Quantifying Artificial Intelligence Contribution in Academic Writing: Development of the Transparency and Reporting of Artificial Intelligence Contribution for Evaluating Submissions Instrument.
Jul 2026· Journal of Neuroscience Nursing· 0 citations· 22 references
Medicine
TL;DR
Authors in any field should self-report a TRACES score when submitting their manuscript, and Journals may benefit from requiring authors to include a TRACES score when submitting a manuscript for peer review.
Abstract
Background
There has been an increase in the availability and use of artificial intelligence (AI) across many professional domains, and there are traces of AI in nearly every manuscript published using modern technology. It is becoming increasingly difficult for peer reviewers, editorial teams, and journal readers to identify the degree to which authors have used AI in the development of their manuscripts.
Methods
Our research group developed an AI scoring rubric to provide authors with an opportunity to self-disclose their use of AI.
Results
The Transparency and Reporting of Artificial Intelligence Contribution for Evaluating Submissions (TRACES) instrument provides a score from 0 to 40 across 3 domains: mechanics, writing, and illustrations. Higher scores indicate increased use of AI by the authors when writing or preparing a manuscript for submission.
Conclusion
Authors in any field should self-report a TRACES score when submitting their manuscript. Journals may benefit from requiring authors to include a TRACES score when submitting a manuscript for peer review. While higher TRACES scores indicate greater use of AI, there is no specific cutoff provided to determine manuscript acceptance or rejection.
A framework for sustainable, human-centered integration of AI is proposed in which AI is restricted to technical verification and efficiency, while judgments on scientific merit, ethics, and paradigm-shifting research are reserved for appropriately valued human experts.
Background Artificial intelligence (AI) transforms scientific research and publication by supporting all aspects of research from setting up hypothesis and research objectives to data analysis and manuscript preparation. Its widespread use in postgraduate research by students improves efficiency and feedback, but also raises concerns related to academic integrity, ethical behavior, algorithmic bias, hallucinated outputs, copyright issues, and mainly, preservation of students’ critical thinking skills. This article presents supervisor-oriented guidance for the responsible integration of AI into scientific research supervision. Methods We have undertaken narrative synthesis based on published manuscripts as well as our collective supervisory experience with AI-assisted research projects. Published literature evidence and practical experience were integrated to develop 10 insights for supervisors guiding research students who use AI tools during all aspects of their research. Results We have generated 10 practical insights for supervisors on guiding research students with ethical AI use: Establishing transparency and accountability; Setting expectations; Creating a customised AI use agreement; Maintenance of AI Use Log for Review; Reviewing AI log – The supervisor’s role; Addressing data privacy and ethical risks; Assessment of students’ critical thinking and conceptual understanding; Guide verification and validation of AI-generated content; Prepare students to justify AI-related decisions during post-research evaluation; and Institutional support for supervisor competency. Together, these recommendations emphasize the importance of human oversight, documentation and verification to distinguish AI assistance and original student scholarship. Conclusions AI can enhance postgraduate research and supervision when used as a transparent and ethically governed tool rather than as a shortcut. Supervisors have a central role in ensuring students’ ethical research practices evident by clear documentation of AI use, verification and transparency. A structured supervisory approach following the 10 insights can help balance the efficiency of AI tools with research integrity, accountability, and meaningful human mentorship.
Although AI tools are useful assistants in the preparation of scientific articles, they should not replace the researcher’s critical thinking and scientific knowledge.
A. Brandão, Leila Posenato Garcia, Luisa Veras de Sandes-Guimarães· Revista Brasileira de Saúde...· 0 citations
Artificial Intelligence (AI) is increasingly being integrated into medical research and scholarly publishing, supporting activities such as literature searching, data analysis, medical imaging, manuscript preparation, and peer review. Despite these opportunities, AI use introduces concerns related to hallucinations, bias, privacy, confidentiality, copyright, reproducibility, and scientific accountability. Existing guidance provides important principles for responsible AI use, but reporting practices remain variable. This article reviews the TITAN guidelines as a practical framework for improving transparency and responsible reporting of AI use in medical research and scholarly publishing. The TITAN guidelines provide a proportionate and technology neutral approach to AI reporting. It distinguishes minor uses, such as language assistance, from substantive applications involving research design, analysis, interpretation, or scientific content. The framework emphasizes that human researchers retain responsibility for evaluating AI outputs and the integrity of published work. It also provides a basis for authors, reviewers, editors, and publishers to incorporate standardized AI reporting into scholarly workflows. TITAN offers a practical framework for documenting meaningful AI involvement while supporting transparency, reproducibility, and human accountability. Its flexible structure can accommodate emerging AI technologies, including multimodal and agentic systems. Periodic revision and coordinated adoption by medical journals and research communities will be important to maintain its relevance as AI capabilities continue to evolve.
Arzoo Nazir, Shah Zeb· Electronic Journal of Medica...· 0 citations
Early-career researchers often have a sound idea yet struggle to turn it into a publishable manuscript that reviewers can trace and evaluate. This paper synthesizes practical guidance on structuring and drafting research articles using the introduction-methods-results-and-discussion (IMRaD) convention, while addressing emerging concerns about the responsible use of generative artificial intelligence (AI) in academic writing. A documentary narrative synthesis was conducted using 36 high-authority sources, including writing guides, guidance from journal editors, publisher and ethics policies, and recent empirical studies on AI-assisted writing. Recommendations were coded using an explicit IMRaD-aligned codebook and then consolidated into a step-by-step workflow from question formulation to submission checks. The synthesis indicates that treating IMRaD as a traceability checklist improves alignment between research questions, methods, results, and claims, and that iterative revision is more effective than one-pass drafting. AI support is most defensible when limited to language and process assistance, combined with disclosure, reference verification, and full human accountability for all content. The paper concludes with an actionable checklist and a visual ‘traceability map’ that can be adapted for research training and supervision.
Emad Al-Mahdawi, Nkaepe E. E. Olaniyi· International Journal of Eva...· 0 citations
Plagiarism-detection and AI-detection tools are now widely used in academic publishing. These systems were introduced to support research integrity by helping journals identify potential plagiarism, inappropriate text reuse, and concerns related to undisclosed use of artificial intelligence (AI). When used appropriately, they can serve as useful screening tools and support editorial decision-making. However, their growing use has also created new challenges. Similarity scores are often interpreted as direct measures of plagiarism, even though they only indicate matching text and require contextual evaluation. Likewise, AI-detection tools can produce uncertain or incorrect classifications, yet their output may influence perceptions of authorship and manuscript quality. As a result, researchers may spend considerable time reducing similarity scores or worrying about AI-detection reports, even when the underlying writing is appropriate. These challenges may be particularly relevant for early-career researchers and authors writing in a second language. This article discusses the benefits and limitations of both similarity-detection and AI-detection systems and argues that their output should be viewed as screening indicators rather than definitive judgments. Human interpretation should remain central to the evaluation of originality, authorship, and research quality.
Deep Shah· RMC Global Journal· 0 citations
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