Sirithumgul, PornpatPrasertsilp, PimpakaOlfman, Lorne2021-12-242021-12-242022-01-04978-0-9981331-5-7http://hdl.handle.net/10125/80243This 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.10 pagesengAttribution-NonCommercial-NoDerivatives 4.0 InternationalComputational Intelligence and State-of-the-Art Data Analyticsartificial intelligence (ai) for knowledge processingexpert systemnatural language processing (nlp)ontology-based designtext miningtext10.24251/HICSS.2022.901