Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles

Can ChatGPT write better scientific titles? 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A comparative evaluation of human-written and AI-generated titles", "datePublished": "2025-12-30T06:19:43", "dateModified": "2026-02-05T12:05:06", "author": [ { "@type": "Person", "name": "Paul Sebo" }, { "@type": "Person", "name": "Bing Nie" }, { "@type": "Person", "name": "Ting Wang" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Large language models (LLMs) such as GPT-4 are increasingly used in scientific writing, yet little is known about how AI-generated scientific titles are perceived by researchers in terms of quality. Objective To compare the perceived alignment with the abstract content (as a surrogate for perceived accuracy), appeal, and overall preference for AI-generated versus human-written scientific titles. Methods We conducted a blinded comparative study with 21 researchers from diverse academic backgrounds. A random sample of 50 original titles was selected from 10 high-impact general internal medicine journals. For each title, an alternative version was generated using GPT-4.0. Each rater evaluated 50 pairs of titles, each pair consisting of one original and one AI-generated version, without knowing the source of the titles or the purpose of the study. For each pair, raters independently assessed both titles on perceived alignment with the abstract content and appeal, and indicated their overall preference. We analyzed alignment and appeal using Wilcoxon signed-rank tests and mixed-effects ordinal logistic regressions, preferences using McNemar’s test and mixed-effects logistic regression, and inter-rater agreement with Gwet’s AC. Results AI-generated titles received significantly higher ratings for both perceived alignment with the abstract content (mean 7.9 vs. 6.7, p-value <0.001) and appeal (mean 7.1 vs. 6.7, p-value <0.001) than human-written titles. The odds of preferring an AI-generated title were 1.7 times higher (p-value =0.001), with 61.8% of 1,049 paired judgments favoring the AI version. Inter-rater agreement was moderate to substantial (Gwet’s AC: 0.54–0.70). Conclusions AI-generated titles were rated more favorably than human-written titles within the context of this study in terms of perceived alignment with the abstract content, appeal, and preference, suggesting that LLMs may enhance the effectiveness of scientific communication. These findings support the responsible integration of AI tools in research. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1470", "name": "Can ChatGPT write better scientific titles? A comparative evaluation..." } } ] } Home Browse Can ChatGPT write better scientific titles? A comparative evaluation... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Sebo P, Nie B and Wang T. Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.12688/f1000research.173647.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] Paul Sebo https://orcid.org/0000-0001-7616-0017 1 , Bing Nie 2 , Ting Wang 3 Paul Sebo https://orcid.org/0000-0001-7616-0017 1 , Bing Nie 2 , Ting Wang 3 PUBLISHED 05 Feb 2026 Author details Author details 1 University Institute for Primary Care, University of Geneva, Geneva, Switzerland 2 Zhejiang Tongji Vocational College of Science and Technology, Hangzhou, Zhejiang, China 3 School of Library and Information Management, Emporia State University, Emporia, Kansas, USA Paul Sebo Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation Bing Nie Roles: Conceptualization, Data Curation, Methodology, Project Administration Ting Wang Roles: Conceptualization, Data Curation, Methodology, Project Administration OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. This article is included in the Research on Research, Policy & Culture gateway. Abstract Background Large language models (LLMs) such as GPT-4 are increasingly used in scientific writing, yet little is known about how AI-generated scientific titles are perceived by researchers in terms of quality. Objective To compare the perceived alignment with the abstract content (as a surrogate for perceived accuracy), appeal, and overall preference for AI-generated versus human-written scientific titles. Methods We conducted a blinded comparative study with 21 researchers from diverse academic backgrounds. A random sample of 50 original titles was selected from 10 high-impact general internal medicine journals. For each title, an alternative version was generated using GPT-4.0. Each rater evaluated 50 pairs of titles, each pair consisting of one original and one AI-generated version, without knowing the source of the titles or the purpose of the study. For each pair, raters independently assessed both titles on perceived alignment with the abstract content and appeal, and indicated their overall preference. We analyzed alignment and appeal using Wilcoxon signed-rank tests and mixed-effects ordinal logistic regressions, preferences using McNemar’s test and mixed-effects logistic regression, and inter-rater agreement with Gwet’s AC. Results AI-generated titles received significantly higher ratings for both perceived alignment with the abstract content (mean 7.9 vs. 6.7, p-value <0.001) and appeal (mean 7.1 vs. 6.7, p-value <0.001) than human-written titles. The odds of preferring an AI-generated title were 1.7 times higher ( p-value =0.001), with 61.8% of 1,049 paired judgments favoring the AI version. Inter-rater agreement was moderate to substantial (Gwet’s AC: 0.54–0.70). Conclusions AI-generated titles were rated more favorably than human-written titles within the context of this study in terms of perceived alignment with the abstract content, appeal, and preference, suggesting that LLMs may enhance the effectiveness of scientific communication. These findings support the responsible integration of AI tools in research. READ ALL READ LESS Keywords AI, artificial intelligence, authorship, ChatGPT, comparison, rater, reader perception, scientific title, scientific writing, title Corresponding Author(s) Paul Sebo ( [email protected] ) Close Corresponding author: Paul Sebo Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Sebo P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Sebo P, Nie B and Wang T. Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.12688/f1000research.173647.2 ) First published: 30 Dec 2025, 14 :1470 ( https://doi.org/10.12688/f1000research.173647.1 ) Latest published: 05 Feb 2026, 14 :1470 ( https://doi.org/10.12688/f1000research.173647.2 ) Revised Amendments from Version 1 In this revised version, we addressed all reviewer comments and made several important methodological and conceptual clarifications. First, we corrected an inconsistency in the reported rating scale. The questionnaire used a 0–10 scale (not 1–10), and we have now standardized this throughout the manuscript. Second, we improved the statistical modeling. The previously used negative binomial models for rating outcomes have been replaced with mixed-effects ordinal logistic regression models, which better reflect the bounded ordinal nature of the data. Third, we expanded the description of the article sampling procedure and clarified the standardization of title formatting. Finally, we refined the conceptual framing and interpretation, including clearer wording around “perceived accuracy” (now explicitly defined as perceived alignment with the abstract), stronger emphasis on temporal and contextual limitations, and more cautious generalization of findings. In this revised version, we addressed all reviewer comments and made several important methodological and conceptual clarifications. First, we corrected an inconsistency in the reported rating scale. The questionnaire used a 0–10 scale (not 1–10), and we have now standardized this throughout the manuscript. Second, we improved the statistical modeling. The previously used negative binomial models for rating outcomes have been replaced with mixed-effects ordinal logistic regression models, which better reflect the bounded ordinal nature of the data. Third, we expanded the description of the article sampling procedure and clarified the standardization of title formatting. Finally, we refined the conceptual framing and interpretation, including clearer wording around “perceived accuracy” (now explicitly defined as perceived alignment with the abstract), stronger emphasis on temporal and contextual limitations, and more cautious generalization of findings. To read any peer review reports and author responses for this article, follow the "read" links in the Open Peer Review table. READ REVIEWER RESPONSES Introduction The title of a scientific article plays a critical role in academic communication. More than a simple label, it serves as the first point of contact between the research and its potential audience, potentially influencing whether the article is read, cited, or even submitted for peer review. Several studies have shown that titles affect readership and citation rates, 1 – 8 an effect that may be especially pronounced in high-impact journals, where competition for visibility is intense. A well-crafted title must strike a balance between scientific accuracy and appeal, providing a succinct yet informative summary of the study’s main objective or findings, while simultaneously engaging the curiosity of readers. 8 – 14 Crafting such titles is a complex task. Authors must condense their work into a limited number of words without compromising on clarity, scientific integrity, or appeal. The title must reflect the content of the study while remaining concise and readable. Moreover, researchers often face additional constraints such as journal-specific formatting rules, word limits, or stylistic preferences. 13 – 16 In this context, the choice of words and tone can affect how a study is perceived and disseminated across the scientific community. For example, titles that use assertive or attention-grabbing language may be more memorable or appealing, yet they risk overstating the results or introducing bias in interpretation. 17 , 18 Recent advancements in natural language processing (NLP) have opened new avenues in scientific writing. Large language models (LLMs) such as ChatGPT, developed by OpenAI, have demonstrated the ability to generate fluent, coherent, and contextually appropriate texts in response to user prompts. 19 – 29 These tools are increasingly being adopted to assist with various writing tasks, including summarization, translation, and scientific manuscript generation. While preliminary evidence suggests that LLMs can support academic writing tasks, their potential role in title generation remains largely unexplored. 30 , 31 Chen and Eger (2023) assessed the performance of transformer-based models—including ChatGPT—in generating scientific titles from abstracts in the domains of NLP and machine learning. 30 Their study focused on stylistic aspects such as humor and novelty, and introduced the first large-scale dataset of humorous scientific titles. Although certain models (e.g., BARTxsum) produced titles approaching human-level quality, effectively capturing authentic humor remained a notable challenge. Rehman et al. (2024) used multiple pre-trained language models to generate titles for biomedical research articles and compared them to human-written titles using standard textual similarity metrics such as ROUGE, BLEU, and METEOR. 