AI writing a manuscript

AI in Academic Writing: Navigating the New Landscape of Manuscript Preparation

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Avery Sinclair in Tech & Innovation December 2025 • 5 min read.

"Explore the ethical considerations, detection methods, and shifting perceptions surrounding AI's role in scholarly publications."


Artificial intelligence (AI) is rapidly changing many aspects of our world, and academia is no exception. While AI tools offer exciting possibilities for research and analysis, they also raise concerns about academic integrity and the potential for misuse. Large Language Models (LLMs), like ChatGPT and Bard, are now capable of assisting with various stages of the writing process, from refining grammar to generating original content.

This has led to a debate within the academic community about the appropriate use of AI in manuscript preparation. Some researchers see AI as a valuable tool for improving efficiency and clarity, while others worry about the potential for plagiarism, bias, and a decline in original thought. As a result, discussions have emerged about the need for authors to disclose their use of AI tools and for journals to implement detection methods to ensure the integrity of published research.

This article delves into the perceptions and detection of AI use in academic manuscript preparation. We'll explore the concerns surrounding AI writing tools, discuss how academics view the necessity of reporting AI use, and examine how current AI detection methods respond to AI-assisted writing. The aim is to provide a clear and balanced perspective on this evolving landscape, helping researchers, educators, and publishers navigate the challenges and opportunities presented by AI in academic writing.

The Rising Tide of AI in Academic Writing: Benefits and Concerns

AI writing a manuscript

The integration of AI tools into academic writing is driven by their potential to streamline and enhance various tasks. As Korinek (2023) points out, LLMs can significantly improve the grammar, style, and clarity of academic texts, allowing researchers to focus on the core ideas and arguments. This is particularly beneficial for non-native English speakers who may struggle with the nuances of academic writing.

However, the ease and accessibility of AI writing tools also raise several ethical and practical concerns. A key worry is the potential for AI to generate inaccurate or misleading content. As highlighted by Altmäe, Sola-Leyva, and Salumets (2023), LLMs can produce text with incorrect math, flawed reasoning, and even fabricated citations. This phenomenon, known as 'hallucination,' poses a significant risk to the integrity of academic research.

  • Risk of Plagiarism: AI tools are trained on vast datasets of existing text, raising concerns about plagiarism if authors do not properly attribute AI-generated content.
  • Bias and Representation: AI algorithms can perpetuate existing biases in the data they are trained on, leading to skewed or unfair representations in academic writing.
  • Lack of Transparency: The 'black box' nature of LLMs makes it difficult to understand how they generate text, raising questions about accountability and the potential for unintended consequences.
  • Erosion of Originality: Over-reliance on AI writing tools could stifle creativity and critical thinking, leading to a decline in the originality and depth of academic research.
These concerns have led to calls for greater transparency and stricter guidelines regarding the use of AI in academic writing. Many journals and conferences are now considering or implementing policies that require authors to disclose any AI tools used in the preparation of their manuscripts.

Navigating the Future: Open Questions and Research Directions

The integration of AI into academic writing is a rapidly evolving field, and many questions remain unanswered. As AI technology continues to advance, it is crucial for the academic community to engage in open and informed discussions about its appropriate use and ethical implications. Further research is needed to develop robust methods for detecting AI-generated content, establish clear guidelines for AI disclosure, and explore the potential impact of AI on the quality and originality of academic research. Ultimately, the goal is to harness the power of AI to enhance, not undermine, the integrity and rigor of scholarly inquiry.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2311.1472,

Title: Perceptions And Detection Of Ai Use In Manuscript Preparation For Academic Journals

Subject: cs.cy cs.ai econ.gn q-fin.ec

Authors: Nir Chemaya, Daniel Martin

Published: 19-11-2023

Everything You Need To Know

1

How are Large Language Models impacting the landscape of academic writing?

Large Language Models like ChatGPT and Bard are transforming academic writing by assisting with various stages of manuscript preparation, from refining grammar to generating original content. This has sparked debate about the appropriate use of AI, with some viewing it as a tool for efficiency and clarity, while others are concerned about potential plagiarism, bias, and a decline in original thought. This shift necessitates authors disclosing AI use and journals implementing detection methods.

2

What are the primary ethical concerns associated with using AI in academic manuscript preparation?

Several ethical concerns arise from using AI in academic manuscript preparation. These include the risk of plagiarism due to AI tools being trained on vast datasets, the perpetuation of bias from skewed data in AI algorithms, a lack of transparency in how Large Language Models generate text, and the potential erosion of originality as over-reliance on AI tools could stifle creativity and critical thinking.

3

Can you elaborate on the phenomenon of 'hallucination' in the context of Large Language Models?

In the context of Large Language Models, 'hallucination' refers to the AI's tendency to generate inaccurate or misleading content, including incorrect math, flawed reasoning, and fabricated citations. Altmäe, Sola-Leyva, and Salumets (2023) highlight that this poses a significant risk to the integrity of academic research, as the information presented may not be factual or reliable. Verification of AI-generated content is crucial to avoid disseminating false information in scholarly publications.

4

What are some steps academic journals and conferences are taking to address the use of AI in submitted manuscripts?

Many journals and conferences are considering or implementing policies that require authors to disclose any AI tools used in the preparation of their manuscripts. This move towards greater transparency aims to ensure accountability and maintain the integrity of published research. Additionally, efforts are being made to develop robust methods for detecting AI-generated content to further safeguard against potential misuse.

5

What future research directions are essential to navigate the integration of AI into academic writing responsibly?

Several future research directions are vital for the responsible integration of AI into academic writing. These include developing robust methods for detecting AI-generated content, establishing clear guidelines for AI disclosure, and exploring the potential impact of AI on the quality and originality of academic research. Addressing these areas will help the academic community harness the power of AI to enhance scholarly inquiry without undermining its integrity and rigor. Continued open discussions about the ethical implications of AI use are also essential.

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