ItemConference Paper

Artificial Intelligence Based Automatic Question Paper Generation Using Natural Language Processing

Title
Artificial Intelligence Based Automatic Question Paper Generation Using Natural Language Processing
Creator
Antony, Joyal; Bobby, George; Joshi, Jowin; Jayapandian, N.
Description
Question paper generation is a crucial task in education, where the objective is to design an assessment that effectively evaluates students knowledge and understanding of various subjects. Traditional methods of question paper generation can be exceedingly difficult, time-consuming, and inappropriate and may not be fully optimized. They ensure a comprehensive assessment of students knowledge. The system also offers the flexibility to customize question papers based on specific preferences and requirements. This research introduces a comparative approach to question paper generation using Latent Semantic Analysis (LSA), Word Embedding, and Sequence-to-Sequence (Seq2Seq) models, leveraging the power of Artificial Intelligence (AI) and Natural Language Processing (NLP). This model compares their Semantic Representation Quality, Context Understanding, and Computational Complexity. Comparing these techniques shows that LSA offers simplicity but may lack precision, while word embedding balances complexity and semantic understanding. Seq2Seq models, despite their complexity, provide contextually rich mappings with the highest degree of fine-tuning potential. This comparative analysis underscores the importance of understanding the nuances and trade-offs of each approach, enabling educators to make informed decisions in adopting these technologies to enhance educational practices and student learning experiences. A few modules are included in this system, including the admin module, add user, subject selection, question entry, question management, paper management and difficulty level. By capitalizing on the capabilities of LSA, Seq2Seq models, and word embedding, educators can revolutionize the process of question paper generation, ultimately leading to more effective and impactful student learning outcomes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Source
Communications in Computer and Information Science;Volume;2621 CCIS;pp.343-355
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Artificial Intelligence; Latent Semantic Analysis; Natural Language Processing; Recurrent Neural Networks; Seq2Seq models
Coverage
Antony J., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Bobby G., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Joshi J., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 18650929; ISBN: 978-303200792-6;
Format
online
Language
English
Type
Conference paper

Citation

Antony, Joyal; Bobby, George; Joshi, Jowin; Jayapandian, N., “Artificial Intelligence Based Automatic Question Paper Generation Using Natural Language Processing,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25337.

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