31 The AI-generated titles showed high lexical similarity with human titles, suggesting that these models can replicate conventional title structures. However, the study relied exclusively on automated metrics, without assessing how readers actually perceive these titles in terms of accuracy, appeal, or credibility. Moreover, the articles used in their study were from the post-2020 era, raising the possibility that human-written titles may themselves have been influenced by AI-assisted tools. As a result, it remains unclear whether LLMs like ChatGPT can independently produce high-quality scientific titles that are preferred by human readers. Building on research showing that scientific titles may influence visibility, readership, and citation patterns, we extend this perspective to examine how AI-generated titles are perceived by human readers. In this context, the present study was designed to evaluate whether ChatGPT-4.0 can generate titles that are perceived as aligned with the abstract content (as a surrogate for perceived accuracy), appealing, and overall preferable compared to those written by human authors. Our study is unique in three main respects. First, it uses articles from a period before AI tools existed, ensuring that the original titles are purely human-authored. Second, it evaluates the quality of titles using human perceptions (rather than automated similarity metrics) on key dimensions of interest to readers. Third, it uses ChatGPT-4.0, one of the most advanced publicly available LLMs to date, as a title-generation tool in a zero-shot setting, reflecting its potential use by researchers without engineering expertise. We hypothesized that titles generated by ChatGPT would be perceived as better aligned with the abstract content and more appealing than those written by humans, and potentially preferred overall. Methods Study objective and design This study aimed to evaluate the capacity of ChatGPT-4.0 to produce scientific article titles that are accurate, i.e., well aligned with the abstract content, appealing, and preferred by readers. We compared AI-generated titles with original human-written titles drawn from high-impact journals in general internal medicine. Our objective was to assess whether ChatGPT could match or surpass human authors in crafting titles that attract readers’ interest while accurately reflecting the abstract. To this end, we conducted a cross-sectional survey in which independent academic raters evaluated paired titles for each of fifty scientific abstracts. Each abstract was presented with two titles, one written by a human, the other generated by ChatGPT, in randomized order to avoid bias. Journal and article selection We first identified the ten general internal medicine journals with the highest impact factors in the 2023 Journal Citation Reports (JCR). To ensure consistency and relevance across journals, only those fulfilling all of the following criteria were eligible: they had to regularly publish original research and/or systematic reviews; they had to use structured abstracts for both types of articles; and they had to have been in continuous publication since at least January 2000. The year 2000 was deliberately chosen as the target publication period because it predates the availability of generative AI tools, eliminating any possibility that the original titles were AI-assisted. Based on these criteria, the following journals were selected: The Lancet (IF 98.4) , The New England Journal of Medicine (IF 96.3) , The BMJ (IF 93.7) , JAMA (IF 63.5) , Archives of Internal Medicine (IF 22.3) , Annals of Internal Medicine (IF 19.6) , CMAJ (IF 12.9) , Journal of Travel Medicine (IF 9.1) , Journal of Internal Medicine (IF 9.0) , and Mayo Clinic Proceedings (IF 6.9). From each eligible journal, we randomly selected five articles published between January 1 and December 31, 2000. These articles were either original research studies or systematic reviews. This sampling strategy resulted in a total of fifty abstracts, each with a corresponding human-written title. For each journal, the sampling frame consisted of all eligible articles published in 2000 that met the inclusion criteria. Articles were assigned numeric identifiers and selected using a computer-generated random number sequence. AI-based title generation procedure To generate alternative titles, we used the ChatGPT-4.0 model developed by OpenAI, which represents one of the most advanced publicly available LLMs at the time of the study. For each abstract, we initiated a new chat session with the model. This was done intentionally to eliminate contextual memory carryover and ensure that each title was generated independently of the others. In each new session, the following standardized prompt was submitted: “ Write a title for this scientific article based on the abstract below ”. Immediately after entering the prompt, we pasted the full abstract of the selected article. The AI-generated title that resulted from this process was recorded verbatim and was not edited, reformulated, or shortened in any way by the researchers, except for standardizing capitalization: words were converted to lowercase when uppercase was not required (e.g., unless referring to names, countries, or other proper nouns). Capitalization was standardized across both human-written and AI-generated titles. This step was repeated for all fifty abstracts, yielding fifty unique AI-generated titles. The human-written and ChatGPT-generated titles are presented in the Supplementary Material. Pairing and randomization of titles Each abstract was thus associated with two titles: one written by the original human authors and the other generated by ChatGPT-4.0. For evaluation purposes, the two titles were assigned randomized positions as either “Title A” or “Title B” using a computer-generated random allocation procedure. This random order was intended to prevent raters from identifying which title had been written by a human and which by an AI, thereby minimizing bias during the evaluation process. Questionnaire development and rating criteria A structured evaluation questionnaire was developed to assess rater perceptions of the two titles accompanying each abstract. The survey presented all fifty abstracts, each introduced by two titles in randomized order (Title A and Title B), followed by the abstract itself. Each rater was asked to assess each title separately on two dimensions: first, how well the title represented the content of the abstract, and second, how much the title made them want to read the abstract or the full article. These two dimensions (i.e., perceived alignment with the abstract content and appeal) were each rated using an ordinal scale ranging from 0 to 10. On this scale, a rating of 0 indicated an extremely negative judgment (e.g., not accurate or not appealing at all), a rating of 5 reflected a neutral or moderate assessment, and a rating of 10 indicated a highly positive evaluation (e.g., perfectly accurate or extremely appealing). Perceived alignment reflects how well the title was judged to match the content of the abstract, rather than verification of the factual or methodological correctness of the study itself. After rating both titles on these two aspects, the raters were also asked to indicate which of the two titles they preferred overall, choosing either “Title A” or “Title B” for each abstract. The questionnaire and rating form are available in the Supplementary Material. Rater recruitment and blinding Twenty-one raters participated in the evaluation phase of the study. All were researchers who had authored at least one peer-reviewed academic publication. Eleven of these raters were recruited and contacted by one co-author (BN), and the remaining ten by another (PS), to ensure balanced recruitment. All participants provided informed consent in written electronic form (email agreement and completion of the questionnaire). To avoid bias and maintain ecological validity, raters were not informed that one of the two titles had been generated by AI. They were simply told that the study aimed to examine how different formulations of article titles affect readers’ perceptions. No specific mention was made of ChatGPT or AI-based generation to preserve the authenticity of the evaluations. Data collection timeline The process of generating AI-based titles was completed in May 2025. The rating process, during which the twenty-one recruited raters completed the questionnaire, was conducted throughout June 2025. All ratings were submitted electronically and compiled in a central database for further statistical analysis. Ethics and consent This study did not require ethics committee approval under Swiss law, as no personal health data were collected (Human Research Act, HRA, art.2). All participants were adult researchers, informed about the study’s purpose (evaluating perceptions of different title formulations), voluntary participation, and anonymized handling of responses. To minimize bias, they were not told that one of the titles was AI-generated. Written informed consent was obtained via email agreement and completion of the questionnaire. Statistical analysis For each title, we calculated the mean (standard deviation, SD) and median (interquartile range, IQR) of rater scores for perceived alignment and appeal. To compare ratings between human-written and AI-generated titles, we used the Wilcoxon signed-rank test for paired data, as the ratings were ordinal and not normally distributed. For title preferences, we calculated the proportion of times each title was selected. Differences in preference proportions were tested using McNemar’s test, which is appropriate for paired categorical data. In addition to these non-parametric tests, we conducted multilevel regression analyses to quantify effect sizes. Mixed-effects ordinal logistic regression models with random intercepts for both rater and article were used to compare perceived alignment and appeal ratings, yielding odds ratios (ORs). A mixed-effects logistic regression model with a random intercept for rater was used to assess the odds of preferring an AI-generated title over a human-written one. To assess inter-rater agreement, we computed two measures separately for AI-generated and human-written titles: percent agreement and Gwet’s agreement coefficient (AC), using quadratic weights to account for the ordinal nature of the 0–10 rating scale. 32 – 34 Agreement levels were computed across the 21 raters and stratified by rating dimension (perceived alignment and appeal). The weighted analysis assigns partial credit for near agreement, making it more appropriate for ordinal data. We interpreted Gwet’s AC using the classification proposed by Landis and Koch (1977): values <0.00 indicate poor agreement, 0.00–0.20 slight, 0.21–0.40 fair, 0.41–0.60 moderate, 0.61–0.80 substantial, and 0.81–1.00 almost perfect agreement. 35 We did not perform subgroup analyses based on rater characteristics, as the limited number of raters (N = 21) would not have allowed for statistically meaningful comparisons. All analyses were conducted using Stata version 15.1 (StataCorp, College Station, TX, USA). A two-sided p-value < 0.05 was considered statistically significant. Results Rater characteristics The main characteristics of the 21 raters who participated in the study are presented in Table 1 . Twelve were women and nine were men. Twelve were under 40 years of age, eight were between 40 and 60 years, and one was over 60 years old. The raters were primarily from China (n = 11) and Switzerland (n = 8), with one rater each from the United States and France. They had diverse academic and professional backgrounds. Among them, five specialized in library and information science, and seven in general internal medicine. Table 1. Characteristics of the 21 raters who evaluated 50 scientific titles from 10 high-impact general internal medicine journals. Rater ID Initials Gender Age group Work city Work country Discipline 1 Y.W. Male <40 Qingdao China General internal medicine 2 YC.B Female <40 Suzhou China Bioinformatics 3 MJ.G. Female 40-60 Hangzhou China Library and information science 4 B.Z. Female 40-60 Hangzhou China Arts 5 RD.J. Male 40-60 Hangzhou China International Chinese education 6 BF.S. Female <40 Hangzhou China Library and information science 7 CQ.W. Female <40 Hangzhou China Political economics 8 HS.X. Female <40 Guangzhou China Psychiatry 9 Y.L. Male <40 Guangzhou China Psychiatry 10 Y.W. Female <40 Guangzhou China Psychiatry 11 B.N. Female <40 Hangzhou China Library and information science 12 S.DL. Male 40-60 Geneva Switzerland General internal medicine 13 B.T. Male 40-60 Lyon France General internal medicine 14 A.M. Male <40 Geneva Switzerland Library and information science 15 M.B. Male 40-60 Geneva Switzerland General internal medicine and angiology 16 N.P. Male 40-60 Geneva Switzerland General internal medicine 17 C.K. Male >60 Geneva Switzerland Anaesthesia 18 N.W. Female <40 Zurich Switzerland General internal medicine and cardiology 19 L.M. Female <40 Geneva Switzerland General internal medicine 20 E.D. Female 40-60 Geneva Switzerland Public health 21 T.W. Female <40 Emporia USA Library and information science Perceived accuracy and appeal ratings For consistency with the original rating instrument, the term “perceived accuracy” is retained in this section. In the context of this study, this term refers to raters’ perceived alignment between the title and the abstract content, rather than verification of factual or methodological correctness. Table 2 presents the median, IQR, and minimum–maximum values of rater scores for perceived accuracy and appeal, stratified by title type (AI-generated vs. human-written) and by individual rater. Figures 1 and 2 display these distributions using boxplots, one per