Generating High-Quality Multiple-Choice Questions Using Small Language Models and Adaptive Agentic Infrastructure

Introduction: We introduce a novel approach for generating high-quality multiple-choice questions (MCQs) within KiMED, an AI-based learning platform designed to support medical students in reviewing lecture material and preparing for coursework. KiMED is a personalized learning web application that [for full text, please go to the a.m. URL]

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Publication Details

Journal
German Medical Science (German Research Foundation)
Published
2025-11-03
DOI
https://doi.org/10.3205/25gmds020
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Generating High-Quality Multiple-Choice Questions Using Small Language Models and Adaptive Agentic Infrastructure

German Medical Science (German Research Foundation)
Intelligent Tutoring Systems and Adaptive Learning
article

Generating High-Quality Multiple-Choice Questions Using Small Language Models and Adaptive Agentic Infrastructure

article en

Abstract

Introduction: We introduce a novel approach for generating high-quality multiple-choice questions (MCQs) within KiMED, an AI-based learning platform designed to support medical students in reviewing lecture material and preparing for coursework. KiMED is a personalized learning web application that [for full text, please go to the a.m. URL]

German Medical Science (German Research Foundation)
Universität Hamburg (DE), University Medical Center Hamburg-Eppendorf (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 23%
Intelligent Tutoring Systems and Adaptive Learning
DOIOpen Access PDFOpenAlex
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Generating High-Quality Multiple-Choice Questions Using Small Language Models and Adaptive Agentic Infrastructure · German Medical Science (German Research Foundation) (2025) | TGRS Research Map | TGRS