-->PONE-D-25-21913-->-->The Impact of University Students’ Computational Thinking on AI Literacy: A Longitudinal Study Based on SEM-PLS-->-->PLOS ONE

Dear Dr. Huang,

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.

Please submit your revised manuscript by Oct 11 2025 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 rebuttal 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.

We look forward to receiving your revised manuscript.

Kind regards,

Chengliang Wang

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We are unable to open your Supporting Information file [Supporting information.rar]. Please kindly revise as necessary and re-upload.

3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

4. 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.

[Note: HTML markup is below. Please do not edit.]

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: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

-->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: Yes

Reviewer #2: Yes

**********

-->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: Yes

Reviewer #2: Yes

**********

-->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: Dear Authors,

Your study adopted a cross-lagged SEM framework to explore the relationship between CT and AIL, which is really meaningful and well-reported. I recommend strengthening the following aspects before your study is published.

1. Conceptual introduction: The core variables should be introduced in the first chapter, including at least their brief/plain-language definitions, why they are important, and how they can be connected with each other.

2. Rewrite the literature review to emphasize research gaps: The current chapter of the literature review is a series of documented previous studies, with very clear topic sentences. The problem is that the authors overemphasized what is known to us. I believe you should analyze and summarize the research gaps that drive you to your research questions and aims.

3. Could you please add theoretical rationales for CT to predict next-time AIL, and not in the reverse direction? E.g., AIL may be promoted through specific training and technology use, which should subsequently benefit CT skills. Then, why is your hypothesized direction conceptually sound?

4. Please provide references for your data cleaning process. E.g., https://doi.org/10.1007/s40299-021-00570-0

5. In the research model, a number of potential moderators are not mentioned, e.g., AI use experience, academic achievements, and gender. Is this gender bias common, and why? How much do you estimate it should influence or bias your results? You may check similar research findings and contexts like https://doi.org/10.1186/s41239-023-00403-8 Potential moderators should be discussed in your implications and limitations.

6. Conceptual synthesis: We currently know that AIL and CT are compound concepts, meaning that they have several subdimensions. However, they are treated as high-level variables, ignoring the specific aspects. This is also a limitation of your study. E.g., https://doi.org/10.1080/0144929X.2022.2072768

Overall, your study is a good one that has been well conducted and reported. Please kindly strengthen the above aspects, add high-quality impactful literature related to your topics, and conduct overall language and format checks. I will be glad to hear back from you.

Reviewer #2: This study presents a commendable longitudinal investigation examining the impact of computational thinking on AI literacy among 216 undergraduate students, though certain methodological limitations warrant attention.

The following points require strengthening:

1. Temporal Design Validity: The two-month interval between data collection points appears insufficient for robust longitudinal analysis. As the authors acknowledge, "two months may not be sufficient to capture the complex dynamics and long-term developmental trends in the relationships among variables." However, no empirical justification is provided for this timeframe choice. Evidence demonstrating that this duration is adequate to avoid test-retest effects and capture meaningful developmental changes in both computational thinking and AI literacy is needed.

2. Gender Distribution Imbalance: The sample exhibits a significant gender imbalance with a male-to-female ratio of approximately 2:3 (39% male, 61% female). As noted by the authors, this "may restrict the generalizability of the results to broader populations." Gender-specific analyses should be conducted to ensure no systematic gender differences affect the structural relationships between computational thinking and AI literacy.

3. AI Literacy Measurement Transparency: Given that AI literacy concepts are rapidly evolving, full disclosure of the 38-item scale adapted from Laupichler, Aster (29) in the supplementary materials is essential for replication and validity assessment.

4. Cross-sectional vs. Longitudinal Findings Discrepancy: The rejection of H3 (β = 0.021, p = 0.708) presents a critical contradiction requiring deeper exploration. While cross-sectional analyses demonstrate that "computational thinking significantly and positively predicts AI literacy" (H1: β = 0.507, H2: β = 0.567, both p < 0.001), the longitudinal predictive relationship from T0 computational thinking to T1 AI literacy is non-significant. This discrepancy undermines the core premise that computational thinking serves as a longitudinal predictor of AI literacy development. The authors must provide theoretical explanations for why cross-sectional associations do not translate to longitudinal predictions, particularly when both constructs show significant improvement over time (H4 and H5 supported).

5. Causal Inference Limitations: This observational study design precludes strong causal claims. The limitations section should explicitly state that causal relationships cannot be definitively established without experimental manipulation. Additionally, the exclusive focus on computational thinking as a predictor overlooks other potentially important variables mentioned in the literature, including "digital divide, perceived trust," learning environment, individual motivation, and prior programming experience.

6. Attrition Analysis: The study experienced substantial participant attrition from the initial sample of 301 students to the final 216 participants (28.2% attrition rate). Specifically, 39 participants from the initial sample did not complete the second-round questionnaire, and an additional 35 responses were excluded due to invalid completion patterns. However, no systematic analysis was conducted to examine whether participants who dropped out differed systematically from those who remained in terms of baseline computational thinking abilities, AI literacy levels, or demographic characteristics. This lack of attrition analysis limits the ability to assess potential selection bias and threatens the internal validity of the longitudinal findings. A comparison between completers and non-completers on key study variables should be provided to ensure that the observed relationships are not artifacts of differential dropout patterns.

**********

-->6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.-->

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

-->PONE-D-25-21913R1-->-->The Impact of University Students’ Computational Thinking on AI Literacy: A Longitudinal Study Based on SEM-PLS-->-->PLOS ONE

Dear Dr. Huang,

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.

-->-->Please note Reviewer #2's outstanding concerns.

Please submit your revised manuscript by Dec 28 2025 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 rebuttal 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.

We look forward to receiving your revised manuscript.

Kind regards,

Avanti Dey, PhD

Staff 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.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #2: All comments have been addressed

**********

-->2. 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 #2: Partly

**********

-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #2: Yes

**********

-->4. 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 #2: Yes

**********

-->5. 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 #2: Yes

**********

-->6. 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 #2: 1) There are quite a few typos.

2) Also, while you're performing a t-test, this requires normality, so please include the results of normality tests.

3) This is the main revision. In the abstract, it states “novel contribution...establishing a longitudinal link,” but the longitudinal hypothesis H3 (CT at T0 → AIL at T1) is not significant (β=0.021, p=0.708). Therefore, this claim of novelty is inconsistent with the current results. Consequently, the claim does not hold. Please address this inconsistency.

**********

-->7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.-->

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation.

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

<div>PONE-D-25-21913R2-->-->The Impact of University Students’ Computational Thinking on AI Literacy: A Longitudinal Study Based on SEM-PLS-->-->PLOS One

Dear Dr. Huang,

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.

Please submit your revised manuscript by May 20 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,

Dr. Manuel Salas-Velasco, PhD

Academic Editor

PLOS One

Journal Requirements:

1. 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.

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Overall, the reviewers agree that the manuscript is technically sound, the statistical analyses are appropriately conducted, and the data adequately support the conclusions. The revisions made in response to previous reviewer comments have substantially improved the clarity, rigor, and balance of the manuscript. In particular, the adjustment of claims regarding longitudinal effects and the strengthened methodological transparency are noted positively.

Reviewer 3 raises several points that are largely editorial or contextual in nature (e.g., style of the abstract, language polishing, discussion of generalizability, and broader contextualization of AI-related risks). These suggestions are reasonable and can be addressed through minor revisions aimed at improving clarity and discussion, without requiring any changes to the study design, analyses, or results.

One comment related to the justification of AMOS and covariance-based SEM (CB-SEM) is not applicable, as the manuscript clearly employs PLS-SEM using SmartPLS, with an appropriate methodological justification already provided. This point does not require further action.

