article · Design+
Scientific figures carry both evidential and rhetorical weight during manuscript evaluation, yet uneven access to specialised software, design expertise, and visual-literacy mentoring creates an inequity in publishing. Artificial intelligence tools, when maintained under strict human supervision, offer potential to reduce this disparity by supporting layout, planning, captioning, accessibility evaluations, and reproducible plotting. However, generative tools introduce significant risks, including fabricated visual elements, semantic drift, bias, confidentiality breaches, and compromised provenance. To address these tensions, a five-stage framework guides researchers to specify their scientific claims, select an appropriate visual grammar, draft using bounded artificial intelligence assistance, validate each visual element against raw source data, and compile publication files complete with provenance records. Tested conceptually on a protein X-ray crystallography workflow, this process ensures that artificial intelligence acts purely as a drafting assistant while full accountability remains with the researcher.
Visual presentation strongly affects how scientific manuscripts are judged, placing researchers with fewer design resources at a disadvantage. Adopting structured guidelines for artificial intelligence assistance enables researchers to improve the accessibility and visual clarity of their figures without sacrificing scientific integrity, generating fabricated findings, or obscuring data provenance during the peer review and publication process.
This work outlines a conceptual framework for academic publishers, software developers, and research institutions seeking to embed artificial intelligence into scientific illustration and editorial workflows. It could inform the development of compliance tools, accessibility checkers, and figure-generation software for researchers in data-heavy disciplines. Because the framework is demonstrated only through a non-empirical, worked application, practical deployment remains at an early, conceptual stage requiring technical development and empirical validation.
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Reviewers, editors, and readers judge a manuscript partly through its figures, which carry evidential as well as rhetorical weight. This review treats figure production as a design problem and as a question of publication equity. Where communicative polish influences how otherwise comparable work is assessed, uneven access to professional illustration, specialized software, mentoring, and visual-literacy support becomes a justice-relevant disadvantage. Kept under human control, artificial intelligence (AI)-assisted tools may narrow part of that gap by helping with figure planning, layout, captioning, accessibility checks, and reproducible plotting. Generative systems also carry their own hazards: fabricated elements, semantic drift, bias, exposure of confidential information, and broken provenance. We propose a five-stage, theory-informed framework in which authors define the scientific claim, choose the visual grammar, draft with bounded AI support, validate every visible element against source material, and prepare files with provenance and disclosure. A worked application to a protein X-ray crystallography workflow shows how the framework can be implemented in a technically demanding setting; the application is explicitly non-empirical. Throughout, AI serves as a drafting and checking aid, not as a source of evidence, and accountability stays with the author.
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DOI: 10.36922/dp026250026
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