic GPT-based chatbot to talk about mental disorders with Spanish teenagers

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper reports a Telegram chatbot with a vulnerable-teen persona that uses self-disclosure to engage Spanish adolescents in discussions about mental disorders, and finds that most users who conversed opened up emotionally.

desk verdict A useful Spanish-language feasibility pilot for a teen mental-health chatbot; its self-disclosure causal claim is the one real overreach. read the letter →

arxiv 2505.05828 v1 pith:IOO6SDO6 submitted 2025-05-09 cs.HC cs.CL

Alba María Mármol-Romero , Manuel García-Vega , Miguel Ángel García-Cumbreras , Arturo Montejo-Ráez This is my paper · ORCID

classification cs.HCcs.CL MSC 68T5097C99 PACS 07.05.Wr

keywords DialoguesystemsLargeLanguageModelMentalDisordersNaturalGenerationGPT-3Self-disclosurechatbotadolescents

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 reports on a Telegram chatbot, built for Spanish teenagers aged 12 to 18, that combines psychologist-written questions with free conversation powered by GPT-3 and automatic translation. Its central claim is that the self-disclosure technique, in which the bot presents itself as a worried teenage peer and shares its own fears before asking about the user's, creates enough trust for adolescents to talk about sensitive topics such as depression, anxiety, eating disorders, and cyberbullying. In a pilot with 44 teenagers who actually conversed, over 70 percent engaged emotionally, and every user who expressed a personal concern had first interacted empathetically with the bot. The authors conclude that such systems interest young people and could help them become aware of mental disorders, while stressing that the bot is not a substitute for professional care.

What carries the argument

The central mechanism is a two-layer dialogue engine: a controlled dialogue built from psychologist-designed triage questions and topic prompts, and an open dialogue using GPT-3, specifically the Davinci-002 model, with DeepL translating between Spanish and English. The bot's persona is itself the key instrument: it appears as a teenager named Ada, Hugo, or Big, lets the user choose the bot's gender, reveals personal worries, asks the user for advice, and reciprocates, following the disclosure layers of Social Penetration Theory. Every five user turns a specialist prompt steers the conversation back on track, and language suggesting self-harm or suicidal ideation triggers an alert to a human. This hybrid of controlled and open dialogue is what the paper credits for both conversational fluency and safety.

What would settle it

Assign teenagers at random to the self-disclosing bot versus a warm but emotionally neutral bot that asks identical questions, and count how many share personal concerns; if the neutral bot produces a similar disclosure rate, the claim that self-disclosure drives openness is refuted.

Watch

Extended reading notes

Core claim

The study's discovery is that a chatbot designed as a vulnerable, disclosing peer, rather than as a therapist or a neutral assistant, can draw Spanish teenagers into sustained and emotionally open conversations about mental health. Of the 44 users who moved past onboarding, 31 helped and advised the bot and 22 shared their own worries, and in all 22 cases empathetic engagement with the bot preceded the disclosure. The authors interpret this as evidence that self-disclosure is consistently effective, and they combine usage statistics, linguistic feature analysis, manual conversation review, and a user survey to support the view that the system was well received and potentially useful for raising awareness.

Load-bearing premise

The argument rests on the assumption that the bot's self-revelation is what causes teenagers to open up, yet the evidence only shows that in every chat the user's disclosure came after empathetic engagement with the bot, with no neutral comparison chatbot to rule out other causes.

Editorial extensions

If this is right

If the finding holds, a freely available chatbot on a familiar messaging platform can serve as a low-stigma first step for teenagers to talk about depression, anxiety, eating disorders, and related topics in Spanish.
A bot that reveals its own worries before asking about the user's can create an atmosphere in which many teenagers reciprocate with personal concerns, supporting self-disclosure as an engagement strategy rather than a purely scripted interview.
Combining a controlled, psychologist-designed dialogue with an open GPT-3 conversation keeps the chat on topic while letting users drift toward what actually worries them, such as friendship, break-ups, and school, suggesting that rigid disorder-focused scripts miss the real content.
The Spanish-to-English translation loop is workable, but colloquial speech and grammatical gender are recurring failure points, so native-Spanish models or better handling of gendered language would improve fluency and comfort.
The system's risk-alert mechanism, together with the safe framing of topics under soft names, offers a template for how generative chatbots can address highly sensitive mental-health content with teenagers without impersonating a clinician.

