-->PONE-D-26-04125-->-->Using Natural Language Processing to Improve Communication Tools for Children with Autism Spectrum Disorders-->-->PLOS One

Dear Dr. ALGAHTANI,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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The manuscript addresses a relevant and timely topic; however, it currently falls short of PLOS ONE’s publication criteria in terms of methodological rigor, transparency, and reproducibility.

Changes required for acceptance:

• The statistical analysis needs to be strengthened with appropriate reporting (including test statistics, confidence intervals, and correction for multiple comparisons where applicable). Interpretations must be aligned with the study design, avoiding causal claims in the absence of controlled comparisons.
• The description of the NLP tool is insufficient. Clear details regarding model architecture, training data, implementation, and functioning are required to ensure transparency and allow reproducibility.
• Data availability must be clearly stated in accordance with PLOS policy. Any restrictions should be explicitly justified, and access to underlying data and materials should be clarified.
• Outcome measures require clarification, particularly if non-validated or researcher-developed instruments are used. Their limitations must be explicitly acknowledged.
• The qualitative analysis needs to meet accepted reporting standards, including description of methodology, coding process, and inclusion of representative data (e.g., participant quotes).
• The conclusions must be revised to reflect the limitations of the study design. Given the small sample size, short duration, and lack of control group, the study should be framed as preliminary or pilot work rather than providing definitive evidence of effectiveness.

Recommended improvements:

• Provide more detailed reporting of usage and engagement data over time to strengthen interpretation.
• Expand the discussion on context-specific utility and user adaptation patterns, which appear to be meaningful findings.
• Address limitations related to sample characteristics and generalizability more explicitly.
• Ensure consistency between reported results and their interpretation, particularly in follow-up findings.

==============================

Please submit your revised manuscript by Jun 21 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Ramandeep Kaur

Academic Editor

PLOS One

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Additional Editor Comments:

The manuscript addresses an important and emerging area—use of NLP-based tools to support communication in individuals with Autism Spectrum Disorder—and demonstrates promise in terms of user-centered design and potential clinical relevance. However, several critical methodological and reporting issues need to be addressed to meet publication standards.

Major revisions required (essential for acceptance):

Statistical rigor: The current statistical analysis is insufficient. There is lack of correction for multiple comparisons, absence of confidence intervals, and limited reporting of test statistics. The interpretation of findings (e.g., “dose-response”) is not justified given the study design. These issues must be corrected.

Study design limitations: The absence of a control group, small sample size (N=16), and short intervention duration significantly limit causal inference and generalizability. While these cannot be fully corrected, the manuscript must clearly position the study as a pilot/feasibility study and temper all causal claims.

Data availability and reproducibility: There is inconsistency regarding data availability across reviews. The manuscript must explicitly clarify what data, code, and materials are available, and justify any restrictions in line with journal policy.

Description of the NLP tool: The model architecture, training data, deployment process, and functioning of the suggestion mechanism are inadequately described. Without this, the study cannot be evaluated or replicated.

Outcome measures: The use of researcher-developed tools without psychometric validation weakens interpretability. Authors should either provide validation evidence or clearly acknowledge this as a limitation.

Qualitative analysis: Reporting is below accepted standards. Include methodology (coding approach), participant quotes, theme frequencies, and reliability measures.

Recommended revisions (to strengthen the manuscript):

Provide longitudinal or week-wise data trends instead of only aggregated results to better illustrate tool engagement and impact.

Expand discussion on context-dependent utility and user-driven adaptations (override rules), which are valuable findings but currently underdeveloped.

Address equity concerns, including applicability to minimally verbal individuals and non-English speakers.

Clarify discrepancies between results and interpretations (e.g., decline at follow-up being described positively).

Improve discussion of real-world applicability, especially in informal communication contexts where effectiveness was lower.

Overall, the study has potential but requires substantial revision in methodological transparency, statistical reporting, and interpretation before it can be considered technically sound.

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Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: No

Reviewer #2: Partly

Reviewer #3: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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-->3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes

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-->4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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-->5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: Thank you for the opportunity to review this manuscript. The topic is important, and the idea of using NLP-based tools to support communication in autistic individuals is timely and potentially valuable. The manuscript is easy to follow at a high level, and the mixed-methods framing is promising. However, in its current form, I have substantial concerns about methodological rigor, statistical reporting, interpretation, and presentation that limit confidence in the findings.

