Dear Dr. Yan,

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 presents an innovative attempt to integrate AI-generated content (AIGC) into multimodal instructional design, focusing on noun retention. The topic aligns with PLOS ONE’s criteria for methodological soundness and interdisciplinary relevance. However, the current version requires substantial revision before it can be considered for acceptance.

The required changes include major clarification of the study design, particularly in distinguishing between AIGC-generated visual material and general multimodal learning effects. As currently structured, the experiment tests the difference between unimodal and multimodal conditions rather than isolating the contribution of AI-generated imagery. The conclusions must therefore be rewritten to accurately represent the study’s scope and findings. Additionally, participant demographic details, adherence to the journal’s data availability policy, and correction of minor typographical and referencing issues are mandatory for acceptance.include major clarification of the study design, particularly in distinguishing between AIGC-generated visual material and general multimodal learning effects. As currently structured, the experiment tests the difference between unimodal and multimodal conditions rather than isolating the contribution of AI-generated imagery. The conclusions must therefore be rewritten to accurately represent the study’s scope and findings. Additionally, participant demographic details, adherence to the journal’s data availability policy, and correction of minor typographical and referencing issues are mandatory for acceptance.include major clarification of the study design, particularly in distinguishing between AIGC-generated visual material and general multimodal learning effects. As currently structured, the experiment tests the difference between unimodal and multimodal conditions rather than isolating the contribution of AI-generated imagery. The conclusions must therefore be rewritten to accurately represent the study’s scope and findings. Additionally, participant demographic details, adherence to the journal’s data availability policy, and correction of minor typographical and referencing issues are mandatory for acceptance.include major clarification of the study design, particularly in distinguishing between AIGC-generated visual material and general multimodal learning effects. As currently structured, the experiment tests the difference between unimodal and multimodal conditions rather than isolating the contribution of AI-generated imagery. The conclusions must therefore be rewritten to accurately represent the study’s scope and findings. Additionally, participant demographic details, adherence to the journal’s data availability policy, and correction of minor typographical and referencing issues are mandatory for acceptance.

Recommended changes include enhancing the theoretical framework with a balanced discussion of both the potential and limitations of AIGC, refining the introduction to better justify the research rationale, and improving readability through concise, focused language.include enhancing the theoretical framework with a balanced discussion of both the potential and limitations of AIGC, refining the introduction to better justify the research rationale, and improving readability through concise, focused language.include enhancing the theoretical framework with a balanced discussion of both the potential and limitations of AIGC, refining the introduction to better justify the research rationale, and improving readability through concise, focused language.include enhancing the theoretical framework with a balanced discussion of both the potential and limitations of AIGC, refining the introduction to better justify the research rationale, and improving readability through concise, focused language.

The reviewer’s observations are consistent and valid, with no major conflicts in interpretation. Addressing these core methodological and reporting concerns will ensure the paper meets PLOS ONE’s standards for technical rigor, transparency, and interpretive accuracy. Therefore, the decision at this stage is Major Revision....

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Please submit your revised manuscript by Dec 14 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.. 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.. 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 study offers an engaging exploration of how AI-generated visual materials may influence noun retention in multimodal learning contexts. The topic is timely and relevant, especially with the increasing integration of artificial intelligence in education. However, several substantive and structural concerns need to be addressed before the paper can progress further.

Conceptual Clarity and Design – The comparison between text-only and multimodal (text + image) conditions does not specifically evaluate the impact of AIGC. To support claims about AIGC-enhanced learning, one of the conditions should employ traditional (non-AI) imagery. Otherwise, the conclusions should be reframed to emphasize multimodal rather than AIGC-based effects.

Introduction and Theoretical Framing – Expand the introduction to define AIGC at first mention and include both supportive and critical perspectives. Discuss known limitations of AI-generated images, such as possible inaccuracy or bias, to present a balanced view.

Methodology Details –

Provide clear participant demographics (age, gender, background, proficiency level).

Justify the chosen tasks and materials, particularly the “image-to-word matching” task, which may not be suitable for a text-only control group.

Ensure all experimental comparisons are methodologically equivalent.

Data Availability – Ensure full compliance with PLOS ONE’s data policy by sharing anonymized datasets or including a valid reason for restrictions.

Writing and Structure –

Revise the abstract to expand “AIGC” and highlight the study’s key findings succinctly.

Correct minor typographical errors and reference inconsistencies (e.g., “approximately” on p.2; missing reference on p.5).

The final paragraph of the introduction should emphasize study rationale and objectives, not manuscript structure.

Conclusion Revision – Rephrase the conclusion to accurately reflect that multimodal learning was superior to unimodal learning in this study, rather than attributing the improvement solely to AIGC.

With these major revisions, the manuscript could make a meaningful contribution to the field of AI-supported education and multimodal learning research.

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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?

Reviewer #1: Partly

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

Reviewer #1: 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: No

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

Reviewer #1: Yes

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Reviewer #1: Reviewer Comments

1. The abstract introduces “AIGC” but does not expand the abbreviation at its first mention.

2. There is a typographical error for “approximately” on page 2.

3. The last paragraph on page 4 should be revised: Instead of outlining the structure of the paper, focus on providing engaging background, rationale, and objectives. A section-by-section preview is less appropriate for the journal's format.

4. On page 5, the statement regarding “traditional multimodal materials” requires a proper reference.

5. The introduction lacks a non-biased review. It should address concerns that AIGC can produce incorrect or distorted images, not just cite studies that support AIGC. A balanced review including potential limitations is needed.

6. There are methodological flaws: The comparison is between unimodal (text-only) and multimodal (text + picture) learning rather than AIGC-based versus traditional learning. Traditional learning typically uses both text and images. For a valid comparison, one group should learn with traditional images and text, and another with AI-generated images and text—then compare outcomes.

7. Please provide demographic details of all study participants.

8. The conclusion that “AIGC-enhanced instruction led to more effective noun learning across multiple aspects” is misleading. The study compares unimodal and multimodal learning, not specifically the impact of AI-generated images. The conclusion should reflect that multimodal learning outperformed text-based learning, regardless of image source.

9. The “image-to-word matching” task (gap of 0.85 vs. 0.45) appears inappropriate because Group 1 only received text (without images), thus undermining the comparison.

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what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Jithin BalanJithin BalanJithin BalanJithin Balan

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Multimodal Instruction with AI-Generated Images for Noun Retention: Exploring Semantic Scene and Materiality Effects

PONE-D-25-52191R1

Dear Dr. Yan,

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.

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Kind regards,

Ramandeep Kaur

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments: