Natural Language Processing in Education: A Review of Applications, Challenges, and Future Directions

Year : 2025 | Volume : 03 | Issue : 02 | Page : 11 18
    By

    Sagar Kumar,

  • Manpreet Kaur,

  • Kulwinder Kaur,

  1. Student, Faculty of Computing, Guru Kashi University, Talwandi Sabo Bathinda, Punjab, India
  2. Student, Faculty of Computing, Guru Kashi University, Talwandi Sabo Bathinda, Punjab, India
  3. Assistant Professor, Faculty of Computing, Guru Kashi University, Talwandi Sabo Bathinda, Punjab, India

Abstract

Natural Language Processing (NLP) has increasingly become a transformative force within the field of education, offering innovative solutions and reshaping traditional methods of teaching, learning, assessment, and educational research. This review explores the evolving landscape of NLP applications in education, shedding light on significant advancements, ongoing challenges, and emerging opportunities. The integration of NLP into intelligent tutoring systems has enabled more personalized learning experiences, while automated assessment tools have enhanced grading efficiency and objectivity. Additionally, NLP supports the development of adaptive educational content and enables deeper insights into student behavior and performance through learner analytics platforms. Despite the rapid adoption of these technologies, there are persistent concerns regarding their accuracy, algorithmic bias, data privacy, and effective integration into existing pedagogical frameworks. Addressing these concerns is essential to maximize the benefits of NLP in educational settings. This study concludes by highlighting key areas for future research, including the development of culturally sensitive NLP models and ethical guidelines for educational AI. Furthermore, it underscores the necessity of interdisciplinary collaboration among AI researchers, education specialists, and policy-makers to ensure that NLP technologies are developed and applied in ways that genuinely enhance educational outcomes and equity.

Keywords: NLP technologies, educational platforms, content development, semantic analysis, artificial intelligence

[This article belongs to International Journal of Computer Science Languages ]

How to cite this article:
Sagar Kumar, Manpreet Kaur, Kulwinder Kaur. Natural Language Processing in Education: A Review of Applications, Challenges, and Future Directions. International Journal of Computer Science Languages. 2025; 03(02):11-18.
How to cite this URL:
Sagar Kumar, Manpreet Kaur, Kulwinder Kaur. Natural Language Processing in Education: A Review of Applications, Challenges, and Future Directions. International Journal of Computer Science Languages. 2025; 03(02):11-18. Available from: https://journals.stmjournals.com/ijcsl/article=2025/view=232857


References

  1. Aletras N, Stevenson M, Clough P. Computing similarity between items in a digital library of cultural heritage. J Comput Cult Heritage. 2013 Jan 9; 5(4): 1–9.
  2. Millis KK, Long D, Magliano J, Wiemer K, editors. Deep comprehension: Multi-disciplinary approaches to understanding, enhancing, and measuring comprehension. Routledge; England, UK. 2018 Sep 25.
  3. Arfé B, Dockrell JE, De Bernardi B. The effect of language specific factors on early written composition: The role of spelling, oral language and text generation skills in a shallow orthography. Read Writ. 2016 Mar; 29(3): 501–27.
  4. Azevedo R, Taub M, Mudrick NV, Martin SA, Grafsgaard J. Using multi-channel trace data to infer and foster self-regulated learning between humans and advanced learning technologies. Handbook of self-regulation of learning and performance. New York, NY: Routledge; 2018; 2: 254–70.
  5. Bibauw S, François T, Desmet P. Discussing with a computer to practice a foreign language: Research synthesis and conceptual framework of dialogue-based CALL. Comput Assist Lang Learn. 2019 Nov 2; 32(8): 827–77.
  6. Blikstein P, Worsley M. Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. J Learn Anal. 2016 Sep 17; 3(2): 220–38.
  7. Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020; 33: 1877–901.
  8. Huang H, Li M. The impact of technology-enhanced language learning environments on second language learners’ willingness to communicate: A systematic review of empirical studies from 2012 to 2023. Lang Learn Technol. 2024; 28(1): 1–22.
  9. Conati C, Porayska-Pomsta K, Mavrikis M. AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling. arXiv preprint arXiv:1807.00154. 2018 Jun 30.
  10. Shibani A. Analytic techniques for automated analysis of writing. Digital Writing Technologies in Higher Education: Theory, Research, and Practice. Cham: Springer; 2023 Sep 15; 317–31.
  11. Crossley SA, Karumbaiah S, Ocumpaugh J, Labrum MJ, Baker RS. Predicting math identity through language and click-stream patterns in a blended learning mathematics program for elementary students. J Learn Anal. 2020; 7(1): 19–37.
  12. Devlin J, Chang MW, Lee K, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). 2019 Jun; 4171–4186.
  13. D’Mello S, Dieterle E, Duckworth A. Advanced, analytic, automated (AAA) measurement of engagement during learning. Educ Psychol. 2017 Apr 3; 52(2): 104–23.
  14. D’Mello SK, Olney AM, Blanchard N, Samei B, Sun X, Ward B, Kelly S. Multimodal capture of teacher-student interactions for automated dialogic analysis in live classrooms. In Proceedings of the 2015 ACM on international conference on multimodal interaction. 2015 Nov 9; 557–566.
  15. Dowell NM, Nixon TM, Graesser AC. Group communication analysis: A computational linguistics approach for detecting sociocognitive roles in multiparty interactions. Behav Res Methods. 2019 Jun 15; 51(3): 1007–41.
  16. Drachsler H, Greller W. Privacy and analytics: it’s a DELICATE issue a checklist for trusted learning analytics. In Proceedings of the sixth international conference on learning analytics & knowledge. 2016 Apr 25; 89–98.
  17. Fishman BJ, Penuel WR, Allen AR, Cheng BH, Sabelli NO. Design-based implementation research: An emerging model for transforming the relationship of research and practice. Teach Coll Rec. 2013 Dec; 115(14): 136–56.
  18. Godwin-Jones R. Smartphones and language learning. Lang Learn Technol. 2017; 21(2): 3–17.
  19. Graesser AC, Chipman P, Haynes BC, Olney A. AutoTutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Trans Educ. 2005 Nov 30; 48(4): 612–8.
  20. Graesser AC, D’Mello S, Hu X, Cai Z, Olney A, Morgan B. AutoTutor. In: Applied natural language processing: Identification, investigation and resolution. IGI Global Scientific Publishing; Pennsylvania, USA. 2012; 169–187.
Regular Issue Subscription Review Article
Volume 03
Issue 02
Received 28/04/2025
Accepted 09/07/2025
Published 08/09/2025
Publication Time 133 Days
170

Login


My IP

PlumX Metrics