Computational Intelligence Framework for Automatic Quiz Question Generation

Computational intelligence techniques are attracting more and more attention in NLP and text analysis applications. This paper is devoted to their use in automatic question generation based on text analysis with the goal to develop the computational intelligence framework that should automate or semi-automate the process of quiz and exam question generation. The framework operation is based on information retrieval and NLP algorithms. It incorporates the application of production rules, LSTM neural network models, and other intelligent techniques. It allows generating multiple choice questions, true and false questions as well as "Wh"-type (What? When? How?) questions. Automation procedures for each type question generation are developed, presented and analyzed. The typical challenges in framework development and application are considered and possible solutions are discussed. The results of the framework application and its use for quiz generation in a real college class are presented and analyzed.