Reading between the lines

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

Editorial inference: the paper's 100 percent disclosure result shows temporal precedence, not causation; a randomized trial with a neutral comparison bot would be needed to prove that self-disclosure itself drives openness.
Editorial inference: because the manual coding of engagement and openness was done by the authors without a second, independent rater, replicating the 70 percent emotional-engagement figure with inter-rater reliability checks would harden the main outcome.
Editorial inference: the Spanish-English translation loop, despite its errors, suggests a reusable recipe for deploying English-centric large language models in lower-resource languages for sensitive dialogue, provided colloquialisms and gender agreement are handled.
Editorial inference: the collected corpus of 1,860 messages and 94 linguistic features could feed early-detection models, but the deliberate anonymity that separates survey responses from chat data means user-satisfaction findings cannot yet be linked to clinical or triage status.

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Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: the paper's real contribution is a feasibility result. A GPT-3-powered Telegram chatbot that role-plays a vulnerable teenage peer can get Spanish adolescents to chat about mental health topics, and most survey respondents liked it. That is worth having. The stronger statement in Section 4.3 — that self-disclosure is 'consistently effective' because 100% of users who opened up had first helped the bot — is not supported by the evidence as presented.

What is new: first deployment I know of using a self-disclosure peer persona with GPT-3 for Spanish-speaking teenagers. The architecture is described in enough detail to reimplement: controlled psychologist-authored questions mixed with open dialogue, topic selection softened, a triage of sensitivity, DeepL translation to keep GPT-3 in English, and a human alert path for risk. The recruitment and ethics process is proper, with parental consent. The manual reading of conversations gives useful qualitative insight: teens wanted to talk about daily problems, relationships, and loneliness more than the controlled disorder questions, and they tended to thank the bot. Those observations have design value.

Soft spots, in order of importance. First, the causal reading of self-disclosure. There is no control bot, no randomization, no inter-rater reliability, and the coding categories are not published. The '100%' is just a temporal precedence in 44 chats; it is compatible with the idea that longer, warmer conversations create more opportunities for both helping and disclosing. The paper would be stronger if it presented this as an observation that self-disclosure preceded openness in many cases, not as evidence the technique works. Second, the NLP correlations in Section 4.2 are exploratory and not corrected for multiple comparisons; the authors present them as suggestive, so this is a minor issue. Third, the conclusion's nod to CBT impact is speculative and should be cut or labeled as future work. The limitations section does acknowledge the pilot nature, so these are overstatements around a reasonable pilot, not fabrication.

Verdict: yes, this deserves a serious referee. A good reviewer would ask for a neutral-bot comparison or at least a re-coding by a second annotator and softer causal language. The paper is for HCI and mental-health chatbot researchers, especially those working in non-English populations. I would bring it to a reading group and would cite it as a design reference for Spanish-language chatbot work.

Referee Report

4 major / 5 minor

Summary. This paper reports a field deployment of a GPT-3-based chatbot ('Ada', 'Hugo', 'Big') on Telegram for Spanish-speaking teenagers aged 12–18, mixing psychologist-authored controlled dialogue, triage questions, and open dialogue with Spanish↔English translation. Usage statistics from 44 active users, a post-hoc anonymous survey, NLP feature correlations, and a manual reading of all 44 chats are presented. The authors conclude that the self-disclosure persona led most users to engage emotionally, that 100% of users who disclosed concerns first helped or advised the bot, and hence that such systems interest young people and can raise awareness of mental disorders.

