Machine Learning Applications for Comparative Philology in the Modern Graduate Curriculum

Authors

  • Mavlyuda Gadoeva
  • Gulbanbegim Jamolova
  • Anora Jabbarova
  • Yulduz Umbarova
  • Shavkat Rustamov
  • Nodir Yadgarov
  • Shamsu Qamar Otamurodova

DOI:

https://doi.org/10.51983/ijiss-2026.16.2.46

Keywords:

Machine Learning, Comparative Philology, Digital Humanities, Information Systems, Graduate Education, Text Analysis, Curriculum Integration

Abstract

The use of machine learning (ML) in the humanities has given a chance of enhancing the research and learning process, particularly in comparative philology. This paper looks into the implementation, effects, and issues that surround the use of ML-enabled information tools in the modern graduate curriculum. The study targets the major tools, including digital corpora, text analysis systems, AI chatbots, and automated translation platforms, that aid the linguistic analysis, semantic exploration, and content generation. Data on the use of tools, perceived benefits, and barriers to adoption were assessed in a systematic analytical method using postgraduate students and faculty members with philology and linguistics programs. The results display a strong rate of use of the ML tools, with the most commonly used being the digital corpora (81.2) and text analysis tools (78.5). The findings also show a great positive change in the academic performance, where the efficiency of research and learning improvement has been found to be the most eminent advantage. Nonetheless, other obstacles that have slowed down successful implementation include poor technical skills (62.4%) and insufficient training (58.7%). To demonstrate the effect of the introduction of the ML, a comparison between traditional and ML-based methods was performed, which revealed an overall improvement of all the assessed parameters. These results underscore the potential transformative nature of ML tools in enhancing research productivity and learning. According to the findings, the research suggests a theoretical framework of the incorporation of ML-based tools into comparative philology based on a curriculum feedback strategy, which allows further academic enhancement. The research adds to the emerging body of digital humanities by offering empirical data and practical suggestions on how to integrate ML technologies in the process of teaching and research in philological education.

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Published

05-06-2026

How to Cite

Gadoeva, M., Jamolova, G., Jabbarova, A., Umbarova, Y., Rustamov, S., Yadgarov, N., & Otamurodova, S. Q. (2026). Machine Learning Applications for Comparative Philology in the Modern Graduate Curriculum. Indian Journal of Information Sources and Services, 16(2), 455–461. https://doi.org/10.51983/ijiss-2026.16.2.46