NLP QUESTION-GENERATION THAT MAXIMIZES STUDENT LEARNING & ENGAGEMENT

Natural Language Processing (NLP) has become a key enabling technology in the development of intelligent educational systems by allowing machines to understand, interpret, and generate human language.Automated assessment generation is one of the most impactful applications of NLP, as it directly supports active learning, continuous evaluation, and personalized education.This paper presents an NLP-based Question Generation (QG) system that automatically generates meaningful, relevant, and grammatically correct questions from academic text.The proposed approach leverages transformer-based deep learning models to analyze contextual and semantic relationships within learning material and generate questions that promote comprehension, revision, and critical thinking.In addition to question generation, the system incorporates semantic similarity-based answer evaluation, enabling accurate assessment of descriptive responses without relying on exact keyword matching.Experimental analysis and qualitative observations indicate that the proposed system improves student engagement, reduces educator workload, and supports scalable assessment in digital learning environments.The study highlights the growing importance of NLP-driven automation in modern education and its potential to transform traditional assessment practices.