How GenAI Is Changing Academic Writing — Data, Risks & Guidance

Adoption & Usage — Researchers (Global, 2024)

The Elsevier Insights 2024: Attitudes toward AI report, based on a global survey of roughly 3,000 researchers, shows that generative AI has entered the scholarly workflow but in a cautious and selective manner. A substantial portion of researchers have experimented with AI tools, with the most common uses including generating early drafts, organising or structuring manuscripts, editing for clarity, and conducting literature reviews.

Despite growing curiosity, barriers remain significant. Many respondents highlighted concerns about transparency in acknowledging AI use, trust in the reliability of outputs, quality control when integrating generated text, and debates about authorship. These issues create hesitation, especially in disciplines where originality and attribution are tightly scrutinised.

Regional differences were also noted in the survey. While adoption rates vary across continents, the overall trend is consistent: researchers in every region are engaging with GenAI, but most stop short of relying on it for final outputs. Instead, they tend to limit usage to supportive tasks that can be reviewed and validated by human expertise.

In sum, the global research community is moving toward pragmatic integration of AI, using it to streamline repetitive tasks and improve readability, while continuing to prioritise human oversight and accountability.

Macro Trends 2025 — What Changed vs 2024

The AI Index 2025 highlights how rapidly generative AI has moved from experimentation to structured integration across education and research. The report shows clear year-on-year growth in adoption rates within universities, with many institutions shifting from informal use toward establishing formal governance, disclosure policies, and training for staff and students.

Another trend is the steady rise in investment and resources directed toward AI tools specifically designed for learning and academic research. Educational platforms, publishers, and funding bodies are now channeling support into tools that claim to enhance student productivity and assist scholars in managing increasingly large datasets and publication demands.

The Index also notes the expansion of policy frameworks: more universities and governments have moved beyond ad hoc responses to formal guidelines that address AI’s ethical use, authorship boundaries, and disclosure requirements. This marks a transition from reactive stances in 2024 to proactive strategies in 2025.

For academic writing, these macro-trends matter because they shape the environment in which students and researchers work. Increased access to specialised AI tools, combined with clearer rules for their use, means that writing practices are evolving toward a model where assistive AI is normalised—but balanced with strong expectations of integrity and accountability.

Use-cases in Academic Writing (What People Actually Do)

The study arXiv: Patterns and Purposes (2025) provides one of the clearest snapshots of how researchers and students actually deploy generative AI in writing. The data reveal that most reported uses remain assistive rather than generative: polishing readability and grammar, creating concise summaries of complex sources, or generating early ideas for framing a paper. ChatGPT dominates the tool landscape, with the majority of respondents citing it as their primary AI assistant. Usage patterns also vary by background: non-native English speakers are more likely to use AI for editing clarity, while international teams report higher rates of summarisation support compared with domestic-only groups.

Taken together, these patterns suggest that GenAI functions as a supportive editor and collaborator rather than a full author. Its appeal is strongest where it reduces linguistic barriers or compresses reading workloads, thereby expanding participation in scholarly communication without displacing human authorship.

Integrity & Detection — What Works, What Doesn’t

The SpringerOpen study on GenAI text detectors (2024) demonstrates that detection tools face serious limitations. While baseline accuracy can appear strong, the research shows that even simple manipulations — such as paraphrasing or rephrasing prompts — sharply reduce their reliability. In some tested cases, accuracy dropped from high levels to near-random performance once these adjustments were applied, highlighting the fragility of current approaches.

Given these results, detectors should be used with caution in academic contexts. Their most constructive role lies in fostering dialogue: allowing students to self-check their work, reflect on appropriate use of generative tools, and discuss boundaries of originality with instructors. They should not serve as the sole basis for disciplinary action, but rather as aids to support integrity education and transparency. For students, tools like AI Checker by PlagiarismSearch can help with non-punitive self-verification before submitting assignments.

Policies & Disclosure — How to Use GenAI Transparently

According to COPE: Authorship and AI tools, generative AI cannot be listed as an author. Responsibility for the accuracy, integrity, and originality of any manuscript rests entirely with the human authors. When AI tools are used, their role should be declared transparently and specifically, ensuring that readers, instructors, or editors understand what part of the process was supported by automation.

Sample AI Declarations

• Student essay: “I used ChatGPT (version XX, accessed on DD/MM/YYYY) to improve the grammar and clarity of my draft. All ideas and arguments are my own.”
• Academic article: “Generative AI (ChatGPT, version XX, accessed on DD/MM/YYYY) was used to summarise background sources and assist in editing for readability. The authors take full responsibility for the content of this manuscript.”

Common Mistakes in AI Declarations

• Writing vague statements such as “AI was used” without specifying tasks.
• Omitting details about tool version or access date.
• Not clarifying the scope (e.g., whether AI helped with grammar, summarisation, or ideation).
• Presenting AI as a co-author rather than a support tool.
• Leaving out context in multi-author or international collaborations.