rater, for perceived accuracy and appeal, respectively. Overall, AI-generated titles received more favorable ratings. For perceived accuracy, 18 raters rated AI-generated titles higher than human-written titles, three gave equal ratings, and none rated AI-generated titles lower. For appeal, 12 raters rated AI-generated titles higher, five gave equal ratings, and four preferred human-written titles. Table 2. Summary of rater scores (median, IQR, min, max) for perceived accuracy and appeal by title type and rater ID, based on 50 scientific titles from 10 general internal medicine journals. Rater ID Title type Dimension Median P25 1 P75 1 Min Max 1 AI accuracy 9 8 9 6 10 1 AI appeal 8 7 9 6 10 1 Human accuracy 7 6 8 1 10 1 Human appeal 7 7 8 5 10 2 AI accuracy 9 8 10 5 10 2 AI appeal 9 7 9 5 10 2 Human accuracy 8 7 9 2 10 2 Human appeal 8 7 9 4 10 3 AI accuracy 9 9 10 5 10 3 AI appeal 8 7 9 6 10 3 Human accuracy 7 6 8 3 10 3 Human appeal 8 6 9 3 10 4 AI accuracy 10 10 10 7 10 4 AI appeal 10 9 10 7 10 4 Human accuracy 8 7 10 4 10 4 Human appeal 8 7 9 3 10 5 AI accuracy 10 10 10 5 10 5 AI appeal 10 10 10 5 10 5 Human accuracy 5 5 9 5 10 5 Human appeal 5 5 10 5 10 6 AI accuracy 10 10 10 8 10 6 AI appeal 10 8 10 6 10 6 Human accuracy 8 7 10 3 10 6 Human appeal 8 7 9 5 10 7 AI accuracy 7 6 8 3 9 7 AI appeal 6 5 6 3 8 7 Human accuracy 7 7 8 3 10 7 Human appeal 6 5 7 3 9 8 AI accuracy 6 4 6 2 9 8 AI appeal 5 3 6 1 9 8 Human accuracy 5 5 6 2 9 8 Human appeal 5 4 6 1 8 9 AI accuracy 7 6 7 4 9 9 AI appeal 4 3 5 2 8 9 Human accuracy 6 5 7 3 9 9 Human appeal 5 4 6 2 8 10 AI accuracy 7 6 8 3 9 10 AI appeal 5 4 7 3 8 10 Human accuracy 6 5 7 3 9 10 Human appeal 6 5 7 3 9 11 AI accuracy 9 8 9 6 10 11 AI appeal 8 7 9 6 9 11 Human accuracy 7 7 8 6 10 11 Human appeal 8 7 8 6 9 12 AI accuracy 8 8 9 5 9 12 AI appeal 7 6 8 2 10 12 Human accuracy 7 6 8 4 9 12 Human appeal 6 5 7 2 10 13 AI accuracy 7 5 8 4 9 13 AI appeal 7 6 8 4 8 13 Human accuracy 6 5 6 3 8 13 Human appeal 6 5 7 3 9 14 AI accuracy 6 5 7 2 8 14 AI appeal 5 4 6 1 8 14 Human accuracy 5 4 6 2 8 14 Human appeal 6 5 8 2 8 15 AI accuracy 8 6 9 2 10 15 AI appeal 7 5 8 3 10 15 Human accuracy 6 4 8 2 10 15 Human appeal 5 5 7 2 9 16 AI accuracy 7 7 8 5 8 16 AI appeal 7 6 8 5 8 16 Human accuracy 6 5 7 3 8 16 Human appeal 7 5 7 4 8 17 AI accuracy 7 5 8 3 10 17 AI appeal 6 5 8 3 10 17 Human accuracy 5 5 7 1 9 17 Human appeal 5 4 7 1 9 18 AI accuracy 8 7 9 5 10 18 AI appeal 8 6 9 3 10 18 Human accuracy 8 6 9 4 10 18 Human appeal 7 5 8 4 10 19 AI accuracy 9 8 10 6 10 19 AI appeal 8 6 9 4 10 19 Human accuracy 8 7 9 5 10 19 Human appeal 7 6 8 4 10 20 AI accuracy 8 6 8 5 10 20 AI appeal 7 6 8 4 8 20 Human accuracy 6 4 7 3 9 20 Human appeal 8 7 8 3 8 21 AI accuracy 10 10 10 7 10 21 AI appeal 10 9 10 7 10 21 Human accuracy 10 8 10 5 10 21 Human appeal 9 8 10 5 10 1 P25: 25th percentile. P75: 75th percentile. Each rater evaluated both AI-generated and human-written titles for perceived accuracy and appeal. Ratings range from 0 to 10. Figure 1. Boxplots showing perceived accuracy ratings for AI-generated and human-written titles for each of the 21 raters, based on 50 scientific titles from 10 high-impact general internal medicine journals. Figure 2. Boxplots showing appeal ratings for AI-generated and human-written titles for each of the 21 raters, based on 50 scientific titles from 10 high-impact general internal medicine journals. As summarized in Table 3 and visualized in Figure 3 , AI-generated titles received significantly higher scores. For perceived accuracy, the mean score was 7.9 for AI-generated titles compared to 6.7 for human-written titles, with a median of 8 versus 7 (p-value < 0.001). For appeal, the mean score was 7.1 for AI-generated titles versus 6.7 for human-written titles, with a median of 7 for both (p-value < 0.001). In multilevel models, AI-generated titles had higher odds of receiving higher ratings for perceived accuracy (OR 4.4, 95% CI 3.7–5.2; p-value < 0.001) and appeal (OR 1.7, 95% CI 1.5–2.0; p-value < 0.001) than human-written titles. Table 3. Perceived accuracy and appeal ratings, and title preferences, by title type, based on 4,196 ratings from 21 raters who evaluated 50 scientific titles from 10 high-impact general internal medicine journals. Number of ratings/total Mean (SD) Median (IQR) Min-max N (%) p-value OR (95% CI) p-value Perceived accuracy <0.001 1 <0.001 3 AI-generated title 1049/1050 7.9 (1.8) 8 (7-9) 2-10 4.4 (3.7-5.2) Human title 1049/1050 6.7 (1.9) 7 (5-8) 1-10 1 (ref ) Appeal <0.001 1 <0.001 3 AI-generated title 1049/1050 7.1 (2.1) 7 (6-9) 1-10 1.7 (1.5-2.0) Human title 1049/1050 6.7 (1.8) 7 (5-8) 1-10 1 (ref ) Preference <0.001 2 0.001 4 AI-generated title 1049/1050 648 (61.8) 1.7 (1.3-2.3) Human title 1049/1050 401 (38.2) 1 (ref ) 1 Wilcoxon signed-rank tests (paired, one rating per title per rater). 2 McNemar’s test (paired binary preferences, one per title per rater). 3 Odds ratios (ORs) and p-values are from mixed-effects ordinal logistic regression models with random intercepts for rater and article. 4 Odds ratio (OR) and p-value are from a mixed-effects logistic regression model with random intercept for rater. Figure 3. Boxplots showing perceived accuracy and appeal ratings for AI-generated and human-written titles by 21 raters, based on 50 scientific titles from 10 high-impact general internal medicine journals. Title preferences Overall preferences also favored AI-generated titles. As shown in Figure 4 , 16 out of 21 raters preferred AI-generated titles, while five preferred human-written ones. Among the 1,049 pairwise preference judgments (out of a possible 1,050; one missing value), 61.8% favored the AI-generated title and 38.2% favored the human-written title (p-value < 0.001; Table 3 ). The odds of preferring an AI-generated title were 1.7 times higher than those of preferring a human-written title (p-value = 0.001). Figure 4. Proportion of AI-generated title preferences for each of the 21 raters, based on 50 scientific titles from 10 high-impact general internal medicine journals. Inter-rater agreement Table 4 presents inter-rater agreement measures by title type. Percent agreement ranged from 88.9% to 92.5%, while Gwet’s ACs, calculated using quadratic weights for ordinal scales, ranged from 0.54 to 0.70. These values indicate moderate to substantial agreement according to the benchmark scale proposed by Landis and Koch (1977), 35 suggesting consistent tendencies across raters while also highlighting that judgments of title quality remain partly subjective. Table 4. Inter-rater agreement on perceived accuracy and appeal ratings, by title type, based on 4,196 ordinal ratings (scale 0–10) from 21 raters who evaluated 50 scientific titles from 10 high-impact general internal medicine journals, using quadratic weights. Dimension Title type Percent agreement (95% CI) p-value Gwet’s agreement coefficient (95% CI) p-value Accuracy AI-generated 0.8965 (0.8876-0.9055) <0.001 0.6141 (0.5741-0.6541) <0.001 Accuracy Human-written 0.9254 (0.9190-0.9318) <0.001 0.7029 (0.6715-0.7343) <0.001 Appeal AI-generated 0.8890 (0.8809-0.8971) <0.001 0.5378 (0.4964-0.5793) <0.001 Appeal Human-written 0.9198 (0.9123-0.9274) <0.001 0.6845 (0.6466-0.7223) <0.001 Discussion Summary of key findings This study evaluated how 21 raters assessed the perceived alignment between title and abstract, appeal, and overall preference of 50 scientific titles, comparing AI-generated and human-written versions. AI-generated titles received significantly higher ratings for both perceived alignment and appeal, with most raters favoring them over human-written alternatives. In total, 61.8% of preference judgments were in favor of AI-generated titles, and inter-rater agreement ranged from moderate to substantial. Comparison with literature Our findings are consistent with a growing body of literature suggesting that LLMs such as GPT-4.0 can generate high-quality scientific text that is often indistinguishable from human-written content. 20 , 36 – 42 Our results go beyond prior work by focusing specifically on titles, a concise yet crucial form of scientific communication. Unlike abstracts or full texts, titles must strike a balance between informativeness, clarity, and appeal in a highly constrained format. While some recent studies have explored AI-generated titles, they have either emphasized stylistic aspects such as humor and novelty in technical fields or evaluated output using only automated similarity metrics, without considering how human readers perceive title quality. 30 , 31 The fact that AI-generated titles scored higher on both perceived alignment with the abstract content and appeal challenges assumptions that LLMs lack the nuance or domain expertise to outperform human authors in such a delicate task. This suggests that LLMs may be particularly well suited for short-form scientific writing, where lexical clarity and stylistic optimization matter more than in-depth reasoning. Importantly, our study focused exclusively on articles from high-impact general internal medicine journals, where title quality is expected to be particularly high due to rigorous editorial and peer-review processes. If AI-generated titles can outperform those published in such venues, the gap may be even greater for titles in lower-tier journals, where writing quality is more variable. Future research should investigate whether similar results hold across different fields, disciplines, and levels of journal prestige. Collectively, our study complements and extends previous research by offering a detailed, comparative analysis of AI vs. human performance in scientific titling, a topic that has received relatively little empirical attention but has major implications for academic publishing practices. However, our findings should be interpreted primarily within contexts similar to those examined in our study (e.g., biomedical research evaluated by non-specialist academic readers). Implications for practice and research From a practical standpoint, the finding that AI-generated titles are rated more highly than human-written ones suggests that LLMs could be reliably used to assist researchers in generating or refining article titles. Given that titles play a key role in shaping reader perceptions, citation rates, and online discoverability, tools that enhance title quality could have a direct impact on dissemination and academic impact. In particular, researchers with limited writing experience or for whom English is not a first language might benefit from LLM-based titling tools to improve clarity and reader engagement. The observed preferences imply that AI-generated suggestions may outperform human intuition in specific aspects of scientific writing, such as title generation. This raises the possibility of integrating AI assistance more formally into journal workflows, for example through automated title suggestions during the submission process or editorial review. While this would require careful oversight, our data indicate that such tools would not compromise, and may even enhance, perceived quality. However, it is important to note that higher appeal or preference does not necessarily imply greater epistemic rigor. Titles optimized for engagement may emphasize clarity or assertiveness while potentially downplaying uncertainty or methodological nuance. Moreover, the integration of AI into scholarly communication also raises critical ethical questions. 