My main concern is that the claims are stronger than the study design can support. The paper presents the work as evidence that the NLP tool improves communication, reduces anxiety, and increases confidence, but the study appears to use a small within-subject sample of 16 autistic participants without a randomized design or a concurrent ASD control group. This makes it difficult to separate intervention effects from expectancy effects, practice effects, regression to the mean, or changes over time unrelated to the tool. The conclusions should therefore be substantially tempered.

There is also a mismatch between the framing of the paper and the sample studied. The title, abstract, and conclusions repeatedly refer to “children with autism,” but the participant age range is reported as 15–28 years. Because this includes adults, the terminology and implications need to be revised throughout.

The description of the intervention is not sufficiently detailed for technical evaluation or reproducibility. The manuscript states that the system combines sentiment analysis, intent identification, and situational simplification, but it does not explain what models were used, what data they were trained on, how outputs were generated, how accuracy was assessed, what safeguards were in place, or how the tool behaved in realistic edge cases. For a paper centered on an NLP-based system, this level of technical detail is essential.

The statistical analysis is underreported and not rigorous enough in its current form. The manuscript mentions mixed-effects linear models and paired t-tests, but the model structure, assumptions, diagnostics, effect-size calculation details, and handling of repeated measures are not adequately described. Confidence intervals are not reported. It is also unclear how Bonferroni correction was actually applied across outcomes and timepoints. Given the small sample size and the use of Likert-type scales, the authors should justify the analytic choices more carefully and report the analyses more transparently.

Outcome measurement also needs clarification. Communication clarity is said to be rated on a validated 5-point scale by two trained raters, but the scale itself is not identified clearly enough for readers to evaluate it. Several key outcomes, including anxiety and confidence, rely on self-report, which is acceptable but should be discussed more explicitly as a limitation. The manuscript should also make clearer which outcomes were primary and which were secondary.

I also noted inconsistencies and reporting issues. The study timeline is described in slightly different ways across sections, and the data-availability statements appear contradictory. In the submission information, the authors indicate that all data are fully available without restriction, yet the manuscript states that the data cannot be shared publicly and are available only upon official request. This should be corrected to align with journal policy and with the actual availability status of the data.

The manuscript would benefit from major language editing. Although the overall meaning is usually understandable, there are frequent grammatical errors, awkward constructions, and non-standard phrasing throughout. These language issues at times interfere with precision and make it harder to assess the work confidently. Careful editing by a fluent English speaker or a professional editing service is recommended before further consideration.

Reviewer #2: Reviewer Statement

ASD is outside my clinical specialization. This review addresses study design, model deployment, statistical analysis, and reproducibility using a standard clinical ML readiness framework. The study explores transformer-based assistive communication for adolescents and young adults with ASD — an area with real potential.

Summary

Models were evaluated for four foundational requirements for clinical ML readiness — Data Availability, Explainability, Interpretability, and Equity — they are not adequately addressed. Results as presented are not reproducible, and the qualitative strand falls short of standard reporting expectations. Two incidental findings of genuine value appear underdeveloped and warrant greater emphasis.

Data Availability

⁃ No datasets, model code, weights, or instrument documentation accompany the submission which provides no opportunity to reproduce results.

⁃ With N = 16 over 8 weeks, the study is underpowered, and all outcomes are reported as group-level means. At this sample size a single outlier could shift any result. The tool generated rich continuous process data — messages sent, suggestions triggered, viewed, accepted — reported only as full-period aggregates. The temporal trajectory of these metrics, broken down by week and suggestion type, would be the most informative evidence for evaluating the tool and should be included.

Explainability

This is the central concern for any ML clinical tool, without it, deployment of any ML tool in healthcare is jepordized.

⁃ ML tool deployment in clinical settings, particularly high stakes setting involving vulnerable populations depends on establishment of clinican trust in the tool. Transparency regarding all aspect of the tools functionality and development are essential and the tool should be easily explainable to clinicians.

⁃ "Transformer model" describes thousands of architectures spanning orders of magnitude in parameters and capability. Without the architecture, training corpus, fine-tuning procedure, prompt strategy, deployment method, and safety filters, reviewers cannot evaluate what was tested.

