Subjective Question Generation and Answer Evaluation using NLP

TL;DR

This paper explores NLP techniques for automated subjective question generation and answer evaluation to assist educational assessment and enhance student learning experiences.

Contribution

It introduces novel NLP methods for generating subjective questions and evaluating answers, addressing a gap in automated educational assessment tools.

Findings

Proposes new NLP models for subjective question generation

Develops automated answer evaluation techniques

Demonstrates improved assessment accuracy

Abstract

Natural Language Processing (NLP) is one of the most revolutionary technologies today. It uses artificial intelligence to understand human text and spoken words. It is used for text summarization, grammar checking, sentiment analysis, and advanced chatbots and has many more potential use cases. Furthermore, it has also made its mark on the education sector. Much research and advancements have already been conducted on objective question generation; however, automated subjective question generation and answer evaluation are still in progress. An automated system to generate subjective questions and evaluate the answers can help teachers assess student work and enhance the student's learning experience by allowing them to self-assess their understanding after reading an article or a chapter of a book. This research aims to improve current NLP models or make a novel one for automated subjective question generation and answer evaluation from text input.