Academic Writing with GPT-3.5: Reflections on Practices, Efficacy and Transparency
Open accessAbstract
The debate around the use of GPT-3.5 has been a popular topic among academics since the release of ChatGPT. Whilst some have argued for the advantages of GPT-3.5 in enhancing academic writing, others have raised concerns such as plagiarism, the spread of false information, and ecological issues. The need for finding ways to use GPT-3.5 models transparently has been voiced, and suggestions have been made on social media as to how to use GPT-3.5 models in a smart way. Nevertheless, to date, there is a lack of literature which clearly outlines how to use GPT-3.5 models in academic writing, how effective they are, and how to use them transparently. To address this, I conducted a personal experience experiment with GPT-3.5, specifically by using text-davinci-003 model of OpenAI, for writing this article. I identified five ways of using GPT-3.5: Chunk Stylist, Bullet-to-Paragraph, Talk Textualizer, Research Buddy, and Polisher. I reflected on their efficacy, and commented on their potential impact on writing ethics. Additionally, I provided a comprehensive document which shows the prompts I used, results I got from GPT-3.5, the final edits and visually compares those by showing the differences in percentage.
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The quality of what is generated by GenAI has been found variable.
“The quality of what is generated by GenAI has been found variable (Buruk, 2023, Ironsi & Solomon Ironsi, 2025).”
From If ChatGPT can do it, where is my creativity? generative AI boosts performance but diminishes experience in creative writing (2025) · cites this paper as Buruk, 2023
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ChatGPT is fun, but not an author
Thorp, H. H. (2023) · Science · doi:10.1126/science.adg7879 · Cited by 1,437 · see citing works
How Large Language Models are Transforming Machine-Paraphrase Plagiarism
Wahle, J. P., Ruas, T., Kirstein, F., & Gipp, B. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.62 · Cited by 35 · see citing works
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- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and others. 2020. Language models are few-shot learners. Advances in neural information processing systems 33: 1877-1901.
- Ke-Li Chiu, Annie Collins, and Rohan Alexander. 2021. Detecting hate speech with gpt-3. arXiv preprint arXiv:2103.12407. · arXiv
- Vanya Cohen and Aaron Gokaslan. 2020. OpenGPT-2: Open Language Models and Implications of Generated Text. XRDS 27, 1: 26- 30. https://doi.org/10.1145/3416063 · DOI
- Vijay Kumar Jain, Karthikeyan P Iyengar, and Raju Vaishya. 2022. Is the English language a barrier to the non-English-speaking authors in academic publishing? Postgraduate Medical Journal 98, 1157: 234-235. https://doi.org/10.1136/postgradmedj-2020- 139243 · DOI
- Tiffany H Kung, Morgan Cheatham, Arielle Medinilla, ChatGPT, Czarina Sillos, Lorie De Leon, Camille Elepano, Marie Madriaga, Rimel Aggabao, Giezel Diaz-Candido, and others. 2022. Performance of ChatGPT on USMLE: Potential for AI-Assisted Medical Education Using Large Language Models. medRxiv: 2022-12.
- Kris McGuffie and Alex Newhouse. 2020. The radicalization risks of GPT-3 and advanced neural language models. arXiv preprint arXiv:2009.06807. · arXiv
- June Oshiro, Suzanne L. Caubet, Kelly E. Viola, and Jill M. Huber. 2020. Going Beyond "Not Enough Time": Barriers to Preparing Manuscripts for Academic Medical Journals. Teaching and Learning in Medicine 32, 1: 71-81. https://doi.org/10.1080/10401334.2019.165914 · DOI
- Tobias Schmohl, Alice Watanabe, Nadine Fröhlich, Dominikus Herzberg, and others. 2020. How Artificial Intelligence can improve the Academic Writing of Students. In Conference Proceedings. The Future of Education 2020.
- Chris Stokel-Walker and Richard Van Noorden. 2023. What ChatGPT and generative AI mean for science. Nature 614, 7947: 214-216. https://doi.org/10.1038/d41586-023-00340-6 · DOI · Paper page
- Angel Urbina-Garcia. 2020. What do we know about university academics' mental health? A systematic literature review. Stress and Health 36, 5: 563-585. https://doi.org/10.1002/smi.2956 · DOI
- 2020. Next chapter in artificial writing. Nature Machine Intelligence 2, 8: 419-419. https://doi.org/10.1038/s42256-020-0223-0 · DOI
- 2021. Compare text online -Show Text Difference - countwordsfree.com. Retrieved from https://countwordsfree.com/comparetexts 22. 2023. Tools such as ChatGPT threaten transparent science; here are our ground rules for their use. Nature 613, 7945: 612-612. https://doi.org/10.1038/d41586-023-00191-1 · DOI · Paper page
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Generative AI for corpus approaches to discourse studies: A critical evaluation of ChatGPT
Curry, N., Baker, P., & Brookes, G. (2024) · Applied Corpus Linguistics · doi:10.1016/j.acorp.2023.100082 · Cited by 102 · see citing works
Mei, P., Brewis, D. N., Nwaiwu, F., Sumanathilaka, D., Alva-Manchego, F., & Demaree-Cotton, J. (2025) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2025.100140 · Cited by 50 · see citing works
- Authors
- Buruk, O. '.
- Affiliations
- Tampere University (FI)
- Corresponding author
- Buruk, Oğuz 'Oz'
- ORCID
- Buruk, Oğuz 'Oz'
- Publisher
- Center for Open Science
- Published
- 2023-03-06
- OpenAlex
- W4323363618
- Cited by
- 3
- References
- 2
- Citations by year
- 2025: 1 · 2023: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- MetArXiv (OSF Preprints) (submittedVersion)
- MetArXiv (OSF Preprints) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- GPT-3.5, academic writing, ChatGPT, prompt engineering, AI-assisted writing, ethics of writing, plagiarism, transparency
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