Natural Language Processing in Education: A Review of Applications, Challenges, and Future Directions - Sagar Kumar; Manpreet Kaur; Kulwinder Kaur (International Journal of Computer Science Languages, 2025)
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; artificial intelligence; content development; educational platforms; semantic analysis