Automatic question generation for intelligent tutoring systems

In this paper, we present the system of automatic MCQs (Multiple Choice Questions) generation for any given input text along with a set of distractors. The system is trained on a Wikipedia-based dataset consisting of URLs of Wikipedia articles. The important words (keywords) which consist of both bigrams and unigrams are extracted and stored in a dictionary along with many other components of the knowledge base. We have used Inverse Document Frequency (IDF) measure for ranking the extracted keywords and Context-Based Similarity approach using Paradigmatic Relation discovery techniques for generation of distractors. In addition, the question generation phase includes eliminating sentences starting with Discourse Connectives to avoid a question with incomplete information. We have obtained significant accuracy compared to many similar approaches. The results are quite promising considering that there is no human intervention. The task of automatic question generation can be of quite an importance in MOOCs (Massive Open Online Courses), Intelligent Tutoring Systems and for self-assessment while learning a new concept. Though we have developed our system for the field of physics, it can be extended to any field.