CoChat
Sign Up
Cited by 39Literary and Philosophical StudiesArt, Technology, and CultureCybernetics and Technology in Society

The poetry of prompts: The collaborative role of generative artificial intelligence in the creation of poetry and the anxiety of machine influence

Open access
Hutson, James, Schnellmann, Ana·2023·Global Journal of Computer Science and Technology
DOI 10.34257/gjcstdvol23is1pg1
Open this paper in CoChat — free accountOpen PDF

Abstract

2022 has been heralded as the year of generative artificial intelligence (AI). Generative AI like ChatGPT and Stable Diffusion, along with a host of others, launched late in the year and immediately disrupted the status quo of the literary and art worlds, leading to outcries to ban “AI Art” and spawning an entirely new market of NFTs. Fears over the “death of the artist” and the “death of college composition,” however, are unfounded when considering the historical adoption of emerging technologies by creatives and the reconsideration of authorship that began with post structuralism and the Foucauldian Death of the Author in 1967. Contemporary scholarship has faced challenges in reconciling the function of the human author in conjunction with artificial intelligence (AI) due to the progressive sophistication and self-sufficiency of generative code. Nonetheless, it is erroneous to establish the threshold for authorship based on the development or advancement of AI or robotics, as it falls within the realm of ontology. Instead, assertions of AI authorship stem from a romanticized perception of both authorship and AI during a period in which neither holds significance.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.34257/gjcstdvol23is1pg1

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Human-AI collaboration received the highest mean quality ratings, but participants' rankings reflected subjective taste and stylistic distinctiveness appealed to niche audiences, explaining Version A's high variance; the central strength of original human-authored text was conveying subtle and culturally specific emotional subtext, which AI-generated content conspicuously lacked, though the same

    “The human-AI collaboration received the highest mean quality ratings in the experimental phase, and the focus group sessions shed considerable light on these results, particularly for Version A.”

    From How Creative Is AI Writing? Generative and Collaborative AI in Japanese Fiction (2026) · cites this paper as Hutson and Schnellmann (2023)

    Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • The absence of a clear quality advantage for human-AI collaboration over original human writing complicates more optimistic accounts of AI's creative potential.

    “the absence of a clear quality advantage for human-AI collaboration over original human writing complicates more optimistic accounts of AI's creative potential (Pretsch, 2023; Hutson and Schnellmann, 2023; Çelik, 2023).”

    From How Creative Is AI Writing? Generative and Collaborative AI in Japanese Fiction (2026) · cites this paper as Hutson and Schnellmann, 2023

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 61

  1. Aggarwal, C. C. (2018). Neural networks and deep learning. Springer, 10(978), 3.
  2. Alibek, B., Kargabayev, D., & Abdyjaparov, A. (2020). Poetry generation using deep learning models. In Proceedings of the 2020 International Conference on Artificial Intelligence and Data Science (pp. 151-155).
  3. Amerika, M. (2022). My Life as an Artificial Creative Intelligence. Stanford University Press.
  4. Anthony, L. (2004, December). AntConc: A Learner and Classroom Friendly, Multi-Platform Corpus Analysis Toolkit. In An Interactive Workshop on Language e-Learning (pp. 7-14).
  5. Babu, D. P. (2013). Automated analysis of Learner's Research Article writing and feedback generation through Machine Learning and Natural Language Processing (Doctoral dissertation, Iowa State University).
  6. Bate, W. J. (2009). John Keats. Harvard University Press.
  7. Blake, R. J. (2011). Current trends in online language learning. Annual review of applied linguistics, 31, 19-35.
  8. Bloom, H. (1997). The anxiety of influence: A theory of poetry. Oxford University Press, USA.
  9. Boden, M. A. (2004). The creative mind: Myths and mechanisms. Routledge.
  10. Burstein, J. (2009, March). Opportunities for natural language processing research in education. In International Conference on Intelligent Text Processing and Computational Linguistics (pp. 6- 27). Springer, Berlin, Heidelberg.
  11. Cahill, A., & Evanini, K. (2020). Natural language processing for writing and speaking. In Handbook of Automated Scoring (pp. 69-92). Chapman and Hall/CRC.
  12. Chang, J. S., & Chang, Y. C. (2004, December). Computer assisted language learning based on corpora and natural language processing: the experience of project CANDLE. In An Interactive Workshop on Language e-Learning (pp. 15-23).
  13. Cohen, H. (1988). How to Draw Three People in a Botanical Garden. In AAAI 1988.
  14. Coniam, David. 2004. Concordancing oneself: Constructing individual textual profiles. International Journal Corpus Linguistics, 9(2), 271-298.
  15. Cox, A. M. (2021). Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions. International Journal of Educational Technology in Higher Education, 18(1), 3.
  16. Creely, E. (2023). Conceiving Creativity and Learning in a World of Artificial Intelligence: A Thinking Model. In Creative Provocations: Speculations on the Future of Creativity, Technology & Learning (pp. 35-50). Cham: Springer International Publishing.
  17. Crossley, S. A., Allen, L. K., Kyle, K., & McNamara, D. S. (2014). Analyzing discourse processing using a simple natural language processing tool. Discourse Processes, 51(5-6), 511-534.
  18. de Bruin, L., & Merrick, B. (2023). Creative Pedagogies with Technology: Future proofing teaching training in Music. In Creative Provocations: Speculations on the Future of Creativity, Technology & Learning (pp. 143-157). Cham: Springer International Publishing.
  19. Franceschelli, G., & Musolesi, M. (2021). Creativity and machine learning: A survey. arXiv preprint arXiv:2104.02726. · arXiv
  20. Gilbert, S. M., & Gubar, S. (Eds.). (1979). Shakespeare's sisters: Feminist essays on women poets (Vol. 263). Indiana University Press.
  21. Goldstein, J. A., Sastry, G., Musser, M., DiResta, R., Gentzel, M., & Sedova, K. (2023). Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations. arXiv preprint arXiv:2301.04246. · arXiv
  22. Gurkaynak, G., Yilmaz, I., & Haksever, G. (2016). Stifling artificial intelligence: Human perils. Computer Law & Security Review, 32(5), 749-758.
  23. Hager, T. (2022). Reconstructing subjectivity from silence: Julia Margaret Cameron, Mary Ryan and the Victorian archive. Women's History Review, 31(5), 760-783.
  24. Hall, C. J., Smith, P. H., & Wicaksono, R. (2017). Mapping applied linguistics: A guide for students and practitioners. Routledge.
  25. Hamzelou, J. (2023). Neuroscientists listened in on people's brains for a week. They found order and chaos. MIT Technology Review. (February 7, 2023). Retrieved from: https://www-technologyreview-com. cdn.ampproject.org/c/s/www.technologyreview.com /2023/02/07/1067951/brains-week-order-chaos/ amp/)
  26. Herbrechter, S., & Callus, I. (2008). What is a post human is treading?.Angelaki: Journal of Theoretical Humanities, 13(1), 95-111.
  27. Hunston, S. (2002). Corpora in Applied Linguistics. Cambridge: Cambridge University Press.
  28. Ibrahim, M., & Ahmad, R. (2010, May). Class diagram extraction from textual requirements using natural language processing (NLP) techniques. In 2010 Second International Conference on Computer Research and Development (pp. 200-204). IEEE.
  29. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.
  30. Kantosalo, A., & Riihiaho, S. (2019). Quantifying co- creative writing experiences. Digital Creativity, 30(1), 23-38.
  31. Keats, J. (1848). Life, letters, and literary remains, of John Keats. GP Putnam.
  32. Keats, J., & Cook, E. (1990). John Keats. Oxford: Oxford University Press.
  33. Kenworthy, J. B., Miller, N., Collins, B. E., Read, S. J., & Earleywine, M. (2011). A trans-paradigm theoretical synthesis of cognitive dissonance theory: Illuminating the nature of discomfort. European Review of Social Psychology, 22(1), 36-113.
  34. Kim, H., Kim, J., & Lee, J. (2021). Imitation of famous poets using AI models. Journal of Computational Intelligence, 27(1), 69-78.
  35. Kimmel, M., & Hristova, D. (2021). The micro- genesis of improvisational co-creation. Creativity Research Journal, 33(4), 347-375.
  36. Li, X., & Ouyang, L. (2019). Challenges in the imitation of human poetry by AI. Artificial Intelligence Review, 52(3), 593-610.
  37. Liu, B., Papangelis, A., Ultes, S., Rastogi, A., Chen, Y. N., Spithourakis, G.,... & Shi, W. (2022, May). Proceedings of the 4th Workshop on NLP for Conversational AI. In Proceedings of the 4th Workshop on NLP for Conversational AI.
  38. Johns, T. 1997. Contexts: the Background, Development and Trialling of a Concordance-based CALL Program. In A. Wichmann, S. Fligelstone, T. McEnery, and G. Knowles (eds.), Teaching and Language Corpora. London: Longman, pp.100-115.
  39. McCarthy, P., & Boonthum-Denecke, C. (Eds.). (2011). Applied natural language processing. Information Science Reference.
  40. McNamara, D. S., Crossley, S. A., & Roscoe, R. (2013). Natural language processing in an intelligent writing strategy tutoring system. Behavior research methods, 45(2), 499-515.
  41. Meehan, J. (1977). TALE-SPIN, an Interactive Program That Writes Stories. In IJCAI 1977. Morgan Kaufmann Publishers Inc., 91-98.
  42. Mittelstadt, B., Russell, C., & Wachter, S. (2019, January). Explaining explanations in AI. In Proceedings of the conference on fairness, accountability, and transparency (pp. 279-288).
  43. Perez, R. (2017). Mexica: 20 Years-20 Stories [20 años-20 historias]. Counterpath Press.
  44. Peters, S. E. (2017). Using the alchemical Magnum Opus as a guideline for creative writing (Doctoral dissertation, Stellenbosch: Stellenbosch University).
  45. Plate, D. & Hutson, J. (2022).Augmented Creativity: Leveraging Natural Language Processing for Creative Writing,Art and Design Review, vol. 10, no. 3 (August, 2022): 376-388
  46. Pope, R. (2005). Creativity: Theory, history, practice. Routledge.
  47. Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 1-13.
  48. Rao, D., & McMahan, B. (2019). Natural language processing with PyTorch: build intelligent language applications using deep learning. "O'Reilly Media, Inc.".
  49. Roe, N. (2012). John Keats: a new life. Yale University Press.
  50. Roemmele, M., & Gordon, A. S. (2018, March). Automated assistance for creative writing with anrnn language model. In Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion (pp. 1-2).
  51. Roemmele, M., & Gordon, A. (2018, June). Linguistic features of helpfulness in automated support for creative writing. In Proceedings of the First Workshop on Storytelling (pp. 14-19).
  52. Rossetti, W. M., & Anderson, J. P. (1887). Life of John Keats (Vol. 10). London: W. Scott.
  53. Sharples, M., & y Pérez, R. P. (2022). Story Machines: How Computers Have Become Creative Writers. Routledge.
  54. Snow, E. L., Allen, L. K., Jacovina, M. E., Crossley, S. A., Perret, C. A., & McNamara, D. S. (2015). Keys to detecting writing flexibility over time: entropy and natural language processing. Journal of Learning Analytics, 2(3), 40-54.
  55. Vaezi, M., & Rezaei, S. (2019). Development of a rubric for evaluating creative writing: a multi-phase research. New Writing, 16(3), 303-317.
  56. Varela, F. J., Thompson, E., & Rosch, E. (2017). The embodied mind, revised edition: Cognitive science and human experience. MIT press.
  57. Vladeck, D. C. (2014). Machines without principals: liability rules and artificial intelligence. Washington Literature Review, 89, 117.
  58. Yang, D., Zhou, Y., Zhang, Z., Li, T. J. J., & LC, R. (2022, March). AI as an Active Writer: Interaction strategies with generated text in human-AI collaborative fiction writing. In Joint Proceedings of the ACM IUI Workshops (Vol. 10).
  59. Zhang, H., Magooda, A., Litman, D., Correnti, R., Wang, E., Matsmura, L. C., ... & Quintana, R. (2019, July). eRevise: Using natural language processing to provide formative feedback on text evidence usage in student writing. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 9619-9625).
  60. Zhang, B., Zhu, J., &Su, H. (2023). Toward the third generation artificial intelligence. Science China Information Sciences, 66(2), 1-19.
  61. Zhang, Y., Liao, Q. V., & Bellamy, R. K. (2020, January). Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 295-305).

Cited by

Sorted by citations

Authors
Hutson, J., & Schnellmann, A.
Affiliations
Lindenwood University (US)
ORCID
Hutson, James
Venue
Global Journal of Computer Science and Technology
Publisher
Global Journals
Published
2023-04-10
Pages
1-14
ISSN
  • 0975-4172 (ISSN-L)
  • 0975-4350
DOI
10.34257/gjcstdvol23is1pg1
OpenAlex
W4365517013
Cited by
39
References
1
Citations by year
2026: 2 · 2025: 24 · 2024: 12 · 2023: 1
FWCI
38.22
Type
journal article
Language
en
Open access
Licence
Locations
Global Journal of Computer Science and Technology (publishedVersion, cc-by)
Topics
  • Social Sciences › Political Science and International Relations › Literary and Philosophical Studies
  • Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
Keywords
generative AI, AI-generated poetry, authorship, death of the author, Michel Foucault, poststructuralism, ChatGPT, Stable Diffusion, NFTs, AI-generated art, co-creation, human-AI collaboration

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review