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 Ramar, Gobinath; Sivasakthivel, Ramkumar; Stephen, R.; Balan, R.V. Siva; Sahu, Mayank Title A name given to the resource Modern Approaches for Automatic Question Paper Generator Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Computing Technologies and Data Communication, ICCTDC 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCTDC64446.2025.11158798" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCTDC64446.2025.11158798</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019050810?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019050810?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 Ramar G., Christ University, Karnataka, Bangalore, India; Sivasakthivel R., Christ University, Karnataka, Bangalore, India; Stephen R., Christ University, Karnataka, Bangalore, India; Balan R.V.S., Christ University, Karnataka, Bangalore, India; Sahu M., Christ University, Karnataka, Bangalore, India Description An account of the resource The Automatic question paper generation system in the educational field can be useful in improving the quality of question paper, distribution and evaluation process. The system can be used for maintaining the quality of the questions with higher accuracy and less error rate compared with other existing systems at a lower cost. This article gives a comprehensive literature survey of modern approaches followed in automatic question generation (AQPG) systems and categorizing the approaches used for the question paper generation process. The techniques such as rule-based, encoder-decoder based, generative adversarial network (GAN)-based, reinforcement learning-based, and transformer-based approaches are discussed in the paper and evaluated using standard metrics. The article presents insights into the strengths and limitations of each approach through the systematic comparison and analysis of multiple studies using BLEU-4, ROUGE-L, and METEOR metrics on the SQuAD dataset. The research finding of the article gives a better opportunity to the researcher and educators to improve the knowledge about automated question paper generator systems as well as the challenges incorporated during the implementation process of question paper generation. This article also gives a depiction of AI enabled solution in automated question paper generator. 2025 IEEE. Subject The topic of the resource BLEU-4; Generative Adversarial Network; METEOR; Reinforcement Learning; ROUGE-L; SQuAD Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152798-3; 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