AI-Enabled Quality Assurance for Multiple-Choice Assessment Items

Excerpt

arXiv:2610.04267v1 Announce Type: cross Abstract: Generating multiple-choice questions is increasingly scalable, but establishing their assessment quality remains difficult. We present a focused narrative review of automated item-writing flaw detection, revision, psychometric screening, and NLP benchmark auditing. Database searches, citation retrieval, and nominated sources yield fourteen research reports reviewed in full text. We distinguish surface checks from content-sensitive judgments and m