How AI-Generated Visualisations Are Transforming Academic Publishing

How AI-Generated Visualisations Are Transforming Academic Publishing

In the past, most academic figures were created manually. Researchers generated plots in statistical software, drew conceptual diagrams in vector programs and occasionally commissioned professional illustrations. Today, however, a new class of tools is reshaping that landscape: systems that use artificial intelligence to generate or refine visualisations based on prompts, sketches, data tables or even rough ideas.

These tools can feel miraculous. They can produce polished line art from a simple sketch, convert dense tables into visually appealing figures, or generate schematic diagrams in seconds. At the same time, they raise serious questions. When is an AI-generated figure still an accurate representation of the underlying data? How can editors and readers know whether a visual has been manipulated? What must authors disclose when AI has helped create an image?

This article offers a practical guide to using AI for research visualisations in ways that enhance clarity without undermining trust. It focuses on three key goals: avoiding manipulation, ensuring traceability and maintaining academic standards.

1. What Counts as an AI-Generated Visualisation?

AI-generated visualisations can take many forms. Some tools operate directly on numerical data, suggesting chart types and layouts based on a dataset. Others specialise in visual design, turning text prompts into conceptual diagrams or illustrative images. A third category includes tools that “enhance” images by removing noise, sharpening edges or filling in missing regions.

In an academic context, it is helpful to distinguish between three broad uses:

1.1. Illustrative conceptual figures
These are diagrams that help explain relationships, workflows, processes or conceptual frameworks. AI tools might generate boxes, arrows and icons or stylised backgrounds that make a figure more attractive. Provided the conceptual content comes from the researcher and is accurately represented, this use can be legitimate.

1.2. Data-driven charts and plots
Some tools accept data tables as input and propose charts automatically. If the chart reflects the data faithfully and uses conventional, transparent scales, the main concern is not aesthetics but traceability and documentation: how was the image generated, and can others reproduce it?

1.3. Image enhancement and synthesis
In fields that rely on microscopy, imaging or screenshots, AI may be used to denoise, upscale or “inpaint” missing regions. At the extreme end, generative models can produce entirely synthetic images that look like real experimental results. These uses carry the greatest ethical risk and are the most likely to violate journal policies if not handled carefully.

2. Opportunities: Clarity, Accessibility and Speed

Used responsibly, AI-generated visualisations can support academic communication in several positive ways. They can help researchers who have strong ideas but limited design skills. They can improve accessibility by prompting authors to simplify cluttered figures. They can reduce the time spent moving shapes around slideware so that more time can be devoted to interpreting results.

AI tools also encourage authors to think visually. Many readers grasp complex relationships more easily in diagram form than in dense text. A good figure can summarise an entire methods section or highlight a key pattern in the data that might otherwise be lost in tables.

However, these benefits depend on a clear rule: the figure must be a faithful servant of the data and argument—not a decorative embellishment that misleads.

3. Ethical Risks: Manipulation, Hallucination and Aesthetic Bias

The same tools that enhance clarity can also make it easier to cross ethical boundaries. Because AI-generated images can be polished in seconds, there is a temptation to prioritise visual impact over accuracy. Some of the key risks include:

3.1. Misleading enhancements
Over-smoothing, aggressive colour changes or selective cropping can exaggerate patterns or hide uncertainty. An image that looks clearer to the eye might, in fact, be less honest about the limits of the data.

3.2. “Hallucinated” details
Generative models are capable of inventing features that were never present in the original data. In scientific imaging, this can be particularly dangerous. A tool that “fills in” missing structure in a micrograph, for instance, may produce a beautiful but false representation.

3.3. Aesthetics over substance
Reviewers and readers are human; they may unconsciously rate polished figures as more convincing. If AI-generated visuals are used to make weak results appear more robust than they are, the technology becomes a tool of persuasion rather than explanation.

Because many of these problems are subtle, the safest approach is to treat any AI involvement in figures as something that must be transparent, documented and justifiable.

4. Principles for Ethical AI-Generated Figures

To ensure that AI-generated visualisations strengthen rather than weaken academic communication, researchers can adopt a set of core principles.

4.1. Fidelity to the underlying data

Any figure based on empirical data should represent that data accurately. Scales, axes, colours and overlays must not distort magnitudes or relationships. If AI suggests a chart type that compresses differences or hides outliers, the researcher should override that suggestion.

Where images are derived from experimental or observational data, the AI’s role should be limited to noise reduction or contrast adjustment that can be justified technically. Transformations that add, remove or invent features go beyond presentation and enter the realm of fabrication.

4.2. Traceability and documentation

Readers and reviewers should be able to understand how a figure was produced. This does not require a full technical appendix for every diagram, but authors should be able to answer basic questions: What software or AI model was used? Was the image generated directly from data, or from a textual description? Were any manual edits made afterwards?

Good practice includes keeping:

• original raw data files and intermediate exports,
• a short methods note describing how the figure was created,
• earlier versions or scripts used for plotting, when possible.

Many journals already require that plots can be regenerated from data on request. Introducing AI into the process does not change this requirement; if anything, it intensifies the need for clear records.

4.3. Reproducibility and version control

Where visualisations are part of a published analysis, it should be possible for another researcher to reproduce the figure using the same data and workflow. If AI is used only as a layout assistant (for example, suggesting colour schemes or label placement), reproducibility is less of a concern. If, however, a proprietary model transforms the visual in ways that cannot be replicated, authors need to consider whether that figure belongs in the permanent record at all.

4.4. Respect for journal and institutional policies

Many journals now publish explicit rules about the use of AI tools in both text and images. Some allow AI-assisted layout or illustration if disclosed; others prohibit AI-generated figures that could be mistaken for experimental data.

Before including an AI-generated visualisation, authors should review relevant guidelines and, when in doubt, explain their process in the cover letter or methods section. Proactive transparency can prevent misunderstandings later.

4.5. Protection of sensitive data

Some AI tools operate entirely in the cloud, sending content to external servers. If visualisations are based on sensitive or confidential data — patient images, proprietary designs, unpublished datasets — using such tools may breach ethical approvals or legal agreements. Locally run or institutionally approved tools are safer in these cases.

5. A Practical Workflow for Using AI in Figure Creation

Translating principles into practice can be challenging, especially for busy researchers. The following workflow offers a pragmatic approach to integrating AI visual tools into academic work without compromising standards.

Step 1: Clarify the purpose of the figure. Decide what the reader should learn from the visualisation. Is it a conceptual map, a summary of results, a depiction of a process, or an illustration of an experimental setup?

Step 2: Start from the data or concept, not from the tool. Draft the figure on paper or in a basic plotting program first. This ensures that the intellectual structure comes from you, not from whatever the AI happens to generate.

Step 3: Use AI to improve clarity, not to invent content. Ask a tool to tidy layout, propose clearer iconography or harmonise colours. Avoid features that extrapolate beyond your data or add decorative but potentially misleading elements.

Step 4: Cross-check against the underlying evidence. After AI assistance, compare the figure with your original data or conceptual notes. Do all elements still correspond to something real and defensible? If you cannot explain a feature with reference to your work, remove it.

Step 5: Document your process. Make brief notes about which tools you used and how. This can go into your internal project records and, where relevant, into the manuscript’s methods or acknowledgements.

Step 6: Disclose AI involvement when appropriate. If your figure was substantially shaped by an AI system, consider adding a short statement, especially if journal guidelines request it. Transparency builds trust.

6. What Editors, Reviewers and Readers Will Expect

As AI-generated visualisations become more common, expectations will evolve. Editors and reviewers are unlikely to object to clearly labelled conceptual diagrams where the relationship to the text is obvious. They will, however, be wary of any figure that appears to make strong empirical claims yet cannot be tied back to documented data or a reproducible pipeline.

Readers, too, may become more sensitive to the difference between explanatory artwork and empirical visuals. They will want reassurance that key plots, images and diagrams are anchored in the underlying evidence rather than in a model’s imagination. Clear legends, transparent captions and honest descriptions of uncertainty will matter more, not less.

7. Building Local Policies: Labs, Departments and Journals

Given the pace of technological change, it is unrealistic to expect individual researchers to solve all ethical questions alone. Institutions, departments and journals should help by developing simple, evolving policies that define acceptable and unacceptable uses of AI in visualisation.

These policies can cover, for example:

• when AI may be used for conceptual diagrams but not for data-derived images;
• what level of disclosure is expected in manuscripts;
• which tools are approved for sensitive datasets;
• how to handle suspected cases of AI manipulation in peer review.

Such guidelines do not need to be perfect from the start. They can be refined as experience accumulates. What matters is that the community openly acknowledges the issue and provides support rather than leaving researchers to guess.

Conclusion: Using AI Visual Tools Without Sacrificing Trust

AI-generated visualisations are undeniably changing academic communication. They make it easier than ever to produce polished figures, but they also make it easier to cross ethical boundaries without realising it. The challenge for researchers is to harness the benefits of these tools while preserving the trust that underpins scholarly work.

That trust depends on three things: avoiding manipulation, ensuring traceability and maintaining academic standards. If a figure remains faithful to the underlying data or concepts, if its creation can be described and reproduced, and if its purpose is to clarify rather than to exaggerate, AI can be a useful ally.

As journals and institutions develop clearer policies, responsible researchers will stand out not only for the quality of their results, but also for the care with which they communicate them. AI will almost certainly become part of that communication. The crucial question is not whether such tools are used, but how openly, thoughtfully and ethically they are integrated into the research process.

For authors who want to ensure that figure captions, methods descriptions and full manuscripts remain clear, accurate and aligned with journal standards, our journal article editing service and scientific editing services can help refine language, resolve ambiguities and strengthen the overall presentation of complex visual material.