Recent surveys (as noted in the Patterns and Purposes study) confirm that many researchers and students already experiment with AI declarations, but the quality and specificity of these disclosures vary widely. Clearer frameworks, such as those recommended by COPE, help ensure transparency while maintaining academic accountability.

Quality & Productivity — What Changes in Outcomes

Evidence from recent surveys and trend reports shows a mixed but increasingly clear picture of how generative AI affects academic writing. On the one hand, it provides tangible improvements in grammar, readability, and document structure, helping both students and researchers produce cleaner drafts in less time. On the other, risks remain: overreliance on AI can lead to surface-level arguments, generic phrasing, or factual errors when the system hallucinates sources or misrepresents data.

• Improved readability: Users consistently report smoother sentence flow and reduced language errors, particularly valuable for non-native English speakers.
• Better structure: Drafts often benefit from clearer organisation and sectioning, as noted in Elsevier surveys of researcher workflows.
• Efficiency gains: Time savings are widely acknowledged, aligning with AI Index 2025 findings on productivity growth in education and research sectors.
• Risks of superficiality: Both sources caution that outputs can remain shallow, requiring critical human editing to achieve scholarly depth.
• Potential inaccuracies: Hallucinated references or unsupported claims are a recurring concern, demanding transparent oversight.

For educators and editors, the balance lies in encouraging assistive uses — such as grammar correction or drafting outlines — while requiring strict disclosure whenever AI contributes to content generation. This distinction helps preserve integrity while enabling real productivity benefits.

Practical Frameworks

Decision Tree: When and How to Use GenAI in Writing

Rubric for Evaluating AI-Assisted Writing

Safety Checklist

• Never paste unpublished manuscripts or confidential data into public AI models.
• Prefer local or closed environments for sensitive material.
• Always double-check citations, quotations, and factual statements manually.

Templates for AI Declarations

• Student essay: “I used ChatGPT (version XX, accessed DD/MM/YYYY) for language polishing and outline suggestions. I accepted changes on grammar but rejected suggested rephrasings of key arguments. All ideas and conclusions are my own.”
• Journal article: “In the Methods and Acknowledgements sections, we note that AI tools (ChatGPT, version XX, accessed DD/MM/YYYY) were used for summarising related literature and editing for readability. The authors take full responsibility for the accuracy and originality of the content.”

Prompts Library (Ethical Use)

• “Rewrite this paragraph for clarity and conciseness, without changing the factual content. Verify all citations manually.”
• “Polish the grammar of this text for academic readability, keeping terminology unchanged. Verify facts separately.”
• “Suggest a clearer structure for this introduction. Do not generate new data or references.”
• “Rephrase this sentence to improve flow, without altering meaning. Check quotes independently.”
• “Summarise this passage in 3 sentences. Do not add examples or facts not present in the text.”
• “Highlight redundant wording and propose alternatives. Author decides what to keep.”
• “Provide two alternative phrasings for this idea, keeping terminology intact.”
• “Suggest improvements in tone to match academic style, no new content allowed.”

Limitations & Research Gaps

Current evidence still leaves important blind spots. For example, while the HEPI survey offers valuable insights into student use of GenAI, open datasets rarely provide clear disciplinary breakdowns. This makes it difficult to compare adoption patterns and writing impacts in the humanities versus STEM fields, where expectations and writing conventions differ sharply.

Another gap concerns detection. As highlighted in the SpringerOpen study, there is no consensus on the reliability of AI-text detectors or on the scenarios where their use is genuinely appropriate. This leaves both educators and students uncertain about when such tools can be trusted and how their results should inform academic decisions.

Finally, despite progress on transparency, there is still no universally accepted format for AI declarations. COPE outlines baseline principles, and our proposed templates illustrate practical applications, but a consistent standard across institutions and disciplines has yet to emerge.

Conclusions — So What?

• For students: Always provide a transparent AI declaration. Use GenAI mainly for readability and structure, not for generating arguments. Apply self-check tools such as AI Checker by PlagiarismSearch to confirm originality before submission.
• For students (EFL focus): Language polishing is acceptable if clearly declared, ensuring that meaning and arguments remain your own.
• For educators: Apply the rubric and decision tree frameworks to guide fair evaluation of AI-assisted writing. Avoid treating detectors as punitive tools; instead, frame them as conversation starters for integrity.
• For educators: Incorporate critical AI literacy into teaching so students learn how to use tools responsibly and reflectively.
• For researchers and editors: Follow COPE principles by rejecting AI authorship but requiring clear, specific declarations of AI use in manuscripts.
• For researchers and editors: Protect confidentiality — never upload unpublished data or sensitive material into public AI systems; rely on secure or local environments instead.