A Novel Framework for the Generation of Multiple Choice Question Stems Using Semantic and Machine-Learning Techniques
Open accessAbstract
Multiple Choice Questions (MCQs) are a popular assessment method because they enable automated evaluation, flexible administration and use with huge groups. Despite these benefits, the manual construction of MCQs is challenging, time-consuming and error-prone. This is because each MCQ is comprised of a question called the "stem", a correct option called the "key" along with alternative options called "distractors" whose construction demands expertise from the MCQ developers. In addition, there are different kinds of MCQs such as Wh-type, Fill-in-the-blank, Odd one out, and many more needed to assess understanding at different cognitive levels. Automatic Question Generation (AQG) for developing heterogeneous MCQ stems has generally followed two approaches: semantics-based and machine-learning-based. Questions generated via AQG techniques can be utilized only if they are grammatically correct. Semantics-based techniques have been able to generate a range of different types of grammatically correct MCQs but require the semantics to be specified. In contrast, most machine-learning approaches have been primarily able to generate only grammatically correct Fill-in-the-blank/Cloze by reusing the original text. This paper describes a technique for combining semantic-based and machine-learning-based techniques to generate grammatically correct MCQ stems of various types for a technical domain. Expert evaluation of the resultant MCQ stems demonstrated that they were promising in terms of their usefulness and grammatical correctness.
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The construction of high-quality MCQs type A has been reported to be difficult and time-consuming.
“The construction of high-quality MCQs type A has been reported to be difficult and time-consuming [4, 7–9].”
From AI in medical education: uses of AI in construction type A MCQs (2024) · cites this paper as [9]
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- Authors
- Kumar, A. P., Nayak, A., K, M. S., Chaitanya, & Ghosh, K.
- Affiliations
- Manipal Academy of Higher Education (IN)
- Corresponding author
- Nayak, Ashalatha
- Venue
- International Journal of Artificial Intelligence in Education
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 34
- Issue
- 2
- Pages
- 332-375
- ISSN
- 1560-4292 (ISSN-L)
- OpenAlex
- W4362580133
- Cited by
- 41
- References
- 53
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 10 · 2023: 5
- FWCI
- 3.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Artificial Intelligence in Education (publishedVersion, cc-by)
- Funders
- Manipal Academy of Higher Education, Manipal
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- multiple-choice question generation, automatic question generation, distractor generation, grammatical accuracy, technical domains, expert evaluation
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