Current Volume 9
Abstract
Automatic Question Paper Generation is a crucial problem in current educational technology. The manual process of generating test papers is cumbersome, prone to bias in the distribution of difficulty, and rarely achieves consistency year after year. This article proposes a complete reviews existing works on automated question paper generation through AI and presents a new combined system that uses NLP, deep learning, and difficulty classification based on Bloom’s Taxonomy to develop balanced curriculum-compliant question papers using past exams. Five recent publications have been examined regarding rule-based systems, information retrieval, sequence-to-sequence deep learning, transformers, and LLMs. From the above literature review, we suggest a two-step process consisting of a (1) question bank generator using BERT to extract and de-duplicate questions and assign tags, and (2) paper assembler using GPT to assemble questions based on difficulty and topics. Experiments demonstrate our proposed system can achieve 91% question accuracy and takes less than 2 minutes compared to 1 hour previously, which is a 9-30% improvement.
Keywords
Auto QG, NLP, BERT, GPT, Bloom’s Taxonomy, EdTech, deep learning, difficult level classification, dataset for exams, machine learning.
Citations
IRE Journals:
Praveen H S, Praveen Khot, Omkar Shinde "AI-Driven Automated Question Paper Generator Using Past Exam Datasets" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 3032-3041 https://doi.org/10.64388/IREV9I10-1716734
IEEE:
Praveen H S, Praveen Khot, Omkar Shinde
"AI-Driven Automated Question Paper Generator Using Past Exam Datasets" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716734
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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