29 , 43 – 46 These concerns echo ongoing debates about the role of LLMs in scientific authorship and the boundaries of acceptable assistance. Our findings underline the importance of maintaining transparent authorship practices and labeling AI contributions in scientific writing, even if such tools are only used to generate the title of the article. Beyond ethical issues, the widespread application of AI in generating titles may lead to homogenization in academic writing, resulting in titles that tend to fall within a narrow stylistic range and suppress the diversity, creativity, and uniqueness of the disciplines. These considerations relate to ongoing discussions in scholarly publishing regarding whether AI-assisted writing should be regarded as authorship, editorial assistance, or technical support, and how journals might operationalize transparent disclosure of AI use. From a research perspective, our study opens several avenues for further investigation. One important direction is to test the generalizability of these findings across disciplines, languages, and types of scientific content. It is possible that preferences for AI-generated titles vary depending on disciplinary norms or journal styles. In addition, future work could examine how title preferences correlate with actual article impact, such as downloads, citations, or Altmetric scores, to determine whether rater judgments align with broader readership behavior. Another key area for future research is to understand the mechanisms behind rater preferences. For example, are AI-generated titles preferred because of greater lexical simplicity, more direct structure, or the avoidance of technical jargon? Applying NLP tools to analyze linguistic features could shed light on what drives these preferences and help refine AI title generation even further. Lastly, as LLMs continue to evolve, longitudinal studies will be needed to assess how perceptions of AI-generated text change over time and whether improvements in model quality lead to higher standards or greater acceptance. Limitations This study has several limitations that should be acknowledged. First, although the use of articles from the year 2000 ensured that original titles were free from AI influence, it also introduces a potential temporal bias. Scientific writing conventions and stylistic preferences may have evolved over the past two decades, and what was considered an effective title in 2000 may differ from current standards. In other words, because AI-generated titles are produced by models trained largely on contemporary scientific language, differences may partly reflect shifts in stylistic conventions and reader expectations over time. Second, although we recruited raters with relevant academic experience, the sample size (N = 21) remains relatively small, and their subjective preferences may not fully represent broader readership or editorial perspectives. Third, while the zero-shot setting of ChatGPT-4.0 reflects real-world usage by non-expert users, it may not capture the full potential of LLMs when used with prompt optimization or human-in-the-loop refinement. Additionally, the evaluation focused on only two dimensions (i.e., perceived accuracy and appeal) along with an overall preference rating. Other important aspects of scientific titles, such as precision, cautiousness of claims, clarity, informativeness, tone, and appropriateness for indexing or search engine optimization, were not explicitly assessed. In particular, titles rated as more appealing may not necessarily reflect more rigorous or conservative scientific framing, and this potential trade-off warrants further investigation. Lastly, the study did not include domain experts for each article’s specific topic area, which may have influenced the ability of raters to judge how well a title reflected the article’s nuanced content. Future research could expand upon this work by including more diverse raters, evaluating newer articles, testing various prompting strategies, and incorporating additional dimensions of title quality. Despite these limitations, our findings provide valuable insights into the potential of LLMs to assist in academic title generation and highlight the subjective nature of title preferences. Conclusion In the context of high-impact general internal medicine journals, AI-generated scientific titles were rated more favorably than human-written titles from the year 2000 in terms of perceived alignment with the abstract content, appeal, and overall preference, with moderate to substantial agreement between raters. While these results reflect perceptions in this specific study context, they suggest that LLMs like GPT-4.0 are not only capable of producing linguistically fluent content but may also enhance key aspects of scientific communication within similar biomedical context. As AI tools become more integrated into the research and publishing process, there is a timely opportunity to harness their strengths while remaining attentive to ethical considerations, disciplinary norms, and the evolving expectations of scientific readers. Ethical approval Since this study did not involve the collection of personal health-related data it did not require ethical review, according to current Swiss law (Human Research Act, HRA, art.2). Data availability statement Underlying data Open Science Framework: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles. https://doi.org/10.17605/OSF.IO/NF8ZR 47 This project contains the following underlying data: • title_data_SM.xlsx – raw accuracy and appeal ratings for each title (AI vs. human) evaluated by 21 raters. Extended data Open Science Framework: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles. https://doi.org/10.17605/OSF.IO/NF8ZR 47 This project contains the following extended data: • title_chatgpt_SM.docx – table of the 50 original human-written titles and the 50 ChatGPT-generated titles, and the questionnaire used for rating accuracy, appeal, and preference. Data are available under the terms of the Creative Commons Attribution licence (CC-BY 4.0). Acknowledgements The authors would like to warmly thank the 21 raters for their valuable time and thoughtful evaluations of the 50 pairs of titles included in this study. References 1. Paiva CE, Lima JP d SN, Paiva BSR: Articles with short titles describing the results are cited more often. Clinics. 2012; 67 (5): 509–513. PubMed Abstract | Publisher Full Text | Free Full Text 2. Jacques TS, Sebire NJ: The Impact of Article Titles on Citation Hits: An Analysis of General and Specialist Medical Journals. JRSM Short Rep. 2010; 1 (1): 1–5. PubMed Abstract | Publisher Full Text | Free Full Text 3. Sagi I, Yechiam E: Amusing titles in scientific journals and article citation. J. Inf. Sci. 2008; 34 (5): 680–687. Publisher Full Text 4. Jamali HR, Nikzad M: Article title type and its relation with the number of downloads and citations. Scientometrics. 2011; 88 (2): 653–661. Publisher Full Text 5. Letchford A, Moat HS, Preis T: The advantage of short paper titles. R. Soc. Open Sci. 2015; 2 (8): 150266. PubMed Abstract | Publisher Full Text | Free Full Text 6. Rostami F, Mohammadpoorasl A, Hajizadeh M: The effect of characteristics of title on citation rates of articles. Scientometrics. 2014; 98 (3): 2007–2010. Publisher Full Text 7. Subotic S, Mukherjee B: Short and amusing: The relationship between title characteristics, downloads, and citations in psychology articles. J. Inf. Sci. 2014; 40 (1): 115–124. Publisher Full Text 8. Gichuki D: The Role of Scientific Titles in Shaping Research Visibility and Citations. Int. J. Res. Innov. Appl. Sci. 2024; IX (X): 511–515. Publisher Full Text 9. Lewison G, Hartley J: What’s in a title? Numbers of words and the presence of colons. Scientometrics. 2005; 63 (2): 341–356. Publisher Full Text 10. Chamorro-Padial J, Rodríguez-Sánchez R: The relevance of title, abstract, and keywords for scientific paper quality and potential impact. Multimed. Tools Appl. 2023; 82 (15): 23075–23090. Publisher Full Text 11. Tips for constructing an effective title. Nat. Biomed. Eng. 2022; 6 (2): 105–105. PubMed Abstract | Publisher Full Text 12. Pottier P, Lagisz M, Burke S, et al. : Title, abstract and keywords: a practical guide to maximize the visibility and impact of academic papers. Proc. R. Soc. B Biol. Sci. 2027; 291 (291): 20241222. PubMed Abstract | Publisher Full Text | Free Full Text 13. Tullu MS: Writing the title and abstract for a research paper: Being concise, precise, and meticulous is the key. Saudi J. Anaesth. 2019; 13 (Suppl 1): S12–S17. PubMed Abstract | Publisher Full Text | Free Full Text 14. Kretchmer: Title Optimization for Peer-Reviewed Journals: How to increase Citations. San Francisco Edit. June 25, 2024. Accessed June 7, 2025. Reference Source 15. Medical Writing 4th Edition: Cambridge University Press & Assessment. Accessed June 7, 2025. Reference Source 16. Hartley J: Current findings from research on structured abstracts. J. Med. Libr. Assoc. 2004; 92 (3): 368–371. PubMed Abstract 17. Vinkers CH, Tijdink JK, Otte WM: Use of positive and negative words in scientific PubMed abstracts between 1974 and 2014: retrospective analysis. BMJ. 2015; 351 : h6467. PubMed Abstract | Publisher Full Text | Free Full Text 18. Yavchitz A, Boutron I, Bafeta A, et al. : Misrepresentation of Randomized Controlled Trials in Press Releases and News Coverage: A Cohort Study. PLoS Med. 2012; 9 (9): e1001308. PubMed Abstract | Publisher Full Text | Free Full Text 19. Else H: Abstracts written by ChatGPT fool scientists. Nature. 2023; 613 (7944): 423. PubMed Abstract | Publisher Full Text 20. Gao CA, Howard FM, Markov NS, et al. : Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. NPJ Digit. Med. 2023; 6 (1): 75. PubMed Abstract | Publisher Full Text | Free Full Text 21. Biswas S: ChatGPT and the Future of Medical Writing. Radiology. 2023; 307 (2): e223312. Publisher Full Text 22. Salvagno M, Taccone FS, Gerli AG: Can artificial intelligence help for scientific writing? Crit. Care. 2023; 27 (1): 75. PubMed Abstract | Publisher Full Text | Free Full Text 23. Korinek A: Language Models and Cognitive Automation for Economic Research.February 2023. Publisher Full Text 24. Stokel-Walker C, Van Noorden R: What ChatGPT and generative AI mean for science. Nature. 2023; 614 (7947): 214–216. Publisher Full Text 25. Lund B, Ting W: Chatting about ChatGPT: How May AI and GPT Impact Academia and Libraries?January 22, 2023. Publisher Full Text 26. Liebrenz M, Schleifer R, Buadze A, et al. : Generating scholarly content with ChatGPT: ethical challenges for medical publishing. Lancet Digit. Health. 2023; 5 (3): e105–e106. PubMed Abstract | Publisher Full Text 27. Hosseini M, Horbach SPJM: Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other Large Language Models in scholarly peer review. Res. Sq. February 20, 2023; rs.3.rs-2587766. PubMed Abstract | Publisher Full Text | Free Full Text 28. Robertson Z: GPT4 is Slightly Helpful for Peer-Review Assistance: A Pilot Study.June 16, 2023. Publisher Full Text 29. Hosseini M, Rasmussen LM, Resnik DB: Using AI to write scholarly publications. Account. Res. 2024; 31 (7): 715–723. PubMed Abstract | Publisher Full Text | Free Full Text 30. Chen Y, Eger S: Transformers Go for the LOLs: Generating (Humourous) Titles from Scientific Abstracts End-to-End.December 26, 2023. Publisher Full Text 31. Rehman T, Sanyal DK, Chattopadhyay S: Can pre-trained language models generate titles for research papers?October 13, 2024. Publisher Full Text 32. Klein D: Implementing a General Framework for Assessing Interrater Agreement in Stata. Stata J. 2018; 18 (4): 871–901. Publisher Full Text 33. Gwet KL: Handbook of Inter-Rater Reliability: The Definitive Guide to Measuring the Extent of Agreement among Raters. Advances Analytics, LLC; Fourth ed. 2014. 34. Sebo P, de Lucia S : Performance of machine translators in translating French medical research abstracts to English: A comparative study of DeepL, Google Translate, and CUBBITT. PloS One. 2024; 19 (2): e0297183. PubMed Abstract | Publisher Full Text | Free Full Text 35. Landis JR, Koch GG: The measurement of observer agreement for categorical data. Biometrics. 1977; 33 (1): 159–174. Publisher Full Text 36. Weber-Wulff D, Anohina-Naumeca A, Bjelobaba S, et al. : Testing of detection tools for AI-generated text. Int. J. Educ. Integr. 2023; 19 (1): 1–39. Publisher Full Text 37. Walters WH: The Effectiveness of Software Designed to Detect AI-Generated Writing: A Comparison of 16 AI Text Detectors. Open. Inf. Sci. 2023; 7 (1). Publisher Full Text 38. Clark E, August T, Serrano S, et al. : All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text. Zong C, Xia F, Li W, et al. , editors. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics; 2021; 7282–7296. Publisher Full Text 39. Else H: ‘Tortured phrases’ give away fabricated research papers. Nature. 2021; 596 (7872): 328–329. PubMed Abstract | Publisher Full Text 40. AL-Rawas M, Qader OAJA, Othman NH, et al. : Identification of dental related ChatGPT generated abstracts by senior and young academicians versus artificial intelligence detectors and a similarity detector. Sci. Rep. 2025; 15 (1): 11275. PubMed Abstract | Publisher Full Text | Free Full Text 41. Nabata KJ, AlShehri Y, Mashat A, et al. : Evaluating human ability to distinguish between ChatGPT-generated and original scientific abstracts. Updat. Surg. January 24, 2025; 77 : 615–621. PubMed Abstract | Publisher Full Text 42. Caiado AJA, Hahsler M: AI Content Self-Detection for Transformer-based Large Language Models.December 28, 2023. Publisher Full Text 43. Science journals set new authorship guidelines for AI-generated textNational Institute of Environmental Health Sciences; Accessed June 13, 2025. Reference Source 44. Resnik DB, Hosseini M: Disclosing artificial intelligence use in scientific research and publication: When should disclosure be mandatory, optional, or unnecessary? Account. Res. 1–13. PubMed Abstract | Publisher Full Text | Free Full Text 45. Lund BD, Naheem KT: Can ChatGPT be an author? A study of artificial intelligence authorship policies in top academic journals. Learn Publ. 2024; 37 (1): 13–21. Publisher Full Text 46. Lund B, Ting W, Mannuru NR, et al. : ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing.March 15, 2023. Publisher Full Text 47. Sebo P, Nie B, Wang T: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles. Open Science Framework. 