Accordingly, the editorial assessment is that no major methodological or conceptual concerns remain. The manuscript would benefit from a minor revision focused on:

•  Light stylistic refinement of the abstract (if desired),

•  Minor language polishing,

•  Briefly reinforcing the discussion of sample size, timeliness, and broader educational implications of AI, including potential risks, at a conceptual level.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

-->2. 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 #2: Yes

Reviewer #3: Yes

**********

-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #2: Yes

Reviewer #3: Yes

**********

-->4. 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 #2: Yes

Reviewer #3: Yes

**********

-->5. 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 #2: Yes

Reviewer #3: Yes

**********

-->6. 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 #2: Regarding the three comments, they are partially unmet.

Comment 1:

While not a major issue, please perform careful proofreading once more before final acceptance. For example:

・In Reference 5, the space is missing between “Framework Construction and Enhancement Paths” and Construction and.

・In the conference name of the reference, “WIPSCE 20242024” has duplicate years.

・In the labels of Figure 2 (Structural Model), “T0-Computation thinking”, “T1- Computation thinking” are missing the letter ‘l’ in “Computational”.

Comment 2:

The Shapiro–Wilk test statistic and p-value itself are not reported in the main text. The paragraph above only states “baseline continuous variables might deviate from a normal distribution” without reporting any normality test values.

Furthermore, while the authors emphasize paired t-tests for T0–T1 changes (H4, H5) in this paper, that section (Table 7 and surrounding text) does not include any mention of normality or results from nonparametric tests like the Wilcoxon signed-rank test.

Comment 3:

Regarding the phrasing “positive predictor” in the conclusion, it would be safer to soften it to explicitly indicate a cross-sectional prediction (association at the same time point), such as “cross-sectional models showed that CT is a positive predictor of AIL at each time point.”

Reviewer #3: Dear Editor, Dear Authors,

I can see that the authors have revised the manuscript carefully, and I believe the paper can be accepted after minor revisions. Please consider the following comments before final acceptance.

Abstract: Please remove numerical values from the abstract, as numbers alone do not communicate the study’s meaning clearly. Instead, the abstract should briefly state the background, purpose, key findings, and especially the implications in a more descriptive way.

Language quality: There are indications that AI tools may have been used in drafting the manuscript. The paper should undergo careful human proofreading (or professional language editing) to improve clarity, coherence, and academic tone before acceptance.

Timeliness of the study: The study appears to be based on data collected in 2024. Given that it is now 2026 and the AI field evolves rapidly, the authors should justify the continued relevance of the findings and/or explicitly acknowledge this as a limitation.

Sample size and potential bias: The sample size is relatively small. Please explain how the authors ensured the findings are not strongly affected by sampling bias, and discuss how the sample size may influence generalizability. If applicable, add a limitation and suggestions for future research with larger samples.

Methodology (AMOS and CB-SEM justification): The authors should clearly justify why AMOS was used and briefly explain what covariance-based SEM (CB-SEM) is, including why it is appropriate for this study. A recent example with clear methodological explanation is:

(2024). Exploring the factors affecting elementary mathematics teachers’ innovative behavior: An integration of social cognitive theory. Scientific Reports, 14(1), 1–14. https://doi.org/10.1038/s41598-024-52604-4

Discussion (negative effects of AI): The discussion should also acknowledge and analyze potential negative effects or risks of AI in education (e.g., overreliance, AI dependency, bias, reduced critical thinking, ethical concerns). The discussion can be strengthened by engaging with recent related literature, such as:

(2024). Examining Chinese preservice mathematics teachers’ adoption of AI chatbots for learning: Unpacking perspectives through the UTAUT2 model. Education and Information Technologies.

(2024). Latent profile analysis of AI literacy and trust in mathematics teachers and their relations with AI dependency and 21st-century skills. Behavioral Sciences (Basel, Switzerland), 14.

(2025). Exploring the relationship between AI literacy, AI trust, AI dependency, and 21st-century skills in preservice mathematics teachers. Scientific Reports, 15(1).

**********

-->7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.-->

Reviewer #2: No

Reviewer #3: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation.

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

-->

The Impact of University Students’ Computational Thinking on AI Literacy: A Longitudinal Study Based on SEM-PLS

PONE-D-25-21913R3

Dear Dr. Huang,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Dr. Manuel Salas-Velasco, PhD

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments: