-->PONE-D-25-50100-->-->Teacher Support for Generative AI Use and Design Learning Outcomes in Fashion Design Education: A Structural Equation Modeling Study-->-->PLOS ONE

Dear Dr. Zhiyi Zhang,

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.

Both reviewers agree that the manuscript addresses an important and timely topic: how teacher support influences generative AI use and design learning outcomes in fashion design education. However, substantial revisions are required before the manuscript can proceed.

1. Theoretical and Conceptual Issues

The integration of the Technology Acceptance Model (TAM) and Social Cognitive Theory is incomplete. Key constructs such as perceived usefulness, self-efficacy, and teacher support are presented but not theoretically connected. Central variables, especially creative design capability, lack clear definitions, sub-dimensions, or justification, which weakens the conceptual framework and measurement validity.

2. Overinterpretation of Cross-Sectional Data

The manuscript uses causal language despite relying solely on cross-sectional survey data. Claims about mediation effects and directional influence should be reframed as associations, and limitations regarding temporal ordering must be clearly acknowledged.

3. Methodological Clarifications Needed

Although SEM is appropriate, several concerns must be addressed: unusually perfect fit indices suggest potential overfitting; prior AI experience is not used as a control variable; and the mediation analysis lacks standard reporting details (e.g., BCa intervals, bootstrap settings). Reviewer 2 also requests clearer sampling procedures, bias control, and confirmation that ethics approval covers all participating institutions.

4. Writing and Contextual Strengthening

Reviewer 1 notes issues with verbosity and unclear terminology. Reviewer 2 recommends adding stronger pedagogical context particularly how fashion design education traditionally fosters creativity and how AIGC reshapes ideation and studio practice.

5. Data Availability Requirements

Reviewer 2 indicates that the manuscript does not yet meet PLOS ONE data-availability standards.

Conclusion

The manuscript shows promise but requires major revision in theory development, construct clarity, methodological transparency, and writing precision. A substantially revised version addressing all reviewer concerns will be needed for further consideration

Please submit your revised manuscript by  Jan 10 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.. 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,

Dokun Iwalewa OIuwajana

Academic Editor

PLOS ONE

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Additional Editor Comments:

Both reviewers agree that the manuscript addresses an important and timely topic: how teacher support influences generative AI use and design learning outcomes in fashion design education. However, substantial revisions are required before the manuscript can proceed.

1. Theoretical and Conceptual Issues

The integration of the Technology Acceptance Model (TAM) and Social Cognitive Theory is incomplete. Key constructs such as perceived usefulness, self-efficacy, and teacher support are presented but not theoretically connected. Central variables, especially creative design capability, lack clear definitions, sub-dimensions, or justification, which weakens the conceptual framework and measurement validity.

2. Overinterpretation of Cross-Sectional Data

The manuscript uses causal language despite relying solely on cross-sectional survey data. Claims about mediation effects and directional influence should be reframed as associations, and limitations regarding temporal ordering must be clearly acknowledged.

3. Methodological Clarifications Needed

Although SEM is appropriate, several concerns must be addressed: unusually perfect fit indices suggest potential overfitting; prior AI experience is not used as a control variable; and the mediation analysis lacks standard reporting details (e.g., BCa intervals, bootstrap settings). Reviewer 2 also requests clearer sampling procedures, bias control, and confirmation that ethics approval covers all participating institutions.

4. Writing and Contextual Strengthening

Reviewer 1 notes issues with verbosity and unclear terminology. Reviewer 2 recommends adding stronger pedagogical context particularly how fashion design education traditionally fosters creativity and how AIGC reshapes ideation and studio practice.

5. Data Availability Requirements

Reviewer 2 indicates that the manuscript does not yet meet PLOS ONE data-availability standards.

Conclusion

The manuscript shows promise but requires major revision in theory development, construct clarity, methodological transparency, and writing precision. A substantially revised version addressing all reviewer concerns will be needed for further consideration

[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: Partly

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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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.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: No

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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

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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: I have the following comments, and please read my annotated pdf file where includes other important suggestions.

1. Although this paper addresses a timely and significant topic—examining how teacher support influences students’ use of generative AI and its subsequent impact on design learning outcomes in fashion design education—it suffers from several notable weaknesses across theoretical, methodological and linguistic dimensions.

2. Theoretically, while the authors claim to integrate the Technology Acceptance Model (TAM) with Social Cognitive Theory (SCT), the integration remains superficial. Key constructs such as “perceived usefulness” (TAM) and “self-efficacy” (SCT) are listed, but their interaction is never explicitly modelled or discussed. For example, the paper does not explain how an increase in students’ self-efficacy (SCT) might moderate or mediate the relationship between perceived usefulness (TAM) and actual AI use, leaving the theoretical contribution thin.

3. In terms of research questions, the three questions do form a logical chain (teacher support → AI use → creative capability → learning outcomes), yet the central mediator—“creative design capability”—is never clearly defined. No sub-dimensions such as fluency, flexibility or originality are provided, so the reader cannot tell whether the construct refers to ideational quantity, aesthetic innovation, or technical proficiency. This conceptual vagueness blurs the focus of the entire study.

4. The conclusions repeatedly over-interpret cross-sectional data causally. A typical sentence reads, “62.3 % of technical support’s effect on learning outcomes operates through behavioural and cognitive mediators.” With only a single-time-point survey, the authors cannot rule out reverse causality (students who already possess strong creative skills may be more likely to seek out AI tools and to perceive higher teacher support), so causal language is unwarranted.

5. Linguistically, the manuscript is generally intelligible, yet it is riddled with verbose and opaque sentences. A representative example: “The findings challenge monolithic conceptualizations of educational support, demonstrating that instrumental competencies supersede affective dimensions in facilitating AI tool adoption.” The term “monolithic conceptualizations” is never defined; readers cannot tell whether it refers to uni-dimensional scales, uniform teacher-training policies, or something else. Another illustration of poor cohesion appears when the authors try to explain the null effect of emotional support: “Alternatively, emotional support may moderate rather than directly influence adoption behaviors, a possibility warranting future investigation through more nuanced analytical frameworks.” The sentence is speculative, references no data, and functions as a rhetorical bridge rather than a logically grounded next step.

6. Methodologically, the choice of structural-equation modelling is appropriate, but several technical problems undermine confidence in the results. First, fit indices are suspiciously perfect (CFI = 1.000, RMSEA = 0.003, SRMR = 0.019), raising the possibility of over-fitting or capitalisation on chance; no sensitivity analyses (e.g., alternative estimators, parcelled vs. item-level indicators) are reported to rule this out. Second, prior AI experience is measured but is used only as a descriptive statistic; it is not entered as a control variable in the structural model, even though experienced users might both perceive higher teacher support and achieve better learning outcomes, thereby inflating path coefficients. Third, the mediation analysis states that “5 000 bootstrap resamples” were run, yet the paper fails to specify whether bias-corrected and accelerated (BCa) confidence intervals were used, what seed was set, or whether any convergence problems occurred, all of which are standard reporting requirements for bootstrap mediation (Hayes, 2022). These omissions make it difficult to replicate or trust the indirect-effect estimates.

Reviewer #2: The manuscript presents an important and timely study on how teacher support influences generative AI (AIGC) adoption and learning outcomes in fashion design education. The quantitative modeling is rigorous, but several areas require theoretical and contextual enrichment.

First, the paper should begin by describing the fashion design curriculum and pedagogy before AIGC, explaining how creativity was traditionally fostered through studio-based, project-oriented, and craft-focused methods such as sketching, textile manipulation, and critique sessions. This context would clarify how AIGC transforms long-standing creative learning practices.

In the literature review, all constructs should be clearly justified with sources. For instance, explain why teacher support is conceptualized as technical, pedagogical, and emotional—each supported by existing educational technology literature—and discuss how this structure ensures content validity. Likewise, justify the operational definitions of “AI usage behavior,” “creative design capability,” and “design learning outcomes” with previous studies, providing sample items for each construct.