2025. Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 30 Dec 2025 ADD YOUR COMMENT Comment Author details Author details 1 University Institute for Primary Care, University of Geneva, Geneva, Switzerland 2 Zhejiang Tongji Vocational College of Science and Technology, Hangzhou, Zhejiang, China 3 School of Library and Information Management, Emporia State University, Emporia, Kansas, USA Paul Sebo Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation Bing Nie Roles: Conceptualization, Data Curation, Methodology, Project Administration Ting Wang Roles: Conceptualization, Data Curation, Methodology, Project Administration Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 05 Feb 2026, 14:1470 https://doi.org/10.12688/f1000research.173647.2 version 1 Published: 30 Dec 2025, 14:1470 https://doi.org/10.12688/f1000research.173647.1 Copyright © 2026 Sebo P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Sebo P, Nie B and Wang T. Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.12688/f1000research.173647.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 05 Feb 2026 Revised Views 0 Cite How to cite this report: Gabay RA. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r455891 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-455891 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Mar 2026 Renz Alvin Gabay , Sorsogon State University, Sorsogon, Philippines Approved VIEWS 0 https://doi.org/10.5256/f1000research.196161.r455891 No ... Continue reading READ ALL No further comments. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Science Education, Physics Education, AI in Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Gabay RA. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r455891 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-455891 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: A. Funa A. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r455890 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-455890 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 12 Mar 2026 Aaron A. Funa , Sorsogon State University, Sorsogon, Philippines Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.196161.r455890 The manuscript is timely and well executed overall, but several points still require clarification. First, the authors should clarify whether any co-authors were included among the 21 raters, as the initials and affiliations reported in Table 1 appear to overlap ... Continue reading READ ALL The manuscript is timely and well executed overall, but several points still require clarification. First, the authors should clarify whether any co-authors were included among the 21 raters, as the initials and affiliations reported in Table 1 appear to overlap with the author list despite the Methods describing the raters as independent. Second, the statistical rationale for the preference model should be explained more clearly, particularly why the mixed-effects model appears to include a random intercept for rater but not article. Third, the conclusions should remain closely aligned with what was actually measured, namely perceived alignment with the abstract, appeal, and overall preference, rather than implying broader superiority of AI-generated titles. Finally, the comparison between AI-generated titles from 2025 and human-written titles from 2000 should be emphasized as a central interpretive limitation, since part of the observed advantage may reflect changing title conventions over time. Overall, these issues appear to require clarification rather than major redesign, and the study remains a well-conceived and valuable contribution. Competing Interests: No competing interests were disclosed. Reviewer Expertise: STEM Education; Science Education; Nursing Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT A. Funa A. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r455890 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-455890 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Aini N. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r458467 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-458467 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 12 Mar 2026 Nurul Aini , English Education, Faculty of Teacher and Training, UIN Syekh Wasil Kediri, Kediri, East Java, Indonesia Approved VIEWS 0 https://doi.org/10.5256/f1000research.196161.r458467 Feedbacks: Since it focuses on academic writing (scientific) but in the scope of general internal medicine, in the introduction part, it is very urgent to activate the readers’ prior knowledge related to those 2 things. ... Continue reading READ ALL Feedbacks: Since it focuses on academic writing (scientific) but in the scope of general internal medicine, in the introduction part, it is very urgent to activate the readers’ prior knowledge related to those 2 things. Write sentences showing the information related to medical things to avoid ambiguity. Locus and scope of the research are essential to bring the readers’ prior knowledge to the same page. My first impression, reading the introduction part, is that I relate to academic writing in the education area. Explore more issues of academic writing in the medical area. We randomly selected five articles published between January 1 and December 31, 2000. To minimize the subjectivity and raise the objectivity, give a critical argument of why selecting those articles in 2000 (although the limitation is already stated but highlighting the reasons makes the article more academically sound). Why not the latest updated article, considering the novelty? Survey: give detailed information on the total number of items (how many items being asked). Was it self-made? Adopted or adapted from the expert? From what framework was the survey developed? Was there any pilot testing of the instrument before a real one? Did you use any validity and reliability or things such so? Also, where did you take the scaling criteria of 0-10 from (mention the framework/expert) to strengthen the theory. Be consistent in using the term fifty articles and 50 articles (numbering and letter). In addition, future work could examine how title preferences correlate with actual article impact, such as downloads, citations, or Altmetric scores, to determine whether rater judgments align with broader readership behavior. Another key area for future research is to understand the mechanisms behind rater preferences (in implication). Future research could expand upon this work by including more diverse raters, evaluating newer articles, testing various prompting strategies, and incorporating additional dimensions of title quality. Despite these limitations, our findings provide valuable insights into the potential of LLMs to assist in academic title generation and highlight the subjective nature of title preferences (in limitations). Are they in line? Who not stating in one part by simplifying the statements as both are targeted to the future researchers. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Technology, Artificial Intelligence in Education, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Aini N. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r458467 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-458467 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Mali YCG. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r458463 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-458463 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 05 Mar 2026 Yustinus Calvin Gai Mali , Universitas Kristen Satya Wacana, Salatiga, Indonesia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.196161.r458463 This article is well written and presents an interesting discussion on AI's ability to generate journal article titles. To enhance the manuscript, the authors should clarify the importance of conducting this study in the introduction and include a paragraph outlining ... Continue reading READ ALL This article is well written and presents an interesting discussion on AI's ability to generate journal article titles. To enhance the manuscript, the authors should clarify the importance of conducting this study in the introduction and include a paragraph outlining the practical benefits of the study, specifying who will benefit most. Moreover, the methods section needs clarification, including why the 2023 JCR was chosen over the most recent edition. To improve transparency, the authors could share samples of their ChatGPT interactions using the standardized prompt: write a title for this scientific article based on the abstract below. This would help readers learn from their approach and apply it in their research. For statistical analysis, the authors should cite one or two prior studies that used similar tests on comparable datasets. Finally, given the prevalence of AI, the authors should consider adding a Gen AI Statement to disclose any AI tools used to write or improve the grammar of their manuscript. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Educational Technology, English Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Mali YCG. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r458463 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-458463 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Ben Saad H. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r458465 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-458465 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 04 Mar 2026 Helmi Ben Saad , Faculty of Medicine of Sousse, Université de Sousse, Sousse, Tunisia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.196161.r458465 I read with a great pleasure the interesting paper titled: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles. Below is my detailed peer-review report. 1. Strengths The paper has some strengths. ... Continue reading READ ALL I read with a great pleasure the interesting paper titled: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles. Below is my detailed peer-review report. 1. Strengths The paper has some strengths. First, it is a timely and original research question. The study addresses an emerging and highly relevant issue in scientific publishing. Second, it has a rigor methodological (blinded paired comparison, randomized title order, mixed-effects modeling accounting for rater and article clustering, and appropriate use of ordinal logistic regression for bounded scales). Third, it used pre-AI articles (year 2000), which is a major strength, ensuring that original titles were not AI-assisted. Fourth, it opted for a Human-centered evaluation: unlike prior studies relying on automated metrics, this study evaluates human perception. Fifth, the authors opted for transparency (open data availability (OSF), clear description of statistical methods, and detailed reporting of inter-rater agreement). 2. Limitations The paper has some limitations, which should be reported inside the paper. First, there is a temporal bias: titles from 2000 may not reflect current stylistic norms. AI models trained on contemporary data may therefore have an inherent stylistic advantage. Second, there is a small and geographically clustered rater sample: N = 21 is modest, majority f researchers were from China and Switzerland, and limited representation of broader editorial communities). Third, raters were non-specialist raters (raters were not necessarily domain experts for each article topic, potentially limiting nuanced assessment of title–abstract alignment). Sixth, there is a restricted outcome dimensions, since only perceived alignment, appeal, and preference were assessed. Important aspects were not evaluated such as epistemic caution, overstatement risk, precision of claims, indexing optimization, and ethical tone. Fifth, a single-model evaluation was used: only GPT-4.0 was tested. No comparison with other LLMs or prompt engineering strategies. Finally, there is a zero-shot prompting: While ecologically valid, it may underestimate the true potential of LLM-assisted titling. 