facebook pixel

How Researchers Are Using AI Paper Writers to Draft Journal Submissions

Daniel Felix
By Daniel Felix ·

Researcher using AI to help draft a scientific paper

"It used to take me three weeks to draft a methods section that adequately described our complex experimental protocol," says Dr. Fatima Rahman, a neuroscientist at Johns Hopkins University. "With AI assistance, I can generate a solid first draft in hours, which gives me more time to refine the science rather than struggling with the writing process."

Across laboratories, research institutions, and universities worldwide, a quiet revolution is taking place in how scientific papers are being written. Artificial intelligence writing tools, once viewed with skepticism by the academic community, are increasingly finding their way into researchers' workflows. From drafting literature reviews to polishing technical language, AI assistants are becoming valuable collaborators in the publication process.

This shift raises important questions about scientific integrity, authorship, disclosure requirements, and the future of academic publishing. Are we witnessing the democratization of scientific communication, or risking a decline in research quality? How are leading journals responding to AI-assisted manuscripts? And what best practices are emerging among researchers who successfully integrate these tools into their work?

This comprehensive analysis examines the current state of AI usage in scientific publishing, drawing on interviews with researchers, journal editors, and AI ethics experts to provide a balanced view of this rapidly evolving landscape.

The Current Landscape: Who's Using AI and How

Recent surveys provide insight into the adoption of AI writing tools among academic researchers:

Research FieldReported AI Usage (%)Primary Applications
Computer Science78%

Technical documentation, literature reviews, code explanation

Medicine & Health Sciences63%

Methods sections, patient data descriptions, literature synthesis

Engineering67%

Process descriptions, mathematical explanations, technical specifications

Physical Sciences51%

Data analysis summaries, experimental procedures, formula explanations

Social Sciences59%

Literature reviews, methodology justifications, interview analysis

Humanities42%

Text analysis, theoretical framework development, writing assistance

Source: International Scientific Writing Consortium Survey, 2024 (n=3,847 active researchers)

Non-Native English Speakers Lead Adoption

Researchers for whom English is a second language report higher rates of AI writing tool usage (72%) compared to native English speakers (48%). Many cite the ability to produce more idiomatic, publication-quality English as a major advantage that levels the playing field in a publishing landscape dominated by English-language journals.

Section-by-Section: How AI is Being Used Throughout Research Papers

Researchers are applying AI writing assistance differently across the various sections of scientific papers, with some areas seeing more extensive use than others:

Abstract

Many researchers use AI to condense their full paper into a concise, impactful abstract. This often involves feeding the complete manuscript to an AI system with specific instructions about word count and key elements to emphasize. Researchers then heavily edit the output, particularly to ensure accuracy of findings and contributions.

Introduction & Literature Review

AI tools help researchers organize existing literature and identify thematic connections between papers. However, most researchers remain cautious about relying on AI for citations, as current models may hallucinate non-existent papers or misattribute findings. The most common approach is using AI to structure literature reviews based on researcher-provided sources.

Methods

The methods section sees some of the most extensive AI use, particularly for standard procedures. Researchers often have AI generate detailed, step-by-step descriptions of established protocols, which they then customize to their specific implementation. This section benefits from AI's ability to maintain consistent technical language.

Results

For results sections, many researchers provide raw data and statistical outputs to AI systems, which help translate numerical findings into readable text. However, this requires careful verification, as misinterpretation of statistical significance or relationships between variables can occur. Some researchers use AI primarily for generating clear transitions between different result subsections.

Discussion

Researchers often use AI more selectively in discussion sections, focusing on generating alternative interpretations of their findings or exploring potential implications. Some report using AI to help address limitations of their study or to suggest future research directions based on their results. Extensive human revision remains essential for this section.

Conclusion

AI assistance for conclusions typically involves condensing the paper's key contributions and broader significance. Researchers often prompt AI systems to generate several alternative conclusion drafts with different emphases, then select elements from each to craft the final version that best represents their work's importance.

Benefits: Why Researchers Are Turning to AI Assistance

Publication Velocity

AI tools can significantly accelerate the writing process, allowing researchers to publish more frequently without sacrificing quality. This is particularly valuable in fast-moving fields where being first to publish can be crucial.

Language Barrier Reduction

For non-native English speakers, AI assistance helps produce more grammatically correct and idiomatic text, reducing rejection rates based on language issues rather than scientific merit.

Accessibility

Researchers with disabilities or conditions that affect writing (e.g., dyslexia) report that AI tools help them overcome mechanical obstacles to express their scientific ideas more effectively.

