SCIENCEBRING   /   HAMBURG, GERMANY
Login Register
International Journal of Advance Scientific Research SCIENTIFIC IDEAS. INTERNATIONAL PERSPECTIVES.
ISSN 2750-1396 12 issues per year · Monthly
Crossref DOI: 10.37547/ijasr
Submit Manuscript ↗

Search the Journal
Articles Published:

Artificial Intelligence and the Future of Language, Literature, and Cultural Heritage

Vol. 6 No. 11 (2026): Volume 06 Issue 11 Pages 81-106
Authors
  • Dr. Sofia Conti Conti
    Department of History and Cultural Studies, Italian Institute of Humanities Research, Rome, Italy
DOI 10.37547/ ↗
RESEARCH OVERVIEW

Abstract

Artificial Intelligence (AI) is transforming the study, preservation, interpretation, and dissemination of language, literature, and cultural heritage. Advances in natural language processing (NLP), machine learning, deep learning, computer vision, knowledge representation, and cloud computing have created new opportunities to analyze historical texts, translate multilingual content, recognize named entities, interpret literary patterns, and digitally preserve tangible and intangible heritage. These technologies can improve access to manuscripts, archival collections, historical monuments, oral traditions, and culturally significant artifacts while supporting interdisciplinary research across the humanities and computer science. Research on natural language understanding, machine translation, and question-answering systems provides a foundation for intelligent language applications (Feng, 2004; Feng, 2008; Guo et al., 2019). Similarly, deep-learning-based heritage classification, image captioning, multimodal documentation, and digital preservation demonstrate how computational methods can support cultural documentation and public engagement (Belhi et al., 2017; Belhi et al., 2019; Alam et al., 2022). However, the integration of AI into the humanities also introduces challenges involving linguistic diversity, cultural bias, historical accuracy, data ownership, interpretive transparency, and the preservation of contextual meaning. This paper reviews the principal AI techniques used in language and cultural heritage research, examines their applications and limitations, and proposes an integrated framework for responsible digital humanities. It argues that AI should complement rather than replace human interpretation, cultural expertise, and ethical judgment. Sustainable progress requires reliable digital collections, representative multilingual datasets, transparent models, community participation, and interdisciplinary collaboration. By combining computational efficiency with humanistic inquiry, AI can contribute to a more accessible, inclusive, and critically informed understanding of linguistic and cultural knowledge.

Keywords

  • Artificial Intelligence
  • Natural Language Processing
  • Digital Humanities
  • Machine Translation
  • Deep Learning
  • Literary Analysis
  • Cultural Heritage Preservation
  • Knowledge Graphs
  • Multimodal Learning
  • Ethical AI
SOURCES & CITATIONS