⁃ The suggestion mechanism — trigger logic, type selection, retrieval vs. generation — is also undescribed.

Interpretability

⁃ Four paired t-tests are reported without correction for multiple comparisons (family-wise error ≈ 19%), confidence intervals, control group, or blinding.

⁃ ASD outcome descriptors are outside my clinical scope, but the measures appear researcher-created with no psychometric documentation provided.

⁃ Precision drawn from novel, unvalidated instruments cannot be externally validated or disentangled from co-occurring influences during the intervention.

⁃ A usage–confidence correlation (r = 0.67) is described as "dose-response," but dose was self-selected rather than manipulated, leaving causation unclear.

⁃ The Limitations section acknowledges insufficient power for individual-differences analyses, which sits in tension with this framing.

⁃ Communication Clarity declined from 3.89 to 3.71 at follow-up but is described as "internalization"; anxiety rebounded by 24% but is described as "maintained."

⁃ The Conclusion's "this paper has shown" reads is overstated. The statistical analysis of this uncontrolled study does not provide mathematically interpretable benefits sufficient to support this causal claim.

Equity

⁃ The suggestion trigger rate rose from 74% to 82% while engagement declined 17% — the tool escalated intervention as users disengaged. For a population that often experiences autonomy challenges and sensory overwhelm, this pattern deserves explicit discussion.

⁃ The framework also trains users toward "clarity" and "appropriateness" along standards that are not defined but appear implicitly neurotypical. Without additional context, the tool could be supporting assistive assimilation rather than the bidirectional support claimed in Future Directions.

⁃ The sample excludes minimally verbal individuals and those with intellectual disability — the subpopulations most likely to need assistive technology.

Qualitative Strand

⁃ Five positively valenced themes are reported without participant quotes, counts per theme, coding methodology, inter-coder reliability, or disconfirming cases — below the standards described by Braun & Clarke (2006).

⁃ The absence of critical themes contrasts with the quantitative signal: declining engagement, 44% rejection of intent clarification suggestions, half of participants building workarounds.

Statistical Notes

⁃ t-statistics are reported for only one of four measures

⁃ SEM bars understate variability;

⁃ Figure 2 overlays different scales with undescribed normalization.

⁃ No pre-registration is noted, and the effective intervention rate (~8 of 47 messages weekly, 17%) is not calculated.

⁃ Intent clarification — the core clinical function — had the lowest acceptance (44%) and warrants discussion.

Findings Worth Foregrounding

1 Context-dependent utility. The tool was most valued in high-stakes, unfamiliar contexts and least with close contacts, suggesting future work should target situational communication anxiety rather than global support. Currently buried in Theme 4.

2 User-generated override rules. Half of participants created rules to bypass suggestions contextually, demonstrating active negotiation with the system and emerging meta-communicative awareness. Captured systematically, override patterns would offer the strongest evidence for understanding communicative independence and merit promotion to a primary research question.

Conclusions

⁃ Absent control group, unvalidated instruments, undescribed tool,  withheld data/code, underpowered sample, insufficient duration, and a qualitative strand not meeting reporting standards are design-level issues unresolvable through revision.

⁃ I encourage the authors to treat this as a pilot, open-source their tool, adopt validated instruments, and design a controlled trial.

⁃ The context-dependent utility and user override findingsshould anchor the next iteration.

⁃ The research question is important and deserves rigorous investigation.

Reviewer #3: The limited sample size and generalizability, as only 16 verbal and cognitively able teenagers and young adults with ASD participated, making it difficult to draw conclusions for younger children, minimally verbal individuals, or those with intellectual disabilities.

The study's short eight-week duration means that long-term effects and skill retention are unknown.

Additionally, the tool was found to be less useful in informal communication contexts, such as conversations with close friends or family, and some participants desired more customization and consistency in the feedback provided. The research was also limited to English-speaking participants, excluding non-English speakers who might benefit from such technology, and the data cannot be shared publicly due to ethical reasons, which may hinder external validation or replication of the findings.