The conceptual model and each path should be more explicitly grounded in theory. Teacher support should be linked to AI usage through the Technology Acceptance Model, usage to creative capability through cognitive engagement, and capability to learning outcomes through Social Cognitive Theory. Cite recent works in fashion and design education showing how AIGC enhances ideation, problem-solving, and creativity through iterative co-design processes.

Clarify how learning outcomes were measured—such as portfolio quality, project complexity, or self-assessed skill gains—and justify these indicators. The population, consisting of 500 students across 18 Chinese universities, should include the estimated total population and sampling error (approximately ±4.2% at a 95% confidence level). The manuscript should also describe procedures to avoid online data bias, including verified institutional recruitment, IP filtering, and anonymized responses.

The ethics section appropriately notes approval from the University of Leeds (FAHC 22-010); ensure this covers all participating institutions. Finally, enrich the SEM results by elaborating on how AIGC use concretely stimulates creative design capability—highlighting behavioral and cognitive transformation rather than just statistical relationships.

Overall, the manuscript offers meaningful contributions but needs stronger pedagogical grounding, construct justification, and interpretation of how AIGC operationally produces creativity. Recommended decision: Major Revision.

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Reviewer #1: No

Reviewer #2: Yes: Eric C K ChengEric C K Cheng

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Attachments

Attachment

Submitted filename: annotated PONE-D-25-50100.pdf

-->-->PONE-D-25-50100R1-->-->Teacher Support for Generative AI Use and Design Learning Outcomes in Fashion Design Education: A Structural Equation Modeling Study-->-->PLOS One

Dear Dr. zhang,

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.-->--> -->-->==============================-->

• We noted that one of the references cited has been retracted. Please see https://dl.acm.org/doi/10.1145/3526219 for more details and please revise the submission to remove any references to retracted publications.
• Additionally, to ensure that PLOS One requirements for human subjects research ethics are met, please include the study protocol that was reviewed and approved by the IRB as well as any extension documents issued on the original approval. These documents are for review purposes only and should be included as "Other" files.

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

Please submit your revised manuscript by May 07 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.. 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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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.

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Staff Editor

PLOS One

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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.-->

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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-->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 #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

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. 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

**********

-->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 #1: Yes

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 #1: The revised version addressed all my concerns in regard to the literature reivew, methodology, and discussion. I recommend it to be published.The revised version addressed all my concerns in regard to the literature reivew, methodology, and discussion. I recommend it to be published.

Reviewer #2: The revised manuscript has comprehensively addressed my comments and concerns. The theoretical framework has been substantially strengthened, with clearer integration between TAM and SCT and more explicit articulation of key constructs and mechanisms. The definition and operationalization of creative design capability are now conceptually precise and well justified. Causal language has been appropriately moderated to reflect the cross-sectional design, and methodological transparency has improved, particularly regarding model fit interpretation, control variables, and bootstrap procedures. The writing has also been significantly clarified and streamlined. Overall, the revisions have enhanced the manuscript’s rigor, coherence, and contribution to the field. The revised manuscript has comprehensively addressed my comments and concerns. The theoretical framework has been substantially strengthened, with clearer integration between TAM and SCT and more explicit articulation of key constructs and mechanisms. The definition and operationalization of creative design capability are now conceptually precise and well justified. Causal language has been appropriately moderated to reflect the cross-sectional design, and methodological transparency has improved, particularly regarding model fit interpretation, control variables, and bootstrap procedures. The writing has also been significantly clarified and streamlined. Overall, the revisions have enhanced the manuscript’s rigor, coherence, and contribution to the field.

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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  For information about this choice, including consent withdrawal, please see our Privacy Policy..-->

Reviewer #1: No

Reviewer #2: Yes: Eric C K ChengEric C K Cheng

**********

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-->-->

Teacher Support for Generative AI Use and Design Learning Outcomes in Fashion Design Education: A Structural Equation Modeling Study

PONE-D-25-50100R2

Dear Dr. zhang,

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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PLOS One

Additional Editor Comments (optional):

Reviewers' comments: