Distinguishing Human Creativity from AI-Generated Literary Texts
Keywords:
AI, ChatGPT, text generation, Human writing, AI-GeneratedAbstract
Artificial intelligence (AI) technology has grown rapidly and authors can write paragraphs and sentences that accept such acts to human compositions, scholars have been examining the differences between computer-generated works and those written by humans. This review is an analysis and combination of papers that do a comparative study of contemporary literature by human and artificial intelligence. Theoretical techniques such as formal language, structure style, and narration are used in the study to make comparisons between human writing features and AI text patterns. While AI systems can reproduce certain essential aspects of literature in text, they capture a limit in producing new content and comprehensive ideas expressed in language. The problem statement of the research consists of describing scientific intervals in the separation of AI substance from human creativity, as well as philosophical, cultural, and ethical issues of AI in the literary context. Current research has been identified as having suffered from significant weaknesses in matters of complicated literary consistency and literary relations between authors and cultural knowledge. The study concludes that collaborative research methodology and critical self-evaluation are necessary because AI transforms how individuals make and analyze literature.
References
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., McGrew, B. (2023). Gpt-4 technical report. arXiv preprint arXiv:2303.08774. https://doi.org/10.48550/arXiv.2303.08774
Al-Amin, M., Ali, M. S., Salam, A., Khan, A., Ali, A., Ullah, A., Chowdhury, S. K. (2024). History of generative Artificial Intelligence (AI) chatbots: past, present, and future development. arXiv preprint arXiv:2402.05122. https://doi.org/10.48550/arXiv.2402.05122
Amirjalili, F., Neysani, M., Nikbakht, A. (2024, March). Exploring the boundaries of authorship: A comparative analysis of AI-generated text and human academic writing in English literature. In Frontiers in Education (Vol. 9, p. 1347421). Frontiers Media SA. https://doi.org/10.3389/feduc.2024.1347421
Baek, T. H., Kim, H. J., Kim, J. (2025). AI-generated recommendations: Roles of language style, perceived AI human-likeness, and recommendation agent. International Journal of Hospitality Management, 126, 104106. https://doi.org/10.1016/j.ijhm.2025.104106
Bal, M. (2009). Narratology: Introduction to the theory of narrative. University of Toronto Press. https://hdl.handle.net/11245/1.128688
Bevilacqua, M., Oketch, K., Qin, R., Stamey, W., Zhang, X., Gan, Y., Abbasi, A. (2025). When automated assessment meets automated content generation: Examining text quality in the era of gpts. ACM Transactions on Information Systems, 43(2), 1-36. https://doi.org/10.1145/3702639
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
Ching, V., & Mothi, D. (2025). AI for Creatives: Unlocking Expressive Digital Potential. CRC Press. https://doi.org/10.1201/9781003533016
Eagleton, T. (2011). Literary theory: An introduction. John Wiley & Sons. Available at: http://thuvienso.bvu.edu.vn/handle/TVDHBRVT/15442
Fedulenkova, T. N. (2018). Stylistics. A resource book for students. Language and Culture, (12), 118-123. https://doi.org/10.17223/24109266/12/11
Forman, J., & Damschroder, L. (2007). Qualitative content analysis. In Empirical methods for bioethics: A primer (pp. 39-62). Emerald Group Publishing Limited. https://doi.org/10.1016/S1479-3709(07)11003-7
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT press. Available at: http://www.gbv.de/dms/weimar/toc/1633735990_toc.pdf
Hundvin, M. H. (2022). Generating sample code snippets from Lark grammar specifications (Master's thesis, The University of Bergen). Available at: https://hdl.handle.net/11250/3045511
Jasim, S. A. (2019). Disintegration of Gender Equality in Alan Moore and David Lloyd’s V for Vendetta. Available at: http://proceedings.sriweb.org/
Jasim, Y. (2021). Benefits of learning a second language. Available at SSRN 3895362. https://dx.doi.org/10.2139/ssrn.3895362