Significance. If the descriptive claims hold, the study contributes a concrete, privacy-preserving deployment with psychologist involvement, a Spanish-language corpus from adolescent interactions, and a design pattern (a vulnerable peer bot with selectable gender) that could inform future mental-health chatbots for youth. The paper's strengths include a real deployment over roughly three and a half months, ethics approval with parental consent, explicit acknowledgment of some limitations in Section 4.4, and enough architectural detail to permit approximate reproduction. There are no fitted models or formal derivations whose circularity could threaten the central result; the main risk is causal overreading of observational data rather than internal inconsistency in the system design.

major comments (4)

[Section 4.3] The paper's distinctive contribution is the self-disclosure design, and the load-bearing sentence — 'the self-disclosure technique is consistently effective, as 100% of users expressing concerns have previously engaged in empathetic interactions with the chatbot' — is not supported by the evidence as reported. The observation is a temporal ordering in 44 chats that were read by the authors, with no published coding rubric, no inter-rater reliability check, and no comparison condition such as a warm bot that does not self-disclose. Longer conversations mechanically allow more opportunities both to advise the bot and to disclose concerns, so the ordering is compatible with a conversation-length confound. Please reframe this as an observational association, report a reliability analysis or blinded re-coding, and soften the corresponding claim in the abstract.
[Sections 4.3 and 5] The numerical summary is internally inconsistent. The text reports 31/44 users 'care about the bot and get involved in advising and helping it' and only 22/44 'open up and talk about their concerns'; the statement that 'more than 70% of the users engaged emotionally with the bot, sharing their concerns and worries' conflates the 70% helping figure with the 50% disclosure figure, and Section 5 repeats this as '70% emotional openness.' Because the self-disclosure claim concerns the 22 users who disclosed, please report the two rates separately and correct the Discussion text.
[Section 4.2] The NLP analysis computes Pearson correlations across 94 linguistic features using n=44 users and then interprets the top correlations (e.g., coordinating-conjunction frequency above 0.4) as evidence that 'there are differences in language between people classified as healthy and people classified as having some form of mental disorder.' With 94 features and no multiple-testing correction, top correlations of this size are expected under noise, and no confidence intervals or effect sizes are provided. Please label this analysis explicitly exploratory and use an adjustment such as false-discovery-rate control, or remove the inferential wording.
[Sections 4.4 and 6] The survey-based evaluation lacks the information needed to support the positive-attitude conclusions. The percentages 'over 50%', '66.7%', '60%', and so on are not accompanied by a response count or response rate, and the survey was limited to interviewed users whose contact details were available, creating a self-selection risk that the paper acknowledges only indirectly. The paper itself states in Section 4.4 that the survey cannot be linked to user data because of anonymity, so it cannot validate any risk-related or clinical benefit. In addition, the Conclusions' statement that the approach 'can help to leverage the impact of Cognitive-Behavioral Therapies through chatbots' is unsupported because no CBT outcome was measured in this study; it should be explicitly marked as speculation or removed.

minor comments (5)

[Section 6] The phrase 'To the best of your knowledge' should read 'To the best of our knowledge.'
[Table 1] The row for '/noTengoAlias,/noAliases' contains a stray '95' before 'Starts.'
[Figure 6] The Pearson correlation matrix is likely illegible at print resolution; please provide a high-resolution version with feature names and a clear legend.
[Section 4.3] '1-gramas' should be '1-grams,' and the word-cloud analysis would benefit from stating the stop-word list used.
[Section 5] 'With this experimental studio' should be 'With this experimental study.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central self-disclosure claim is an empirical coded observation, not a derived or fitted prediction, and the only self-citation is a non-load-bearing NLP toolkit.

full rationale

No circularity found. The paper makes no formal derivation claim that reduces an equation or fitted parameter to its own input. Its main assertion is the observational result in Section 4.3: “the self-disclosure technique is consistently effective, as 100% of users expressing concerns have previously engaged in empathetic interactions with the chatbot.” This is a coded temporal pattern from conversation logs, not a quantity fitted from data and then renamed as a prediction. The coding categories are distinct enough that the paper reports 9 chats where users helped the bot but did not open up, so the “100%” claim is not true by definition. The only self-citation is the TextFlow feature-extraction toolkit (ref [37]) used in Section 4.2; the engagement claim does not depend on it, so it is not load-bearing. No uniqueness theorem or ansatz is imported from the authors’ prior work, and the paper does not rename a known result as a new derivation. Methodological limitations—no control chatbot, no inter-rater reliability for chat coding, and no pre/post clinical re-evaluation—are evidentiary threats to the causal reading, but they are not circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claims rest on several domain assumptions rather than mathematical axioms. The most important is the causal interpretation of self-disclosure: the paper assumes, following Social Penetration Theory, that a bot revealing personal worries will make users reciprocate. This is a domain assumption from prior literature, and the study design cannot separate it from other factors. The manual engagement coding and translation fidelity are further unverified assumptions. No free parameters are fitted to data in a predictive sense; the listed GPT-3 settings are hand-chosen operating values. No new physical or theoretical entities are proposed; the bot personas are software artifacts.