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Reviewer #1: Yes: Dr. Rinku Sharma Dixit

Reviewer #2: Yes: Adrian C Demidont

Reviewer #3: Yes: Dr. A Ajina

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-->PONE-D-26-04125R1-->-->Using Natural Language Processing to Support Digital Communication in Adolescents and Young Adults with Autism Spectrum Disorder: A Pilot Feasibility Study-->-->PLOS One

Dear Dr. ALGAHTANI,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.-->-->

The authors have made substantial efforts to address several concerns raised during the previous review round, particularly regarding statistical reporting, pilot-study framing, and expansion of methodological details. However, important issues remain.

Several inconsistencies persist between the response letter and the revised manuscript. References to “children with ASD” remain despite the participant sample comprising adolescents and young adults, dose-response language continues to appear in the Results section, and some interpretations of follow-up findings remain stronger than the study design supports. In addition, the data availability statements are still contradictory and require clarification to ensure compliance with PLOS ONE policies. The qualitative analysis section would also benefit from clearer reporting of the procedures described in the response letter.

The reviewer comments are generally consistent in emphasizing concerns related to reporting transparency, interpretation of findings, reproducibility, and consistency across the manuscript. These issues should be addressed before the manuscript can be considered further.

Based on the remaining concerns regarding reporting rigor and transparency, I recommend Major Revision .

Please submit your revised manuscript by Jul 16 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:-->

• A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

As the corresponding author, your ORCID iD is verified in the submission system and will appear in the published article. PLOS supports the use of ORCID, and we encourage all coauthors to register for an ORCID iD and use it as well. Please encourage your coauthors to verify their ORCID iD within the submission system before final acceptance, as unverified ORCID iDs will not appear in the published article. Only  the individual author can complete the verification step; PLOS staff cannot  verify ORCID iDs on behalf of authors.

We look forward to receiving your revised manuscript.

Kind regards,

Ramandeep Kaur

Academic Editor

PLOS One

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Additional Editor Comments:

Thank you for submitting the revised version of the manuscript. The authors have made substantial efforts to address many of the concerns raised during the previous review round, particularly with respect to statistical reporting, pilot-study framing, inclusion of confidence intervals, technical specifications of the NLP system, and expansion of the qualitative findings.

However, several important issues remain insufficiently addressed and require further revision before the manuscript can be considered for publication.

Although the authors indicate that all references to “children with autism” were replaced with “adolescents and young adults,” multiple sections of the manuscript continue to refer to children with ASD despite the participant age range being 15–28 years. Terminology should be reviewed carefully throughout the manuscript to ensure consistency.

The response letter states that all “dose-response” language has been removed; however, the Results section continues to describe the correlation between tool use and confidence improvement as indicating a “dose-response relationship.” Given the observational nature of the study and self-selected usage patterns, such terminology remains inappropriate and should be removed.

Concerns regarding overinterpretation of follow-up findings have not been fully resolved. The manuscript continues to suggest that participants “internalized communication strategies” despite observed declines between intervention and follow-up phases. These findings should be described more cautiously and without implying mechanisms that were not directly measured.

Data availability statements remain inconsistent across the submission. Different sections alternatively indicate that data are fully available without restriction, unavailable due to ethical considerations, available upon request from authors, or available through a Data Access Committee. A single, internally consistent statement compliant with PLOS data-sharing requirements is needed.

The qualitative analysis has been improved through the inclusion of participant quotes and theme frequencies; however, several elements described in the response letter are not clearly evident in the manuscript. The coding procedure, thematic analysis process, saturation procedures, and disconfirming cases should be described more explicitly to support transparency and rigor.

The manuscript still requires careful language editing. Numerous grammatical inconsistencies, duplicated content, formatting issues, and awkwardly constructed sentences remain. A thorough editorial review is recommended to improve readability and presentation.

The response letter indicates that additional analyses and figures were added (e.g., weekly usage trends), but these additions are not clearly identifiable within the revised manuscript. Please ensure that all revisions described in the response document are fully incorporated into the manuscript and appropriately referenced in the text.

Overall, the manuscript addresses an important and timely topic with potential relevance for assistive communication technologies in autism. However, the remaining concerns primarily relate to consistency, transparency, interpretation of findings, and reporting quality. These issues should be addressed before the manuscript can be reconsidered.

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NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

<p>Using Natural Language Processing to Support Digital Communication in Adolescents and Young Adults with Autism Spectrum Disorder: A Pilot Feasibility Study

PONE-D-26-04125R2

Dear Dr. ALGAHTANI,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Ramandeep Kaur

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments: