Artificial Intelligence in the humanities: Between innovation and challenges

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Article

Artificial Intelligence in the humanities: Between innovation and challenges

Nurali Chelebiyev

Received 12.06.2025, Revised 24.10.2025, Accepted 09.12.2025

https://doi.org/10.67524/verus5/1.2025.04

Retrieved from Vol. 1, No. 1, 2025

Pages 4-15

Abstract

The purpose of the study was to determine the boundaries and possibilities of using artificial intelligence technologies in the examination of literary and historical sources. The methodological strategy combined critical analysis of published findings, systematisation of tools, evaluation of representative practices in the digital humanities, and integration of these observations within a unified analytical framework that links technical procedures with interpretative conclusions. Particular attention was paid to case studies that illustrate the capacity of algorithms to reconstruct latent narratives and intertextual networks. The obtained findings demonstrate that topic modelling combined with clustering reliably identifies latent themes and their temporal dynamics in large text collections; contextual neural architectures provide consistent identification of persons, toponyms, and genre features across dispersed corpora; stylometric approaches, together with tools for detecting textual correspondences, enable localisation of shifts in authorial signal and reconstruction of borrowing chain. The study established that tools of geographical annotation allow the reconstruction of spatial trajectories of characters, correspondence, and routes. The analysis also indicated sensitivity of results to the quality of digitisation: errors in the recognition of printed and handwritten text altered thematic distributions and affected attribution accuracy, which required prior cleaning and normalisation. The study noted that generative systems are appropriately considered instruments of co-creation at three levels: operational (variation of formulations), procedural (structural organisation and iterative support), and conceptual (semantic integration and interpretative framing), while decisive responsibility remains with the human researcher. The ethical dimension confirmed the presence of risks related to bias in training collections, threats to confidentiality, and potential distortion of cultural meanings; requirements for transparency, documentation of the role of automated tools, and the involvement of domain expertise were outlined. The practical value of the study lies in the fact that the proposed framework provides researchers with a clear sequence of actions for reproducible analysis of textual corpora, ensures accurate attribution and interpretative validity, and offers instrumentally grounded guidelines for the secure integration of technologies into humanities projects

References
  1. American Historical Association. (2025). Guiding principles for artificial intelligence in history education. Washington: American Historical Association.
  2. Bankov, К. (2020). Platfospheres and sociocultural explosion of Web 2.0: The commercial centre of the digital semiosphere. Sign Systems Studies, 48(2-4), 246-270. doi: 10.12697/SSS.2020.48.2-4.04.
  3. Buleu, C.R. (2024). Literary works generated by artificial intelligence: Methodology, achievements, dilemmas. Dacoromania Litteraria, 11(1), 90-107.
  4. Casadei, P., Bloom, M., Camerani, R., Masucci, M., Siepel, J., & Ospina, J.V. (2023). Mapping the state of the art of creative cluster research: A bibliometric and thematic analysis. European Planning Studies, 31(12), 2531-2551. doi: 10.1080/09654313.2022.2158722.
  5. Chang, I.C., Yu, T.K., Chang, Y.J., & Yu, T.Y. (2021). Applying text mining, clustering analysis, and latent dirichlet allocation techniques for topic classification of environmental education journals. Sustainability, 13(19), article number 10856. doi: 10.3390/su131910856.
  6. Chauhan, U., & Shah, A. (2021). Topic modeling using latent Dirichlet allocation: A survey. ACM Computing Surveys, 54(7), 1-35. doi: 10.1145/3462478.
  7. Chun, J., & Elkins, K. (2023). The crisis of artificial intelligence: A new digital humanities curriculum for human-centred AI. International Journal of Humanities and Arts Computing, 17(2), 147-167. doi: 10.3366/ijhac.2023.0310.
  8. Colella, S. (2025). The language of the digital air: AI-generated literature and the performance of authorship. Humanities, 14(8), 1-17. doi: 10.3390/h14080164.
  9. Committee on Publication Ethics (COPE). (n.d.). Retrieved from https://publicationethics.org/.
  10. COPE Council. (2023). Authorship and AI tools. doi: 10.24318/cCVRZBms
  11. del Rio Riande, G., & Vitale, V. (2020). Recogito-in-a-box: From annotation to digital edition. Modern Languages Open, 1, article number 44. doi: 10.3828/mlo.v0i0.299.
  12. Doyle, R., & Conboy, K. (2020). Considering the merits of applying ‘liquid modernity’ as a theoretical lens for information systems research. In Proceedings of the 28th European conference on information systems (ECIS 2020), research-in-progress papers (Paper 90). Marrakech: Association for Information Systems.
  13. Duch, W. (2024). Artificial intelligence and the limits of the humanities. Er(r)go. Teoria-Literatura-Kultura, 48, 269-297.
  14. Elsharif, W., Alzubaidi, M., & Agus, M. (2025). Cultural Bias in text-to-image models: A systematic review of bias identification, evaluation and mitigation strategies. IEEE Access, 13, 122636-122659. doi: 10.1109/ACCESS.2025.3585745.
  15. Erslev, M.S. (2023). A poetics of misrepresentation. In A. Ensslin, J. Round & B. Thomas (Eds.), The Routledge companion to literary media (pp. 197-208). London: Routledge.  
  16. Floridi, L. (2023). The ethics of artificial intelligence: Principles, challenges, and opportunities. Oxford: Oxford University Press.
  17. Foka, A., & Griffin, G. (2024). AI, cultural heritage, and bias: Some key queries that arise from the use of GenAI. Heritage, 7(11), 6125-6136. doi: 10.3390/heritage7110287.
  18. Frontoni, E., Paolanti, M., Migliorelli, L., Pietrini, R., & Asimakopoulos, S. (2024). Artificial intelligence: The new frontier in digital humanities. Frontiers in Computer Science, 6, article number 1529826. doi: 10.3389/fcomp.2024.1529826.
  19. Gallent-Torres, C., Zapata-González, A., & Ortego-Hernando, J.L. (2023). The impact of generative artificial intelligence in higher education: A focus on ethics and academic integrity. Electronic Journal of Educational Research and Evaluation, 29(2). doi: 10.30827/relieve.v29i2.29134.
  20. Gefen, A. (2024). The author’s second death. Poetics Today, 45(2), 301-307. doi: 10.1215/03335372-11092938.
  21. GeoNames. (n.d.). Retrieved from https://www.geonames.org/.
  22. Gîrbacia, F. (2024). An analysis of research trends for using artificial intelligence in cultural heritage. Electronics, 13(18), article number 3738. doi: 10.3390/electronics13183738.
  23. Haase, J., & Pokutta, S. (2024). Human-AI co-creativity: Exploring synergies across levels of creative collaboration. ArXiv Preprint. doi: 10.48550/arXiv.2411.12527.
  24. Hannaford, E.D., Schlegel, V., Lewis, R., Ramsden, S., Bunn, J., Moore, J., & Nenadic, G. (2024). Our heritage, our stories: Developing AI tools to link and support community-generated digital cultural heritage. Journal of Documentation, 80(5), 1133-1147. doi: 10.1108/JD-03-2024-0057.
  25. HathiTrust Research Center Analytics. (n.d.). Retrieved from https://analytics.hathitrust.org/.
  26. HathiTrust. (n.d.). Retrieved from https://www.hathitrust.org/.
  27. HIPE – Identifying Historical People, Places and other Entities. (n.d.). Retrieved from https://hipe-eval.github.io/HIPE-2022/.
  28. Ilsemann, H. (2023). R Stylo and the authorship determination of Henry V. Digital Scholarship in the Humanities, 38(4), 1577-1581. doi: 10.1093/llc/fqad067.
  29. International Committee of Medical Journal Editors (ICMJE). (n.d.). Retrieved from https://www.icmje.org/.
  30. Jones, M.L. (2023). AI in history. The American Historical Review, 128(3), 1360-1367. doi: 10.1093/ahr/rhad361.
  31. Kamatović, T. (2024). Augustine and the posthuman writer: Toward an ambiguous authorship in the age of artificial intelligence. Journal of Posthuman Studies, 8(1), 6-22. doi: 10.5325/jpoststud.8.1.0006.
  32. KITAB. (n.d.). Retrieved from https://kitab-project.org/.
  33. Lavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., & Karacapilidis, N. (2024). Determinants of humanities and social sciences students’ intentions to use artificial intelligence applications for academic purposes. Information, 15(6), article number 314. doi: 10.3390/info15060314.
  34. Liimatta, A. (2024). OCR quality and the resilience of algorithmic identification of linguistic register features in eighteenth century collections online. Journal of Data Mining & Digital Humanities.
  35. Linares, L. (2025). Bridging disjunctures: Appadurai’s cultural anthropology of globalisation and literary translation studies. Translation in Society, 4(1), 97-113. doi: 10.1075/tris.24016.lin.
  36. Liu, Z. (2025). Human-AI co-creation: A framework for collaborative design in intelligent systems. ArXiv Preprint. doi: 10.48550/arXiv.2507.17774.
  37. Longstreet, J. (2024). From manassas to appomattox: Memoirs of the civil war in America. Bloomington: Indiana University Press.
  38. Marchine, A. (2023). Death of an author. New York: Pushkin Industries.
  39. Medeiros, K., Cropley, D.H., Marrone, R.L., & Reiter‐Palmon, R. (2025). Human‐AI co‐creativity: Does ChatGPT make us more creative? The Journal of Creative Behavior, 59(2), article number e70022. doi: 10.1002/jocb.70022.
  40. Merriam, T. (2021). Review of Stylo(). Other, by M. Eder, J. Rybicki, & M. Kestemont. Renaissance and Reformation, 44(4), 223-228.
  41. Miao, X., Zhu, S., Fu, F., Guo, Z., Yang, Z., Tu, Y., & Cui, B. (2024). X-former elucidator: Reviving efficient attention for long context language modeling. In Proceedings of the thirty-third international joint conference on artificial intelligence (pp. 8179-8187). Bremen: International Joint Conferences on Artificial Intelligence.
  42. Mining the Dispatch. (n.d.). Retrieved from https://dsl.richmond.edu/dispatch/.
  43. Mohammed, A.H., & Ali, A.H. (2021). Survey of bert (bidirectional encoder representation transformer) types. Journal of Physics: Conference Series, 1963(1), article number 012173.
  44. Open Islamicate Texts Initiative. (n.d.). Retrieved from https://openiti.org/.
  45. Oztabak, C. (2025). AI-generated folklore for culturally resonant storytelling in the digital age: Bridging tradition and technology in storytelling. In E. Saka (Ed.), Understanding generative AI in a cultural context: Artificial myths and human realities (pp. 161-184). New York: IGI Global Scientific Publishing.
  46. Pelagios Network. (n.d.). Retrieved from https://pelagios.org/.
  47. Porter, B., & Machery, E. (2024). AI-generated poetry is indistinguishable from human-written poetry and is rated more favorably. Scientific Reports, 14, article number 26133. doi: 10.1038/s41598-024-76900-1.
  48. Project Update – Bentham vs the computer. (2018). Retrieved from https://blogs.ucl.ac.uk/transcribe-bentham/2018/02/23/project-update-bentham-vs-computer/.
  49. Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. (2025). Retrieved from https://www.icmje.org/icmje-recommendations.pdf.
  50. Report of the United States Copyright Office “Copyright and Artificial Intelligence: Part 2: Copyrightability”. (2025, January). Retrieved from https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf.
  51. ReRites. (n.d.). Retrieved from https://www.glia.ca/rerites/.
  52. Riemenschneider, F., & Frank, A. (2023). Graecia capta ferum victorem cepit. Detecting latin allusions to ancient greek literature. ArXiv Preprint. doi: 10.48550/arXiv.2308.12008.
  53. Romanello, M., & Hengchen, S. (2021). Detecting text reuse with passim. Programming Historian. doi: 10.46430/phen0092.
  54. Shahid, A., Ogunola, A.A., Ishaq, S.M., Khawaji, T., Mowafaq, F., & Arafat, Y. (2024). Revolutionizing education: The transformative power of artificial intelligence in shaping a brighter future for humanities. Library of Progress-Library Science, Information Technology & Computer, 44(3), 21898-21912.
  55. Statement of Policy of the United States Copyright Office “Copyright Registration Guidance: Works Containing Material Generated by Artificial Intelligence”. (2023, March). Retrieved from https://www.copyright.gov/ai/ai_policy_guidance.pdf.
  56. Tesserae. (n.d.). Retrieved from https://www.tesserae.co/.
  57. The Viral Texts Project. (n.d.). Retrieved from https://viraltexts.org/.
  58. Transkribus. (n.d.). Retrieved from https://www.transkribus.org/.
  59. Wan, Q., Hu, S., Zhang, Y., Wang, P., Wen, B., & Lu, Z. (2024). “It felt like having a second mind”: Investigating human-AI co-creativity in prewriting with large language models. Proceedings of the ACM on Human-Computer Interaction, 8, 1-26. doi: 10.1145/3637361.
  60. Wei, B. (2024). Advancements and challenges in AI-driven creative translation: A comprehensive analysis. Applied and Computational Engineering, 82, 82-87.
  61. Westenberger, P., & Farmaki, D. (2025). Artificial intelligence for cultural heritage research: The challenges in UK copyright law and policy. European Journal of Cultural Management and Policy, 15, article number 14009. doi: 10.3389/ejcmp.2025.14009.
  62. Yang, J., Zhang, X., Liang, K., & Liu, Y. (2023). Exploring the application of large language models in detecting and protecting personally identifiable information in archival data: A comprehensive study. In 2023 IEEE International Conference on Big Data (BigData) (pp. 2116-2123). Sorrento: IEEE. doi: 10.1109/BigData59044.2023.10386949.
  63. Zhao, H. (2023). Challenges or opportunities: When artificial intelligence is applied to digital humanities. Journal for ABM Special Issue, 8(1), 57-65. doi: 10.33063/tabm.v8i1.548.
Suggested citation
Chelebiyev, N. (2025). Artificial Intelligence in the humanities: Between innovation and challenges. Journal of Digital Humanities and Cultural Innovation, 1(1), 4-15. https://doi.org/10.67524/verus5/1.2025.04