3. Title The current title ( Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles ) is clear, engaging, and accurately reflects the study’s central research question and comparative design. It appropriately highlights the intervention (ChatGPT), the outcome (quality of scientific titles), the comparator (human-written titles), and the study type (comparative evaluation). However, the term “better” is inherently subjective. Since the study evaluates perceived alignment, appeal, and preference . It may be preferable to reflect this more explicitly to avoid overgeneralization. I suggest this long and more precise title: Perceived Alignment, Appeal, and Preference of Artificial Intelligence Generated versus Human-Written Scientific Titles: A Blinded Comparative Study Using GPT-4.0 4. Abstract The abstract is well structured (Background, Objective, Methods, Results, Conclusions). It adheres to conventional scientific reporting standards. It clearly states rationale and gap in the literature, describes study design, sampling frame, and statistical approach, reports key quantitative results (means, p-values, ORs). Its conclusions are generally aligned with findings. However, some points need improvement. First, the term “better” is implied in the results and conclusions but should be contextualized as perceived superiority . Second, clarify that “alignment” refers to perceived alignment rather than objective accuracy. Third, avoid abbreviations misuse: define AI, GPT, AC at first use. 5. Keywords Opt for MeSH terms Classify keywords in alphabetical order Avoid citing as keywords terms used in the title or the abstract 6. Ethical guidelines The manuscript states that: No personal health data were collected, ethics approval was not required under Swiss law (HRA art. 2), and raters were informed and participated voluntarily. This appears compliant with local legal requirements. However, the authors may consider: Explicitly stating whether institutional review board consultation was sought or formally waived, and clarifying whether deception (non-disclosure of AI generation) was ethically reviewed or justified. 7. Figures and tables Figures: Can authors consider adding effect size visualization (e.g., forest plot of ORs). Figures: Define all used abbreviations (ID AI ) at the bottom of the table. Tables: Minor suggestions: Table 2 may be excessively granular for main text; consider moving full version to Supplementary Material, Ensure consistent formatting of ORs and Cis, and Standardize decimal places throughout. Tables: Define all used abbreviations (AI IQR SD OR CI) at the bottom of the table. 8. References Some web-based references may require access dates standardized. Avid references resented as preprints and arXiv citations. Rectify references 23, 25, 28, 30, 31, 42, 43, 46, ??? 9. Use of AI chatbots during preparation The manuscript evaluates ChatGPT but does not clearly state whether AI tools were used in writing the manuscript itself. Given the subject matter, transparency is essential. I recommend adding a dedicated statement related to the use of AI during the writing of the paper. 10. Additional remarks and suggestions 10.1. Conceptual clarification The manuscript carefully reframes “accuracy” as “perceived alignment.” This is appropriate. Continue emphasizing that this is a perceptual, not epistemic, superiority. 10.2. Overinterpretation risk The conclusion should avoid implying that AI titles are objectively superior. Suggested wording: Replace “may enhance scientific communication” with “may enhance perceived clarity and appeal under specific conditions.” 10.3. Statistical interpretation The OR of 4.4 for perceived alignment is substantial. Consider discussing: Practical magnitude, and whether effect sizes correspond to meaningful real-world differences. 10.4. Potential biases Cultural differences in stylistic preferences may influence ratings. LLM training bias toward assertive phrasing could affect appeal ratings. 10.5. Writing and organization Slight reduction of repetition in discussion. Tightening of some longer paragraphs for readability. 10.6. Misuse of references Several sentences are lacking references: in the introduction section add references after: communication (L1), for peer review (L2), is intense (L4), complex task, or appeal, readable, community, scientific writing, generation, Chen and Erger, 10.7. Misuse of abbreviations Define abbreviations at first use: see ChatGPT in the introduction, 10.8. Article structure Objectives must be reported at the end of the introduction not in the beginning of the methods section. The paragraph (in the results section) named “Perceived accuracy and appeal ratings” should be moved to the methods section. 10.9. Sample size Authors are asked to calculate the sample size: why 21? Why not 10 or 100? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Medical writing, Use of AI, respiratory physiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ben Saad H. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r458465 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-458465 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 30 Dec 2025 Views 0 Cite How to cite this report: Gabay RA. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.191483.r446770 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v1#referee-response-446770 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 29 Jan 2026 Renz Alvin Gabay , Sorsogon State University, Sorsogon, Philippines Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.191483.r446770 Overall Assessment This manuscript addresses a timely and relevant question: whether large language models (LLMs), specifically ChatGPT-4.0, can generate scientific titles that are perceived as more accurate, appealing, and preferable than human-written titles. The study is clearly written, methodologically ... Continue reading READ ALL Overall Assessment This manuscript addresses a timely and relevant question: whether large language models (LLMs), specifically ChatGPT-4.0, can generate scientific titles that are perceived as more accurate, appealing, and preferable than human-written titles. The study is clearly written, methodologically transparent, and thoughtfully situated within the emerging literature on AI-assisted academic writing. The use of blinded human raters, articles published before the advent of generative AI, and multiple statistical approaches are notable strengths. At the same time, several aspects of the design, interpretation, and framing would benefit from clarification or refinement. Most of these do not undermine the core findings but would strengthen the rigor, scope, and interpretability of the study. With revisions, this work has strong potential to make a meaningful contribution to the literature on AI in scientific communication. 1. Conceptualization of “Perceived Accuracy” Strength: The authors appropriately focus on reader-centered evaluation rather than automated metrics, which is a valuable contribution compared to prior studies. Suggestion: The construct of “perceived accuracy” would benefit from clearer conceptual framing. Because raters evaluated titles based only on the abstract (and were not domain experts for each topic), the measure appears to capture perceived alignment between title and abstract rather than factual or methodological accuracy of the study itself. The authors may consider clarifying this distinction throughout the manuscript. Rephrasing some claims to emphasize perceived representativeness or abstract–title alignment would improve conceptual precision. 2. Use of Articles from the Year 2000 Strength: Selecting pre-AI articles is an elegant design choice that convincingly eliminates the possibility of AI-assisted human titles. Suggestion: At the same time, this introduces a potential temporal effect: writing conventions, stylistic norms, and reader expectations may have changed over 25 years. AI-generated titles, trained on modern scientific language, may naturally align better with contemporary preferences. The authors acknowledge this as a limitation, but it may merit stronger emphasis in both the Discussion and Conclusion. Slightly tempering general claims (e.g., “AI-generated titles can surpass human-written titles”) to reflect this context would enhance interpretive balance. 3. Scope and Generalizability Strength: The study design is well-controlled within a clearly defined domain (high-impact general internal medicine journals), which enhances internal validity. Suggestion: Some discussion sections extend the implications to scientific communication broadly, including lower-tier journals and other disciplines. The authors might consider more explicitly limiting generalizations to similar contexts (e.g., biomedical research, non-specialist academic readers). Framing broader claims as hypotheses for future research would maintain scholarly caution while preserving the manuscript’s relevance. 4. Interpretation of “Preference” as Title Quality Strength: Using overall preference as an outcome is intuitive and directly relevant to how readers interact with scientific articles. Suggestion: Preference, appeal, and perceived accuracy are inherently subjective constructs and may not fully capture other dimensions of title quality, such as precision, cautiousness of claims, or indexing suitability. The Discussion could benefit from acknowledging that higher appeal does not always equate to higher epistemic rigor. A brief reflection on possible trade-offs (e.g., rhetorical optimization versus conservative scientific framing) would enrich the interpretive depth. 5. Modeling of Ordinal Data Strength: The authors appropriately use non-parametric tests for paired comparisons and apply multilevel models to account for clustering by rater. Suggestion: The use of negative binomial regression for 0–10 ordinal ratings could be more fully justified, as these scores are not count data in the conventional sense. Providing a short rationale for this choice, or referencing prior studies that have used similar approaches, would strengthen methodological transparency. Alternatively, mentioning that the main conclusions were consistent across analytic approaches (if true) would reassure readers. 6. Inter-Rater Agreement Strength: Reporting both percent agreement and Gwet’s AC with weighted coefficients is commendable and demonstrates careful attention to reliability. Suggestion: Given that agreement was in the moderate-to-substantial range, the manuscript might briefly note that judgments of title quality remain partly subjective. Framing the findings as reflecting consistent tendencies rather than unanimous consensus would appropriately contextualize the results. 7. Ethics and Responsible Use of AI Strength: The manuscript commendably addresses ethical issues such as transparency, authorship, and the potential homogenization of scientific writing. Suggestion: This section could be modestly expanded to engage more directly with current debates in scholarly publishing, such as: whether AI-assisted title generation constitutes authorship, editing, or technical assistance; how journals might operationalize disclosure of AI use. This would strengthen the practical relevance of the study. 8. Literature balance: The Introduction provides a strong overview of recent studies on AI-assisted scientific writing, which effectively situates the work within the current technological discourse. To further strengthen the theoretical grounding, the authors may consider incorporating additional literature on scientific title construction, rhetorical framing, and scientometrics from information science and bibliometrics. Integrating these perspectives could enrich the conceptual framework and more firmly anchor the study in the broader scholarship on how titles function in academic communication. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Science Education, Physics Education, AI in Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Gabay RA. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.191483.r446770 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v1#referee-response-446770 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: A. Funa A. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.191483.r446771 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v1#referee-response-446771 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 29 Jan 2026 Aaron A. Funa , Sorsogon State University, Sorsogon, Philippines Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.191483.r446771 The study addresses a timely question and uses an appropriate blinded paired-title design: 50 abstracts from 10 high-impact general internal medicine journals (year 2000) were retitled with GPT-4.0, and 21 researchers rated each human vs AI title for accuracy and ... Continue reading READ ALL The study addresses a timely question and uses an appropriate blinded paired-title design: 50 abstracts from 10 high-impact general internal medicine journals (year 2000) were retitled with GPT-4.0, and 21 researchers rated each human vs AI title for accuracy and appeal (0–10) and chose an overall preference. The work is generally clear, technically sound, and supported by open underlying/extended data. However, two points must be fixed for scientific soundness: (1) correct the inconsistency in the reported rating scale (0–10 vs 1–10) and confirm the coding used in all analyses; and (2) reconsider modeling bounded ordinal ratings with negative binomial regression. An ordinal mixed-effects model (or a clearly justified alternative with sensitivity analyses) would better match the outcome type and should explicitly account for repeated measures across raters and articles. Additional improvements: specify the random article-selection procedure (sampling frame, method/seed) and standardize formatting (e.g., capitalization) for both title types to avoid confounding. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: STEM Education; Science Education; Nursing Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT A. Funa A. Reviewer Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.191483.r446771 ) The direct URL for this report is: https://f1000research.com/articles/14-1470/v1#referee-response-446771 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 30 Dec 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 5 Version 2 (revision) 05 Feb 26 read read read read read Version 1 30 Dec 25 read read Aaron A. Funa , Sorsogon State University, Sorsogon, Philippines Renz Alvin Gabay , Sorsogon State University, Sorsogon, Philippines Helmi Ben Saad , Université de Sousse, Sousse, Tunisia Yustinus Calvin Gai Mali , Universitas Kristen Satya Wacana, Salatiga, Indonesia Nurul Aini , UIN Syekh Wasil Kediri, Kediri, Indonesia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Gabay R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Mar 2026 | for Version 2 Renz Alvin Gabay , Sorsogon State University, Sorsogon, Philippines 0 Views copyright © 2026 Gabay R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions No further comments. Competing Interests No competing interests were disclosed. Reviewer Expertise Science Education, Physics Education, AI in Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Gabay RA. Peer Review Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r455891) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-455891 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 A. Funa A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 Mar 2026 | for Version 2 Aaron A. Funa , Sorsogon State University, Sorsogon, Philippines 0 Views copyright © 2026 A. Funa A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript is timely and well executed overall, but several points still require clarification. First, the authors should clarify whether any co-authors were included among the 21 raters, as the initials and affiliations reported in Table 1 appear to overlap with the author list despite the Methods describing the raters as independent. Second, the statistical rationale for the preference model should be explained more clearly, particularly why the mixed-effects model appears to include a random intercept for rater but not article. Third, the conclusions should remain closely aligned with what was actually measured, namely perceived alignment with the abstract, appeal, and overall preference, rather than implying broader superiority of AI-generated titles. Finally, the comparison between AI-generated titles from 2025 and human-written titles from 2000 should be emphasized as a central interpretive limitation, since part of the observed advantage may reflect changing title conventions over time. Overall, these issues appear to require clarification rather than major redesign, and the study remains a well-conceived and valuable contribution. Competing Interests No competing interests were disclosed. Reviewer Expertise STEM Education; Science Education; Nursing Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) A. Funa A. Peer Review Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r455890) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-455890 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Aini N. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 Mar 2026 | for Version 2 Nurul Aini , English Education, Faculty of Teacher and Training, UIN Syekh Wasil Kediri, Kediri, East Java, Indonesia 0 Views copyright © 2026 Aini N. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Feedbacks: Since it focuses on academic writing (scientific) but in the scope of general internal medicine, in the introduction part, it is very urgent to activate the readers’ prior knowledge related to those 2 things. Write sentences showing the information related to medical things to avoid ambiguity. Locus and scope of the research are essential to bring the readers’ prior knowledge to the same page. My first impression, reading the introduction part, is that I relate to academic writing in the education area. Explore more issues of academic writing in the medical area. We randomly selected five articles published between January 1 and December 31, 2000. To minimize the subjectivity and raise the objectivity, give a critical argument of why selecting those articles in 2000 (although the limitation is already stated but highlighting the reasons makes the article more academically sound). Why not the latest updated article, considering the novelty? Survey: give detailed information on the total number of items (how many items being asked). Was it self-made? Adopted or adapted from the expert? From what framework was the survey developed? Was there any pilot testing of the instrument before a real one? Did you use any validity and reliability or things such so? Also, where did you take the scaling criteria of 0-10 from (mention the framework/expert) to strengthen the theory. Be consistent in using the term fifty articles and 50 articles (numbering and letter). In addition, future work could examine how title preferences correlate with actual article impact, such as downloads, citations, or Altmetric scores, to determine whether rater judgments align with broader readership behavior. Another key area for future research is to understand the mechanisms behind rater preferences (in implication). Future research could expand upon this work by including more diverse raters, evaluating newer articles, testing various prompting strategies, and incorporating additional dimensions of title quality. Despite these limitations, our findings provide valuable insights into the potential of LLMs to assist in academic title generation and highlight the subjective nature of title preferences (in limitations). Are they in line? Who not stating in one part by simplifying the statements as both are targeted to the future researchers. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Technology, Artificial Intelligence in Education, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Aini N. Peer Review Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r458467) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-458467 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Mali Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 05 Mar 2026 | for Version 2 Yustinus Calvin Gai Mali , Universitas Kristen Satya Wacana, Salatiga, Indonesia 0 Views copyright © 2026 Mali Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This article is well written and presents an interesting discussion on AI's ability to generate journal article titles. To enhance the manuscript, the authors should clarify the importance of conducting this study in the introduction and include a paragraph outlining the practical benefits of the study, specifying who will benefit most. Moreover, the methods section needs clarification, including why the 2023 JCR was chosen over the most recent edition. To improve transparency, the authors could share samples of their ChatGPT interactions using the standardized prompt: write a title for this scientific article based on the abstract below. This would help readers learn from their approach and apply it in their research. For statistical analysis, the authors should cite one or two prior studies that used similar tests on comparable datasets. Finally, given the prevalence of AI, the authors should consider adding a Gen AI Statement to disclose any AI tools used to write or improve the grammar of their manuscript. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Educational Technology, English Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Mali YCG. Peer Review Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r458463) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-458463 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Ben Saad H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 04 Mar 2026 | for Version 2 Helmi Ben Saad , Faculty of Medicine of Sousse, Université de Sousse, Sousse, Tunisia 0 Views copyright © 2026 Ben Saad H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I read with a great pleasure the interesting paper titled: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles. Below is my detailed peer-review report. 1. Strengths The paper has some strengths. First, it is a timely and original research question. The study addresses an emerging and highly relevant issue in scientific publishing. Second, it has a rigor methodological (blinded paired comparison, randomized title order, mixed-effects modeling accounting for rater and article clustering, and appropriate use of ordinal logistic regression for bounded scales). Third, it used pre-AI articles (year 2000), which is a major strength, ensuring that original titles were not AI-assisted. Fourth, it opted for a Human-centered evaluation: unlike prior studies relying on automated metrics, this study evaluates human perception. Fifth, the authors opted for transparency (open data availability (OSF), clear description of statistical methods, and detailed reporting of inter-rater agreement). 2. Limitations The paper has some limitations, which should be reported inside the paper. First, there is a temporal bias: titles from 2000 may not reflect current stylistic norms. AI models trained on contemporary data may therefore have an inherent stylistic advantage. Second, there is a small and geographically clustered rater sample: N = 21 is modest, majority f researchers were from China and Switzerland, and limited representation of broader editorial communities). Third, raters were non-specialist raters (raters were not necessarily domain experts for each article topic, potentially limiting nuanced assessment of title–abstract alignment). Sixth, there is a restricted outcome dimensions, since only perceived alignment, appeal, and preference were assessed. Important aspects were not evaluated such as epistemic caution, overstatement risk, precision of claims, indexing optimization, and ethical tone. Fifth, a single-model evaluation was used: only GPT-4.0 was tested. No comparison with other LLMs or prompt engineering strategies. Finally, there is a zero-shot prompting: While ecologically valid, it may underestimate the true potential of LLM-assisted titling. 