Automatic Question Paper Generator Using Artificial Intelligence

Automatic Question Paper Generator Using Artificial Intelligence

PRABHU D, YOGESH S, RAGUL V

Abstract: This process  involves using Natural Language Processing (NLP) techniques, specifically LSTM (Long Short-Term Memory) algorithms, to generate questions from text while ensuring semantic accuracy and syntactic coherence. Unlike tasks such as summarization and paraphrasing, generating questions requires not only understanding the text but also being able to frame questions that elicit specific information, thus necessitating answers. To overcome this challenge, the approach involves several steps. First, the text undergoes preprocessing with NLP techniques to remove irrelevant or redundant information, as well as to identify and extract keywords crucial for generating questions. Next, LSTM algorithms are employed to process the text and keywords, enabling the system to understand the context and semantic nuances present in the text. Once the text has been processed, the system begins constructing questions. Instead of directly asking questions based on the extracted keywords, the LSTM algorithm twists or rephrases the questions to ensure they are not merely repetitions of the original text. This twisting process involves rearranging sentence structures, altering word choices, or introducing subtle variations to produce questions that are natural-sounding and coherent while still effectively eliciting the desired information. By leveraging NLP techniques and LSTM algorithms in this manner, the system can generate questions that are not only semantically accurate and syntactically cohesive but also effectively engage with the content of the text, overcoming the challenge of needing answers to frame meaningful questions. This approach represents an innovative use of NLP technology to enhance question generation capabilities, enabling more robust and contextually relevant questioning from textual data.