Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Antony, Joyal; Bobby, George; Joshi, Jowin; Jayapandian, N. Title A name given to the resource Artificial Intelligence Based Automatic Question Paper Generation Using Natural Language Processing Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Communications in Computer and Information Science;Volume;2621 CCIS;pp.343-355 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-00793-3_27" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-00793-3_27</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015690703?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015690703?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Artificial Intelligence; Latent Semantic Analysis; Natural Language Processing; Recurrent Neural Networks; Seq2Seq models Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303200792-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online