YMCQ: Reasoning-Enhanced MCQ Generation
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| Title: | YMCQ: Reasoning-Enhanced MCQ Generation |
|---|---|
| Language: | English |
| Authors: | Andreea Dutulescu, Stefan Ruseti, Denis Iorga, Mihai Dascalu, Danielle S. McNamara (ORCID |
| Source: | Grantee Submission. 2025. |
| Peer Reviewed: | Y |
| Page Count: | 9 |
| Publication Date: | 2025 |
| Sponsoring Agency: | National Center for Education Research (NCER) (ED/IES) |
| Contract Number: | R305T240035 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Descriptors: | Automation, Computer Assisted Testing, Multiple Choice Tests, Natural Language Processing, Test Items, Misconceptions, Efficiency, Open Source Technology, Test Validity |
| DOI: | 10.1007/978-3-031-98465-5_39 |
| Abstract: | Automated multiple-choice question (MCQ) generation is valuable for scalable assessment and enhanced learning experiences. How-ever, existing MCQ generation methods face challenges in ensuring plausible distractors and maintaining answer consistency. This paper intro-duces a method for MCQ generation that integrates reasoning-based explanations for both correct answers and distractors, leveraging open-source language models finetuned on publicly available datasets. Our approach addresses these issues with 3 major improvements. First, over 300k questions from public datasets were augmented with synthetically generated reasoning explanations. Second, we fine-tune a Large Language Model (LLM) with reasoning-based explanations to condition the generation while accounting for correct reasoning and possible misconceptions. Third, we introduce a multi-step filtering pipeline to ensure the validity of the question and the diversity of the generated distractors. This work argues for the effectiveness of reasoning-enhanced finetuning in improving MCQ generation quality while maintaining accessibility and cost efficiency. We release all resources, including synthetically augmented questions, training code, and the best model as open-source. [This paper was published in: "AIED 2025, LNAI 15882," edited by A. I. Cristea et al., Springer Nature Switzerland, 2025, pp. 308-15.] |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2025 |
| Accession Number: | ED674604 |
| Database: | ERIC |
| FullText | Text: Availability: 0 |
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| Header | DbId: eric DbLabel: ERIC An: ED674604 AccessLevel: 3 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: YMCQ: Reasoning-Enhanced MCQ Generation – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Andreea+Dutulescu%22">Andreea Dutulescu</searchLink><br /><searchLink fieldCode="AR" term="%22Stefan+Ruseti%22">Stefan Ruseti</searchLink><br /><searchLink fieldCode="AR" term="%22Denis+Iorga%22">Denis Iorga</searchLink><br /><searchLink fieldCode="AR" term="%22Mihai+Dascalu%22">Mihai Dascalu</searchLink><br /><searchLink fieldCode="AR" term="%22Danielle+S%2E+McNamara%22">Danielle S. McNamara</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5869-1420">0000-0001-5869-1420</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2025. – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 9 – Name: DatePubCY Label: Publication Date Group: Date Data: 2025 – Name: SourceSuprt Label: Sponsoring Agency Group: SrcSuprt Data: National Center for Education Research (NCER) (ED/IES) – Name: NumberContract Label: Contract Number Group: NumCntrct Data: R305T240035 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Speeches/Meeting Papers<br />Reports - Research – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Testing%22">Computer Assisted Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+Choice+Tests%22">Multiple Choice Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Items%22">Test Items</searchLink><br /><searchLink fieldCode="DE" term="%22Misconceptions%22">Misconceptions</searchLink><br /><searchLink fieldCode="DE" term="%22Efficiency%22">Efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Open+Source+Technology%22">Open Source Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Validity%22">Test Validity</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1007/978-3-031-98465-5_39 – Name: Abstract Label: Abstract Group: Ab Data: Automated multiple-choice question (MCQ) generation is valuable for scalable assessment and enhanced learning experiences. How-ever, existing MCQ generation methods face challenges in ensuring plausible distractors and maintaining answer consistency. This paper intro-duces a method for MCQ generation that integrates reasoning-based explanations for both correct answers and distractors, leveraging open-source language models finetuned on publicly available datasets. Our approach addresses these issues with 3 major improvements. First, over 300k questions from public datasets were augmented with synthetically generated reasoning explanations. Second, we fine-tune a Large Language Model (LLM) with reasoning-based explanations to condition the generation while accounting for correct reasoning and possible misconceptions. Third, we introduce a multi-step filtering pipeline to ensure the validity of the question and the diversity of the generated distractors. This work argues for the effectiveness of reasoning-enhanced finetuning in improving MCQ generation quality while maintaining accessibility and cost efficiency. We release all resources, including synthetically augmented questions, training code, and the best model as open-source. [This paper was published in: "AIED 2025, LNAI 15882," edited by A. I. Cristea et al., Springer Nature Switzerland, 2025, pp. 308-15.] – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: CodeSource Label: IES Funded Group: SrcInfo Data: Yes – Name: DateEntry Label: Entry Date Group: Date Data: 2025 – Name: AN Label: Accession Number Group: ID Data: ED674604 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED674604 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/978-3-031-98465-5_39 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 9 Subjects: – SubjectFull: Automation Type: general – SubjectFull: Computer Assisted Testing Type: general – SubjectFull: Multiple Choice Tests Type: general – SubjectFull: Natural Language Processing Type: general – SubjectFull: Test Items Type: general – SubjectFull: Misconceptions Type: general – SubjectFull: Efficiency Type: general – SubjectFull: Open Source Technology Type: general – SubjectFull: Test Validity Type: general Titles: – TitleFull: YMCQ: Reasoning-Enhanced MCQ Generation Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Andreea Dutulescu – PersonEntity: Name: NameFull: Stefan Ruseti – PersonEntity: Name: NameFull: Denis Iorga – PersonEntity: Name: NameFull: Mihai Dascalu – PersonEntity: Name: NameFull: Danielle S. McNamara IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Type: published Y: 2025 Titles: – TitleFull: Grantee Submission Type: main |
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