3. Title The current title ( Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles ) is clear, engaging, and accurately reflects the study’s central research question and comparative design. It appropriately highlights the intervention (ChatGPT), the outcome (quality of scientific titles), the comparator (human-written titles), and the study type (comparative evaluation). However, the term “better” is inherently subjective. Since the study evaluates perceived alignment, appeal, and preference . It may be preferable to reflect this more explicitly to avoid overgeneralization. I suggest this long and more precise title: Perceived Alignment, Appeal, and Preference of Artificial Intelligence Generated versus Human-Written Scientific Titles: A Blinded Comparative Study Using GPT-4.0 4. Abstract The abstract is well structured (Background, Objective, Methods, Results, Conclusions). It adheres to conventional scientific reporting standards. It clearly states rationale and gap in the literature, describes study design, sampling frame, and statistical approach, reports key quantitative results (means, p-values, ORs). Its conclusions are generally aligned with findings. However, some points need improvement. First, the term “better” is implied in the results and conclusions but should be contextualized as perceived superiority . Second, clarify that “alignment” refers to perceived alignment rather than objective accuracy. Third, avoid abbreviations misuse: define AI, GPT, AC at first use. 5. Keywords Opt for MeSH terms Classify keywords in alphabetical order Avoid citing as keywords terms used in the title or the abstract 6. Ethical guidelines The manuscript states that: No personal health data were collected, ethics approval was not required under Swiss law (HRA art. 2), and raters were informed and participated voluntarily. This appears compliant with local legal requirements. However, the authors may consider: Explicitly stating whether institutional review board consultation was sought or formally waived, and clarifying whether deception (non-disclosure of AI generation) was ethically reviewed or justified. 7. Figures and tables Figures: Can authors consider adding effect size visualization (e.g., forest plot of ORs). Figures: Define all used abbreviations (ID AI ) at the bottom of the table. Tables: Minor suggestions: Table 2 may be excessively granular for main text; consider moving full version to Supplementary Material, Ensure consistent formatting of ORs and Cis, and Standardize decimal places throughout. Tables: Define all used abbreviations (AI IQR SD OR CI) at the bottom of the table. 8. References Some web-based references may require access dates standardized. Avid references resented as preprints and arXiv citations. Rectify references 23, 25, 28, 30, 31, 42, 43, 46, ??? 9. Use of AI chatbots during preparation The manuscript evaluates ChatGPT but does not clearly state whether AI tools were used in writing the manuscript itself. Given the subject matter, transparency is essential. I recommend adding a dedicated statement related to the use of AI during the writing of the paper. 10. Additional remarks and suggestions 10.1. Conceptual clarification The manuscript carefully reframes “accuracy” as “perceived alignment.” This is appropriate. Continue emphasizing that this is a perceptual, not epistemic, superiority. 10.2. Overinterpretation risk The conclusion should avoid implying that AI titles are objectively superior. Suggested wording: Replace “may enhance scientific communication” with “may enhance perceived clarity and appeal under specific conditions.” 10.3. Statistical interpretation The OR of 4.4 for perceived alignment is substantial. Consider discussing: Practical magnitude, and whether effect sizes correspond to meaningful real-world differences. 10.4. Potential biases Cultural differences in stylistic preferences may influence ratings. LLM training bias toward assertive phrasing could affect appeal ratings. 10.5. Writing and organization Slight reduction of repetition in discussion. Tightening of some longer paragraphs for readability. 10.6. Misuse of references Several sentences are lacking references: in the introduction section add references after: communication (L1), for peer review (L2), is intense (L4), complex task, or appeal, readable, community, scientific writing, generation, Chen and Erger, 10.7. Misuse of abbreviations Define abbreviations at first use: see ChatGPT in the introduction, 10.8. Article structure Objectives must be reported at the end of the introduction not in the beginning of the methods section. The paragraph (in the results section) named “Perceived accuracy and appeal ratings” should be moved to the methods section. 10.9. Sample size Authors are asked to calculate the sample size: why 21? Why not 10 or 100? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Medical writing, Use of AI, respiratory physiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Ben Saad H. Peer Review Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.196161.r458465) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1470/v2#referee-response-458465 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Gabay R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 29 Jan 2026 | for Version 1 Renz Alvin Gabay , Sorsogon State University, Sorsogon, Philippines 0 Views copyright © 2026 Gabay R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Overall Assessment This manuscript addresses a timely and relevant question: whether large language models (LLMs), specifically ChatGPT-4.0, can generate scientific titles that are perceived as more accurate, appealing, and preferable than human-written titles. The study is clearly written, methodologically transparent, and thoughtfully situated within the emerging literature on AI-assisted academic writing. The use of blinded human raters, articles published before the advent of generative AI, and multiple statistical approaches are notable strengths. At the same time, several aspects of the design, interpretation, and framing would benefit from clarification or refinement. Most of these do not undermine the core findings but would strengthen the rigor, scope, and interpretability of the study. With revisions, this work has strong potential to make a meaningful contribution to the literature on AI in scientific communication. 1. Conceptualization of “Perceived Accuracy” Strength: The authors appropriately focus on reader-centered evaluation rather than automated metrics, which is a valuable contribution compared to prior studies. Suggestion: The construct of “perceived accuracy” would benefit from clearer conceptual framing. Because raters evaluated titles based only on the abstract (and were not domain experts for each topic), the measure appears to capture perceived alignment between title and abstract rather than factual or methodological accuracy of the study itself. The authors may consider clarifying this distinction throughout the manuscript. Rephrasing some claims to emphasize perceived representativeness or abstract–title alignment would improve conceptual precision. 2. Use of Articles from the Year 2000 Strength: Selecting pre-AI articles is an elegant design choice that convincingly eliminates the possibility of AI-assisted human titles. Suggestion: At the same time, this introduces a potential temporal effect: writing conventions, stylistic norms, and reader expectations may have changed over 25 years. AI-generated titles, trained on modern scientific language, may naturally align better with contemporary preferences. The authors acknowledge this as a limitation, but it may merit stronger emphasis in both the Discussion and Conclusion. Slightly tempering general claims (e.g., “AI-generated titles can surpass human-written titles”) to reflect this context would enhance interpretive balance. 3. Scope and Generalizability Strength: The study design is well-controlled within a clearly defined domain (high-impact general internal medicine journals), which enhances internal validity. Suggestion: Some discussion sections extend the implications to scientific communication broadly, including lower-tier journals and other disciplines. The authors might consider more explicitly limiting generalizations to similar contexts (e.g., biomedical research, non-specialist academic readers). Framing broader claims as hypotheses for future research would maintain scholarly caution while preserving the manuscript’s relevance. 4. Interpretation of “Preference” as Title Quality Strength: Using overall preference as an outcome is intuitive and directly relevant to how readers interact with scientific articles. Suggestion: Preference, appeal, and perceived accuracy are inherently subjective constructs and may not fully capture other dimensions of title quality, such as precision, cautiousness of claims, or indexing suitability. The Discussion could benefit from acknowledging that higher appeal does not always equate to higher epistemic rigor. A brief reflection on possible trade-offs (e.g., rhetorical optimization versus conservative scientific framing) would enrich the interpretive depth. 5. Modeling of Ordinal Data Strength: The authors appropriately use non-parametric tests for paired comparisons and apply multilevel models to account for clustering by rater. Suggestion: The use of negative binomial regression for 0–10 ordinal ratings could be more fully justified, as these scores are not count data in the conventional sense. Providing a short rationale for this choice, or referencing prior studies that have used similar approaches, would strengthen methodological transparency. Alternatively, mentioning that the main conclusions were consistent across analytic approaches (if true) would reassure readers. 6. Inter-Rater Agreement Strength: Reporting both percent agreement and Gwet’s AC with weighted coefficients is commendable and demonstrates careful attention to reliability. Suggestion: Given that agreement was in the moderate-to-substantial range, the manuscript might briefly note that judgments of title quality remain partly subjective. Framing the findings as reflecting consistent tendencies rather than unanimous consensus would appropriately contextualize the results. 7. Ethics and Responsible Use of AI Strength: The manuscript commendably addresses ethical issues such as transparency, authorship, and the potential homogenization of scientific writing. Suggestion: This section could be modestly expanded to engage more directly with current debates in scholarly publishing, such as: whether AI-assisted title generation constitutes authorship, editing, or technical assistance; how journals might operationalize disclosure of AI use. This would strengthen the practical relevance of the study. 8. Literature balance: The Introduction provides a strong overview of recent studies on AI-assisted scientific writing, which effectively situates the work within the current technological discourse. To further strengthen the theoretical grounding, the authors may consider incorporating additional literature on scientific title construction, rhetorical framing, and scientometrics from information science and bibliometrics. Integrating these perspectives could enrich the conceptual framework and more firmly anchor the study in the broader scholarship on how titles function in academic communication. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Science Education, Physics Education, AI in Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Gabay RA. Peer Review Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.191483.r446770) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1470/v1#referee-response-446770 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 A. Funa A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 29 Jan 2026 | for Version 1 Aaron A. Funa , Sorsogon State University, Sorsogon, Philippines 0 Views copyright © 2026 A. Funa A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The study addresses a timely question and uses an appropriate blinded paired-title design: 50 abstracts from 10 high-impact general internal medicine journals (year 2000) were retitled with GPT-4.0, and 21 researchers rated each human vs AI title for accuracy and appeal (0–10) and chose an overall preference. The work is generally clear, technically sound, and supported by open underlying/extended data. However, two points must be fixed for scientific soundness: (1) correct the inconsistency in the reported rating scale (0–10 vs 1–10) and confirm the coding used in all analyses; and (2) reconsider modeling bounded ordinal ratings with negative binomial regression. An ordinal mixed-effects model (or a clearly justified alternative with sensitivity analyses) would better match the outcome type and should explicitly account for repeated measures across raters and articles. Additional improvements: specify the random article-selection procedure (sampling frame, method/seed) and standardize formatting (e.g., capitalization) for both title types to avoid confounding. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise STEM Education; Science Education; Nursing Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) A. Funa A. Peer Review Report For: Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles [version 2; peer review: 2 approved, 3 approved with reservations] . F1000Research 2026, 14 :1470 ( https://doi.org/10.5256/f1000research.191483.r446771) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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