Linguistic Capabilities Of Artificial Intelligence In History Education
DOI:
https://doi.org/10.55640/eijp-06-02-09Keywords:
Artificial intelligence, history education, natural language processingAbstract
The integration of artificial intelligence (AI) in history education represents a paradigm shift in how historical knowledge is accessed, analyzed, and disseminated. This article examines the linguistic capabilities of AI technologies and their application in teaching history, focusing on automated textual analysis, translation, and semantic processing of historical documents. As educational institutions worldwide seek innovative approaches to enhance student engagement and academic literacy, AI-powered linguistic tools offer unprecedented opportunities for deepening historical comprehension, ensuring terminological precision, and developing scholarly discourse skills. This study explores how natural language processing (NLP), machine translation, and semantic analysis can transform history pedagogy, making primary sources more accessible while fostering critical thinking and analytical competencies essential for historical scholarship.
References
Guldi, J., & Armitage, D. (2014). The History Manifesto. Cambridge University Press.
Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266.
Wineburg, S. (2001). Historical Thinking and Other Unnatural Acts: Charting the Future of Teaching the Past. Temple University Press.
Jurafsky, D., & Martin, J. H. (2020). Speech and Language Processing (3rd ed. draft). https://web.stanford.edu/~jurafsky/slp3/
Moretti, F. (2013). Distant Reading. Verso Books.
Ehrmann, M., Romanello, M., Flückiger, A., & Clematide, S. (2016). Extended overview of CLEF HIPE 2020: Named entity processing on historical newspapers. Experimental IR Meets Multilinguality, 12260.
Blevins, C. (2010). Topic modeling Martha Ballard's diary. Historying, 1. http://www.cameronblevins.org/posts/topic-modeling-martha-ballards-diary/
Kim, E., & Klinger, R. (2019). A survey on sentiment and emotion analysis for computational literary studies. Zeitschrift für digitale Geisteswissenschaften, 4.
Cotos, E. (2014). Enhancing writing pedagogy with learner corpus data. ReCALL, 26(2), 202-224.
McNamara, D. S., Graesser, A. C., McCarthy, P. M., & Cai, Z. (2014). Automated Evaluation of Text and Discourse with Coh-Metrix. Cambridge University Press.
Stahlberg, F. (2020). Neural machine translation: A review. Journal of Artificial Intelligence Research, 69, 343-418.
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, 5998-6008.
Chu, C., & Wang, R. (2018). A survey of domain adaptation for neural machine translation. Proceedings of the 27th International Conference on Computational Linguistics, 1304-1319.
Cronin, M. (2013). Translation in the Digital Age. Routledge.
Bowker, L., & Buitrago Ciro, J. (2019). Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community. Emerald Publishing.
Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2016). Diachronic word embeddings reveal statistical laws of semantic change. Proceedings of the 54th Annual Meeting of the ACL, 1489-1501.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26, 3111-3119.
Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G. D., Gutierrez, C., ... & Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), 1-37.
Rouet, J. F., Favart, M., Britt, M. A., & Perfetti, C. A. (1997). Studying and using multiple documents in history: Effects of discipline expertise. Cognition and Instruction, 15(1), 85-106.
Grimes, D., & Warschauer, M. (2010). Utility in a fallible tool: A multi-site case study of automated writing evaluation. The Journal of Technology, Learning and Assessment, 8(6), 4-43.
Warschauer, M., & Matuchniak, T. (2010). New technology and digital worlds: Analyzing evidence of equity in access, use, and outcomes. Review of Research in Education, 34(1), 179-225.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Kurbanov Azizbek
This work is licensed under a Creative Commons Attribution 4.0 International License.
Individual articles are published Open Access under the Creative Commons Licence: CC-BY 4.0.
