Revolutionizing Education With NLP and ML: Innovations, Challenges, and Future Directions

Abstract

Natural Language Processing (NLP) and Machine Learning (ML) are transforming education by making learning interactive, adaptive, and personalized. Conversational AI enables AI-driven tools to enhance student support, automate administrative tasks, and improve teaching methods. Innovations in NLP power chatbots, virtual tutors, and automated grading, making them more responsive. ML uses predictive analytics to identify at-risk students and tailor personalized learning paths, fostering inclusivity. This chapter explores NLP and ML applications in K-12 and higher education, highlighting case studies on student engagement, knowledge retention, and teaching efficacy. Key challenges include AI integration, ethical concerns like data privacy, and biases in AI models. The chapter examines policy discussions and the need for regulatory frameworks that balance innovation and ethics. As AI-driven education expands globally, collaboration among researchers, educators, and policymakers is vital to shaping its future for improved learning outcomes.