An Algorithm for Generating Gap-Fill Multiple Choice Questions of an Expert System
This addresses the need for efficient question generation for expert systems, though it appears incremental as it applies known techniques to a specific domain.
The researchers tackled the problem of automatically generating gap-fill multiple choice questions by proposing an AI algorithm that uses ontology, text mining, and NLP, resulting in over 16,000 valid questions from 103 online documents in the software testing domain.
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.