Modern Approaches for Automatic Question Paper Generator

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.