free parameters (3)

GPT-3 sampling temperature = 0.9
Set in Section 3.2.5 for creative dialogue; hand-chosen, not fitted to user outcomes.
Maximum response tokens = 170
Set in Section 3.2.5 to constrain response length; hand-chosen.
Controlled prompt refresh interval = every 5 user messages
Set in Section 3.2.2 and 3.2.5 to limit hallucination and steer conversation; hand-chosen.

assumptions (4)

domain assumption Self-disclosure by the bot leads to reciprocal self-disclosure by the user, following Social Penetration Theory.
Adopted from prior literature in Section 2.2; the conclusion that self-disclosure is 'consistently effective' in Section 4.3 rests on this causal premise, but the design has no control condition.
domain assumption Manual qualitative coding of conversations accurately distinguishes emotional engagement.
Section 4.3 classifies chats into engaged versus non-engaged without inter-rater reliability or a coding rubric, so the 70% engagement figure depends on this assumption.
domain assumption DeepL translation preserves meaning and affect sufficiently for GPT-3 to conduct empathic dialogue in Spanish.
Section 3.2.1 relies on SP-EN and EN-SP translation; Section 4.3 documents 6 chats with translation misunderstandings, so the assumption is only partially satisfied.
ad hoc to paper Pearson correlations computed on 44 users across 94 features are interpretable without adjustment for multiple testing.
Section 4.2 reports correlations above 0.4, 0.3, and 0.2 as meaningful with no p-values or multiple-comparison correction, implying noise is negligible; this is not justified and is load-bearing for the linguistic claims.

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Cite this review

Pith. "Pith review of An empathic GPT-based chatbot to talk about mental disorders with Spanish teenagers." pith.science (2026). https://pith.science/paper/IOO6SDO6

@misc{pith2026250505828,
  author       = {Pith},
  title        = {Pith review of: An empathic GPT-based chatbot to talk about mental disorders with Spanish teenagers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IOO6SDO6}},
  note         = {Machine review of arXiv:2505.05828}
}

read the original abstract

This paper presents a chatbot-based system to engage young Spanish people in the awareness of certain mental disorders through a self-disclosure technique. The study was carried out in a population of teenagers aged between 12 and 18 years. The dialogue engine mixes closed and open conversations, so certain controlled messages are sent to focus the chat on a specific disorder, which will change over time. Once a set of trial questions is answered, the system can initiate the conversation on the disorder under the focus according to the user's sensibility to that disorder, in an attempt to establish a more empathetic communication. Then, an open conversation based on the GPT-3 language model is initiated, allowing the user to express themselves with more freedom. The results show that these systems are of interest to young people and could help them become aware of certain mental disorders.

Figures

Figures reproduced from arXiv: 2505.05828 by the authors.

**Figure 2.** Components on the dialogue system For the open dialogue, we have integrated the Generative Pre-trained Transformer (GPT-3) trained mostly on English texts, so we also used DeepL7 as one of the best Language Translation API to translate the GPT-3’s messages to Spanish from English and user’s messages to English from Spanish. While GPT-3 does offer support for Spanish prompts, during the project development phase, we… view at source ↗

**Figure 3.** The chatbot conversation flowchart. Another aspect of the chatbot is its ability to remind each user to talk to it in a personalised way. To do this, it takes into account the last interaction time-stamp and, if 23 hours have passed, it sends them a friendly 10 [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Distribution of users and messages over time. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]

**Figure 5.** Figure 5: Number of interactions according to the mental disorder and user type. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]

**Figure 6.** Figure 6: Pearson correlation matrix for each pair of linguistic features generated from [PITH_FULL_IMAGE:figures/full_fig_p018_6.png]

**Figure 7.** Figure 7: The most frequent 1-gramas for the set of users marked as indicated or healthy. [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]

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