An Algorithm for Generating Gap-Fill Multiple Choice Questions of an Expert System

TL;DR

This paper presents an AI algorithm that automatically generates large sets of gap-fill multiple choice questions using ontology, text mining, and NLP techniques, demonstrated in the software testing domain.

Contribution

The paper introduces a novel AI-based algorithm for automatic generation of gap-fill MCQs, integrating ontology, text mining, and NLP, with a practical application in software testing.

Findings

Generated over 16,000 valid gap-fill MCQs from 103 documents

The algorithm effectively covers diverse topics in software testing

Potential for creating extensive question pools for expert systems

Abstract

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.