This research is aimed to propose an artificial intelligence algorithm comprising an ontology-based design, text mining, and natural language processing for automatically generating gap-fill multiple choice questions (MCQs). The simulation of this research demonstrated an application of the algorithm in generating gap-fill MCQs about software testing. The simulation results revealed that by using 103 online documents as inputs, the algorithm could automatically produce more than 16 thousand valid gap-fill MCQs covering a variety of topics in the software testing domain. Finally, in the discussion section of this paper we suggest how the proposed algorithm should be applied to produce gap-fill MCQs being collected in a question pool used by a knowledge expert system.
@article{arxiv.2109.11421,
title = {An Algorithm for Generating Gap-Fill Multiple Choice Questions of an Expert System},
author = {Pornpat Sirithumgul and Pimpaka Prasertsilp and Lorne Olfman},
journal= {arXiv preprint arXiv:2109.11421},
year = {2021}
}