References

1. S. Alam, K. Islam, N. Sharmila, Z. R. Sovon, and R. M. Rahman, Image Captioning-Bangladesh’s Heritage Perspective Using Deep Learning. In 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–8, IEEE, June, 2022.
2. Bao Bide, Using Technology and Algorithms to Explore Issues in the Humanities, Chinese Journal of Social Sciences , 2020 ( 006 ).
3. A. Belhi, A. Bouras, and S. Foufou, Digitization and preservation of cultural heritage: The CEPROQHA approach, in Software, Knowledge, Information Management and Applications (SKIMA), 2017 11th International Conference on, 2017, pp. 1–7 : IEEE.
4. A. Belhi, H. Gasmi, A. K. Al-Ali, A. Bouras, S. Foufou, X. Yu, and H. Zhang, Deep learning and cultural heritage: the CEPROQHA project case study. In 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 1–5, IEEE, August, 2019.
5. Cai Yanjing, Cheng Xianyi, Pan Yan, A Framework of Artificial Intelligence Oriented Natural Language Processing. Microelectronics & Computer , 2011, 28 ( 10 ).
6. M. Casillo, F. Colace, B. B. Gupta, A. Lorusso, F. Marongiu, and D. Santaniello, A Deep Learning Approach to Protecting Cultural Heritage Buildings Through IoT-Based Systems. In 2022 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 252–256, IEEE, June, 2022.
7. Chomsky N, Aspects of the Theory of Syntax . Cambridge, Massachusetts : The MIT Press, 1965.
8. Fillmore C J, The Case for Case, In: Bach E, Harms Reds, Universals in Linguistic Theory , New York : Holt, Rinehart and Winstion, 1968, pp. 1–88.
9. Feng Zhiwei, Academic Position of Natural Language Processing, Journal of PLA University of Foreign Languages , 2005,( 03 ): 1–8.
10. Feng Zhiwei, Machine Translation Research , Beijing : China International Translation and Publishing Company, 2004.
11. Feng Zhiwei, The Past and Present of Natural Language Processing, Foreign Languages in China , 2008 ( 01 ), pp. 14–22.
12. G. Gennarelli, G. Ludeno, and F. Soldovieri, Applying Ground-Penetrating Radar and Microwave Tomography Data Processing in Cultural Heritage. IEEE Signal Process, 36, 53–61, 2019.
13. Guo Tianyi, Peng Min and Yi Mulan, Advances in Question Answering in Natural Language Processing, Journal of Wuhan University(Natural Science Edition) , 2019, 65 ( 05 ), pp. 417–426.
14. M. Hess, V. Petrovic, D. Meyer, D. Rissolo, and F. Kuester, Fusion of multimodal three-dimensional data for comprehensive digital documentation of cultural heritage sites. In 2015 Digital Heritage, Vol. 2, pp. 595–602, IEEE, September, 2015.
15. R. A. Kambau, Z. A. Hasibuan, and M. O. Pratama, Classification for multiformat object of cultural heritage using deep learning. In 2018 Third International Conference on Informatics and Computing (ICIC), pp. 1–7, IEEE, October, 2018.
16. K. Madani, A. M. Rinaldi, and C. Russo, Combining linked open data and multimedia knowledge base for digital cultural heritage robotic applications. In 2021 IEEE International Symposium on Multimedia (ISM), pp. 196–203, IEEE, November, 2021.
17. Luo Yan, Study on Natural Language Understanding of the Chinese Information Retrieval System, Library and Information Service , 2005,( 04 ), pp. 52–55.
18. S. F. Rashid, and R. P. Qasha, Cloud-Based Big Data Approach for Managing Multi-Types Data of Cultural Heritage. In 2022 International Conference on Communications, Information, Electronic and Energy Systems (CIEES), pp. 1–6, IEEE, November, 2022.
19. Sun Zhen, Wang Huilin, Overview on the Advance of the Research on Named Entity Recognition, Data Analysis and Knowledge Discovery . 2010,( 06 ), pp. 42–47.
20. Wang He, In Search of “Digital Lu Xun”: Texts, Machines and Robots: Rethinking the “Digitalization” of Modern Literature and Related Issues, Literary contention , 2021 ( 11 ), pp. 77–94.
21. M. Yilmazer, M. M. Karakose, Railway Condition Monitoring and Fault Detection Based on YOLOv4. In 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 439–443, IEEE, November, 2022.
22. Y. Xie, Internet Big Data Mining and Research on Intangible Cultural Heritage Activation with Smart Devices. In 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pp. 724–727, IEEE, November, 2021.
23. Yu Shiwen, Zhu Xuefeng, Geng Libo, Natural Language Processing Technology and Language Depth Computing, Social Sciences in China , 2015 ( 03 ), pp. 127–135.
24. Zheng Yongxiao, Accelerating the transformation from “digitalization” to “dataization”—The theory of “big data”, “cloud computing” and classical literature research, Literary Heritage , 2014 ( 06 ), pp. 141–148.
DISCOVER RELATED RESEARCH

Similar Articles

Continue exploring the collection ↗
1-10 of 741