“Questomatic”: Automated Question Formulation System

The importance of automatically creating questions from text is growing, especially in a variety of industries.However, current AQG systems struggle with scalability, contextual accu-racy, and question diversity also existing systems often focus on objective questions, with less attention given to generating subjec-tive or open-ended questions.This paper introduces Questomatic, a novel system that leverages the BERT model, advanced Natural Language Processing (NLP) tools, and a custom randomization algorithm to generate unique, contextually accurate, and diverse questions from input text and this study introduces a stan-dardized method for automated question development based on phrase structure and meaning analysis. Tokenization, stemming, lemmatization, and punctuation are among the activities that are carried out using several NLP libraries, including NLTK and Spacy. Strong NLP tool BERT has also been used in machine translation, named entity identification, sentiment analysis, and other fields. NLP has benefited greatly from BERT's sophisticated capabilities, opening the door for more developments. Natural language processing relies heavily on part-of-speech tagging, which finds the right tags for text. This tagging is used by NLP libraries to efficiently process paragraphs of text. This method can be used to create test questions for educational establish-ments such as universities, coaching facilities, and schools. This technique can also be used by students for self-evaluation, and colleges can utilize it to generate questions for their students.