Generative Literature: The Role of Artificial Intelligence in the Creative Writing Process

Bayu Ade Prabowo, Rahmanti Asmarani

Abstract


This study explores the emerging phenomenon of AI-generated literature and its implications for creative writing, focusing on the characteristics of AI-generated texts, the impact of AI-human collaboration on the creative process, and the challenges posed by these technologies for traditional concepts of authorship, originality, and creativity. Through a comparative analysis of selected AI-generated literary works and a case study of the "Pharmako-AI" project, this research reveals the distinct stylistic, thematic, and structural features of AI-generated literature, as well as the complex dynamics of AI-human collaboration in the creative process. The findings suggest that while AI can serve as a powerful tool for creative exploration and experimentation, it also has limitations in terms of consistency, coherence, and emotional depth, and requires significant human input and judgment to shape the final literary output. The study contributes to the understanding of AI in creative writing by providing concrete insights into the capabilities and limitations of these technologies, and by highlighting the need for new frameworks and models to understand the nature of creative agency in the age of AI. The implications of AI-generated literature for the field of literature and future literary practices are discussed, including the potential for new forms of literary expression, new modes of authorship and collaboration, and new challenges to traditional concepts of originality and creativity. The study concludes with recommendations for future research, emphasizing the need for interdisciplinary collaboration and the development of new theoretical and methodological approaches to analyze and evaluate AI-generated literature. 


Keywords


AI-generated literature; AI-human collaboration; authorship; creative writing; creativity

Full Text:

PDF

References


Alabdulkarim, A., Li, S., & Peng, X. (2021). Automatic story generation: Challenges and attempts. Proceedings of the Third Workshop on Narrative Understanding, 72–83. https://doi.org/10.18653/v1/2021.nuse-1.8

Allado-McDowell, K. (2020). Pharmako-AI. Ignota Books.

Anantrasirichai, N., & Bull, D. (2022). Artificial Intelligence in the creative industries: A review. Artificial Intelligence Review, 55(1), 589–656. https://doi.org/10.1007/s10462-021-10039-7

Asyifa, P. A., & Daulay, E. (2024). The influence of grammarly application to improve students’ writing skills in recount text. Allure Journal, 4(2), 97–105. https://doi.org/10.26877/allure.v4i2.19272

Boden, M. A. (2004). The creative mind. Routledge. https://doi.org/10.4324/9780203508527

Boden, M. A. (2014). Creativity and Artificial Intelligence. In The Philosophy of Creativity (pp. 224–244). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199836963.003.0012

Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications Ltd.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 1877–1901). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf

Burgess, M. (2016). Google’s AI has written some amazingly mournful poetry. WIRED. https://www.wired.com/story/google-artificial-intelligence-poetry/

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods Approaches (5th Edition). SAGE Publications, Inc.

Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. ArXiv: Machine Learning. https://api.semanticscholar.org/CorpusID:11319376

Esser, F., & Vliegenthart, R. (2017). Comparative research methods. In The International Encyclopedia of Communication Research Methods (pp. 1–22). Wiley. https://doi.org/10.1002/9781118901731.iecrm0035

Gao, M., Hu, X., Ruan, J., Pu, X., & Wan, X. (2024). LLM-based NLG evaluation: Current status and challenges. ArXiv: Machine Learning. http://arxiv.org/abs/2402.01383

Gee, J. P. (2014). An introduction to discourse analysis. Routledge. https://doi.org/10.4324/9781315819679

Ippolito, D., Yuan, A., Coenen, A., & Burnam, S. (2022). Creative writing with an AI-powered writing assistant: Perspectives from professional writers. 1–18. http://arxiv.org/abs/2211.05030

Kain, P. (1998). How to do a close reading. Harvard University.

Li, J., Galley, M., Brockett, C., Gao, J., & Dolan, B. (2016). A persona-based neural conversation model.

Lomas, J. D., Maden, W. van der, Bandyopadhyay, S., Lion, G., Litowsy,

Y., Xue, H., & Desmet, P. (2023). The alignment of AI emotions: Human ratings of the emotions expressed by GPT-3, DALL-E and stable diffusion. Frontiers. https://doi.org/10.13140/RG.2.2.11123.81441

Manjavacas, E., Karsdorp, F. B., Burtenshaw, B., & Kestemont, M. (2017). Synthetic literature: Writing science fiction in a co-creative process. Proceedings of the Workshop on Computational Creativity in Natural Language Generation (CC-NLG 2017), 29–37.

Mirowski, P., Mathewson, K. W., Pittman, J., & Evans, R. (2023). Co-writing screenplays and theatre scripts with language models: Evaluation by industry professionals. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–34. https://doi.org/10.1145/3544548.3581225

Parwati, I., & Sugesti, I. (2023). Exploring EFL students’ strategies in writing research background at one of the universities in Cirebon. Allure Journal, 3(1), 38–47. https://doi.org/10.26877/allure.v3i1.13352

Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). SAGE Publications.

Perera, R., & Nand, P. (2017). Recent advances in natural language generation: A survey and classification of the empirical literature. Computing and Informatics, 36(1), 1–32. https://doi.org/10.4149/cai_2017_1_1

Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. 1–12.

Sa’idah, N., & Permatasari, R. (2024). A stylistic analysis of figurative language and its functions in the Four (Deluxe) album by One Direction. Allure Journal, 4(1), 23–33. https://doi.org/10.26877/allure.v4i1.16951

Starnino, C. (2022). Robots are writing poetry, and many people can’t tell the difference. The Walrus. https://thewalrus.ca/ai-poetry/

Stojanovic, L., Radojcic, V., Savic, S., Sarcevic, D., & Cvetković, A. S. (2023). The influence of Artificial Intelligence on creative writing: Exploring the synergy between AI and creative authorship. International Journal of Engineering Inventions, 12(12), 70–74.

Streitfeld, D. (2018). Computer stories: A.I. is beginning to assist novelists. The New York Times. https://www.nytimes.com/2018/10/18/technology/ai-is-beginning-to-assist-novelists.html

Tsao, J., & Nogues, C. (2024). Beyond the author: Artificial Intelligence, creative writing and intellectual emancipation. Poetics, 102, 101865. https://doi.org/10.1016/j.poetic.2024.101865

Varshney, L. R., Keskar, N. S., & Socher, R. (2019). Pretrained AI models: Performativity, mobility, and change. ArXiv, abs/1909.0. https://api.semanticscholar.org/CorpusID:202538698

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial Intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167

Woo, D. J., Wang, Y., & Susanto, H. (2022). Student-AID creative writing: Pedagogical strategies for applying natural language generation in schools. OSF.

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE Publications.

Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019). Defending against neural fake news. In H. Wallach, H. Larochelle, A. Beygelzimer, F. dtextquotesingle Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 32). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2019/file/3e9f0fc9b2f89e043bc6233994dfcf76-Paper.pdf

Zhu, J., Xia, Y., Wu, L., He, D., Qin, T., Zhou, W., Li, H., & Liu, T. Y. (2020). Incorporating Bert into neural machine translation. 8th International Conference on Learning Representations, ICLR 2020, 1–18.




DOI: https://doi.org/10.26877/allure.v5i1.19959

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Bayu Ade Prabowo, Rahmanti Asmarani

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License
ALLURE Journal by English Education Study Program, Universitas PGRI Semarang is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.