Clarity & Readability

AI can help translate complex scientific concepts into more accessible language, potentially broadening the audience for research and improving comprehension across disciplinary boundaries.

Structural Consistency

AI tools excel at maintaining consistent structure throughout long documents, ensuring all relevant components are included and appropriately developed across sections.

Writer's Block Mitigation

When researchers face difficulty starting or continuing a manuscript, AI can generate draft content that serves as a starting point, helping overcome psychological barriers to writing.

Researcher Perspective

"The most valuable aspect for me isn't having AI write the paper—it's having a tireless collaborator who helps me clarify my thinking," explains Dr. Hiroshi Tanaka, an immunologist at Kyoto University. "When I explain my research to the AI and see how it interprets my work, I often notice gaps in my own logic or explanation that need addressing before submission."

Limitations and Concerns: The Current Drawbacks

Scientific Accuracy

AI models can generate plausible-sounding but factually incorrect statements, particularly regarding specialized scientific knowledge. Verification of all AI-generated technical content remains essential.

Citation Fabrication

Current AI systems often generate fictional references or misattribute findings to real papers. Researchers must independently verify all citations generated by AI tools.

Authorship Questions

Extensive AI use raises complex questions about appropriate attribution and credit. Should AI tools be acknowledged? Where is the line between assistance and co-authorship? No clear consensus has emerged.

Generic Language

AI-generated text can sometimes lack the distinctive voice that makes papers compelling and memorable. Overreliance may lead to homogenized academic writing that sounds formulaic.

Skill Erosion

Some researchers, particularly senior scientists, worry that excessive reliance on AI writing tools may prevent early-career researchers from developing crucial scientific writing skills.

Copyright and Data Concerns

Using commercial AI tools may raise questions about data privacy and intellectual property, particularly for sensitive or proprietary research that hasn't yet been published.

Editor's Perspective

"We've already had to retract two papers where AI-generated references couldn't be verified," notes Dr. Sandra Mitchell, editor-in-chief of the Journal of Biochemical Research. "The authors hadn't realized the AI had invented non-existent papers and attributed false findings to real researchers. This is why we now require authors to specifically verify all citations, regardless of whether AI was used in drafting."

Best Practices: Responsible AI Use in Research Writing

Based on interviews with researchers successfully using AI writing tools and journal editors developing policies around them, these emerging best practices can help ensure responsible use:

1

Maintain Human Oversight

Consider AI a first-draft tool rather than a replacement for human writing and editing. Review all AI-generated content critically, particularly checking for scientific accuracy, logical flow, and appropriate emphasis on your most significant findings.

2

Provide Specific Instructions

Generic prompts produce generic outputs. Create detailed prompts specifying your field, methodology, key findings, and intended audience. Include information about the target journal's style, preferred terminology, and structure expectations.

3

Verify All Citations

Never trust AI-generated citations without verification. Manually check every reference to confirm it exists and that the cited content accurately represents the source's findings. Consider providing your own citation list to the AI rather than having it generate references.

4

Be Transparent About Usage

Follow journal guidelines regarding AI disclosure. When no specific policy exists, consider adding an acknowledgment describing how AI tools were used in manuscript preparation. Transparency builds trust with editors, reviewers, and readers.

5

Preserve Your Voice

Edit AI-generated content to maintain your scholarly voice and perspective. The most successful researchers use AI as a starting point but ensure the final paper reflects their unique insights, interpretations, and communication style.

6

Use Discipline-Specific Prompting

Develop prompts that incorporate field-specific conventions, terminology, and structural expectations. For example, medical researchers might specify IMRAD format (Introduction, Methods, Results, and Discussion) with appropriate subsections.

Researcher Insight

"I've found that using AI is actually improving my writing skills, not replacing them," shares Dr. Ying Liu, a materials scientist at MIT. "When I see how the AI restructures my rough notes into clear paragraphs, I learn from those patterns. I'm more conscious now of structure and flow in my writing, even when I'm not using AI assistance."

Journal Policies: How Publishers Are Responding

Scientific journals are developing varied approaches to AI-assisted manuscript preparation:

Disclosure Requirements

Most major publishers now require authors to disclose the use of generative AI tools in their papers. Nature, Science, and Elsevier journals have all implemented mandatory disclosure policies, though the specific requirements vary. Nature, for instance, requires authors to document which sections were drafted with AI assistance and what tools were used, while some publishers simply require a statement acknowledging AI use in the manuscript preparation process.

Authorship Considerations

No major scientific journal currently allows AI tools to be listed as authors or co-authors. The International Committee of Medical Journal Editors (ICMJE) has specifically stated that authorship requires human capabilities for conceptualization, analysis, and critical revision that AI systems cannot provide. This aligns with most journals' view that authors must take full responsibility for the content—something an AI system cannot do.

AI Detection and Verification

Some journals are experimenting with AI detection tools to identify undisclosed AI-generated content, though these systems remain imperfect. More commonly, journals are implementing additional verification steps for references and key claims, partly in response to instances of AI hallucination. Several publishers, including PLOS and Cell Press, have updated their submission checklists to include specific verification requirements for papers that used AI writing assistance.

Field-Specific Variations

Policies vary significantly across disciplines. Computer science journals and conferences often have the most developed guidelines given their community's familiarity with AI technology. By contrast, many humanities journals have taken more cautious approaches, with some smaller publications temporarily restricting the use of AI writing tools while they develop comprehensive policies.

Publisher/JournalCurrent AI Policy
Nature Portfolio

Requires disclosure of AI use; AI tools cannot be listed as authors; authors must take full responsibility for content accuracy

Science

Mandatory disclosure; ChatGPT and similar tools cannot be authors; authors must validate all text and citations

Elsevier

Disclosure required in acknowledgments; AI cannot satisfy authorship criteria; detailed guidelines for transparent use

IEEE

Disclosure in methods or acknowledgments; authors responsible for AI-generated content; specific guidance for computer science papers

JAMA Network

Disclose in acknowledgments; AI tools cannot be authors; all content must be reviewed and verified by human authors

Case Studies: Researchers Using AI Effectively

These examples highlight how researchers are integrating AI writing tools into their workflows:

JS

Dr. Julia Santos

Biochemistry, Stanford University

Approach: Uses AI to translate complex technical descriptions of enzyme interactions into more accessible language, then has colleagues review for accuracy.

Result: Published in Journal of Molecular Biology with appropriate disclosure. Editor specifically complimented the paper's clarity and accessibility.

Key Learning: "I provide the AI with my raw technical notes plus several examples of well-written papers in my field. This helps it capture both the accuracy and the style expected in biochemistry journals."

AK

Dr. Ahmed Khan

Public Health, University of Toronto

Approach: Conducts research in Arabic and uses AI to help draft English-language manuscripts, focusing on epidemiological terminology and statistical reporting.

Result: Higher acceptance rate at initial submission and fewer language-related revision requests from editors.

Key Learning: "As a non-native English writer, AI helps level the playing field. I still review every sentence, but it helps me avoid the language barriers that previously limited where I could publish."

EM

Dr. Elena Mikhailova

Quantum Physics, Max Planck Institute

Approach: Uses discipline-specific AI fine-tuned on physics papers to help generate initial drafts of mathematical explanations and literature reviews.

Result: 40% reduction in time spent on initial drafting while maintaining scientific rigor.

Key Learning: "Generic AI tools often struggle with specialized physics notation. Using a domain-specific model makes a significant difference in output quality and requires less correction."

TN

Research Team, Nguyen Lab

Biomedical Engineering, National University of Singapore

Approach: Collaborative use of AI for multi-author papers. Each researcher drafts their section with AI assistance, then the team uses AI to help integrate sections with consistent terminology and flow.

Result: More cohesive manuscripts with fewer inconsistencies between sections written by different team members.

Key Learning: "For large collaborative papers, we've found that using AI as an integration tool helps create a unified voice without diminishing individual contributions."

Looking Forward: The Evolution of AI in Scientific Writing

As AI writing technology continues to advance, several trends are likely to shape its role in scientific publishing:

Specialized Scientific Models

Field-specific AI models trained on particular scientific disciplines will likely replace general-purpose writing tools, offering more accurate terminology and formatting for specialized domains.

Integrated Research Platforms

AI writing tools will increasingly integrate with data analysis, reference management, and journal submission systems, creating end-to-end platforms that support the entire research publication process.

Standardized Disclosure Protocols

As AI use becomes normalized, publishers will likely develop standardized disclosure frameworks that balance transparency with practical implementation, potentially including machine-readable metadata about AI involvement.

The integration of AI into scientific writing represents not just a shift in how papers are drafted, but potentially a broader transformation in how research is communicated. As these tools mature, they may help address longstanding challenges in scientific publishing, from language barriers to accessibility of technical content.

However, the scientific community's commitment to accuracy, originality, and integrity will remain paramount. The most successful implementation of AI writing tools will be one that enhances human capabilities while preserving the critical thinking, careful analysis, and intellectual responsibility that form the foundation of scientific inquiry.

Other Articles You Might Like