Feasibility Assurance: A Review of Automatic Item Generation in Medical Assessment

Autor/inn/en	Falcão, Filipe; Costa, Patrício; Pêgo, José M.
Titel	Feasibility Assurance: A Review of Automatic Item Generation in Medical Assessment
Quelle	In: Advances in Health Sciences Education, 27 (2022) 2, S.405-425 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Falcão, Filipe)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1382-4996
DOI	10.1007/s10459-022-10092-z
Schlagwörter	Multiple Choice Tests; Computer Assisted Testing; Test Items; Medical Education; Test Construction; Medical Students; Student Evaluation + Suchen Sie Ihr Suchwort? Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; Test content; Testaufgabe; Medizinische Ausbildung; Testaufbau; Schulnote; Studentische Bewertung
Abstract	Background: Current demand for multiple-choice questions (MCQs) in medical assessment is greater than the supply. Consequently, an urgency for new item development methods arises. Automatic Item Generation (AIG) promises to overcome this burden, generating calibrated items based on the work of computer algorithms. Despite the promising scenario, there is still no evidence to encourage a general application of AIG in medical assessment. It is therefore important to evaluate AIG regarding its feasibility, validity and item quality. Objective: Provide a narrative review regarding the feasibility, validity and item quality of AIG in medical assessment. Methods: Electronic databases were searched for peer-reviewed, English language articles published between 2000 and 2021 by means of the terms 'Automatic Item Generation', 'Automated Item Generation', 'AIG', 'medical assessment' and 'medical education'. Reviewers screened 119 records and 13 full texts were checked according to the inclusion criteria. A validity framework was implemented in the included studies to draw conclusions regarding the validity of AIG. Results: A total of 10 articles were included in the review. Synthesized data suggests that AIG is a valid and feasible method capable of generating high-quality items. Conclusions: AIG can solve current problems related to item development. It reveals itself as an auspicious next-generation technique for the future of medical assessment, promising several quality items both quickly and economically. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01