Jasim, Y. A. (2018). Improving intrusion detection systems using artificial neural networks. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 7(1), 49-65. Available at: https://dialnet.unirioja.es/servlet/articulo?codigo=6512702
Jockers, M. L. (2013). Macroanalysis: Digital methods and literary history. University of Illinois Press. Available at: https://muse.jhu.edu/book/21978/
Kalantzis, M., & Cope, B. (2025). Literacy in the time of Artificial Intelligence. Reading Research Quarterly, 60(1), e591. https://doi.org/10.1002/rrq.591
Kaul, V., Enslin, S., & Gross, S. A. (2020). History of artificial intelligence in medicine. Gastrointestinal endoscopy, 92(4), 807-812. https://doi.org/10.1016/j.gie.2020.06.040
Karem, M. A., Raewf, M. B., Thabit, T. H., & Shakir, R. M. (2022). The factors that influence knowledge sharing in educational institutions. Cihan University-Erbil Journal of Humanities and Social Sciences, 6(1), 69-74. https://doi.org/10.24086/cuejhss.v6n1y2022.pp69-74
Khalifa, M., & Albadawy, M. (2024). Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, 100145. https://doi.org/10.1016/j.cmpbup.2024.100145
Kusmiatun, A., Efendi, D., Al Pansori, M. J., Judijanto, L., Sariasih, Y., & Saddhono, K. (2024, May). The Power of AI in Generating Novel Based and Impactful Character Development for Fiction Story. In 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 1555-1561). IEEE. https://doi.org/10.1109/ICACITE60783.2024.10616700
Law, L. (2024). Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review. Computers and Education Open, 100174. https://doi.org/10.1016/j.caeo.2024.100174
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Massoudi, A. H., Birdawod, H. Q., & Raewf, M. B. (2023). Personal digital marketing influence on successful marketing campaign in today’s digital age. Cihan University-Erbil Journal of Humanities and Social Sciences, 7(1), 158-165. https://doi.org/10.24086/cuejhss.v7n1y2023.pp158-165
Muhamad, A., Windi, M., Saeed, M., AbdulAmeer, S. Jasim, Y. A. (2021). High-performance deep learning to detection and tracking tomato plant leaf predict disease and expert systems. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(2). https://doi.org/10.14201/ADCAIJ202110297122
Pacini, P. (2022, September). From Beat Generation to Hacker Generation: On/1 The Road: Experimental Road Narratives. In Beat Times: Temporalities in Beat Writing. EUROPEAN BEAT STUDIES NETWORK 10th ANNUAL CONFERENCE. Available at: https://hal.science/hal-04073407v1
Raewf, M., Aissa, S. A. H., & Thabit, T. (2021). The impact of quality, cost, and lead time on competitive advantage: Case of SMEs operating in Iraq. Economic Studies Journal, 19(03). Available at SSRN: https://ssrn.com/abstract=3945123
Raita, Y. (2019). The Day a Computer Writes a Novel. Pobrane z: https://my.vanderbilt.edu.
Tawfeeq, T. M., Awqati, A. J., & Jasim, Y. A. (2023). The ethical implications of chatgpt ai chatbot: A review. JMCER, 2023, 49-57. Available at: https://jmcer.org/research/the-ethical-implications-of-chatgpt-ai-chatbot-a-review/
Thabit, T. H., & Jasim, Y. A. (2015). A manuscript of knowledge representation. International Journal of Human Resource & Industrial Research, 4(4), 10-21. Available at SSRN: https://ssrn.com/abstract=3665036
Thabit, T. H., & Jasim, Y. A. A. (2017). The role of social networks in increasing the activity of E-learning. Social Media Shaping e-Publishing and Academia, 35-45. https://doi.org/10.1007/978-3-319-55354-2_4
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Wang, L., Chen, X., Deng, X., Wen, H., You, M., Liu, W., ... & Li, J. (2024). Prompt engineering in consistency and reliability with the evidence-based guideline for LLMs. NPJ digital medicine, 7(1), 41. https://doi.org/10.1038/s41746-024-01029-4
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Copyright (c) 2025 Yaser A. Jasim, Ali Jalal Awqati
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Copyright © 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). You may not alter or transform this work in any way without permission from the authors. Non-commercial use, distribution, and copying are permitted, provided that appropriate credit is given to the authors and Al-Hadba University.
