Structured approaches to improving outcomes of oral exam in medical and paramedical education: A systematic review and evidence-based framework

Abstract

Introduction Oral examinations are central to medical and paramedical education for assessing reasoning, communication, and professional competencies, but remain challenged by issues of reliability, bias, and standardization. This systematic review synthesizes evidence on issues, requirements, and solutions to optimize oral exam outcomes in these fields. Materials and methods Following PRISMA guidelines, a comprehensive search was conducted across 14 databases (e.g., PubMed, Scopus, Web of Science) up to June 23, 2025, using PICO-framed queries focused on medical and paramedical students. Inclusion criteria encompassed English-language full text studies on oral assessments; exclusions included editorials and reviews. Three independent reviewers screened titles, abstracts, and full texts, extracting data on study characteristics, challenges, requirements, solutions, and outcomes. Quality appraisal utilized QUADAS, with thematic analysis and matrix patterns to identify correlations. Results From 25,594 records, 102 studies were included after deduplication and screening. The number of publications increased substantially after 2000, with the majority originating from the field of medicine (70.6%) and primarily addressing undergraduate (44.1%) and postgraduate (37.3%) education. Structured oral examinations (SOEs) dominated (56.9%), outperforming traditional formats in reliability (median Cronbach’s alpha 0.75; inter-rater interclass correlation coefficient (ICC) 0.47–0.82) and validity (modest correlations with objective structured clinical examinations (OSCEs), r=0.10–0.74). Key challenges included examiner variability (66.7%), student anxiety (41.2%), and logistical constraints (34.3%). The important requirements included assessment design, examiner calibration, technological integration, and equity. Solutions were emphasized on training (60.8%), standardization (56.9%), and technology (27.5%), yielding improved pass rates (50–100%) and satisfaction (72–96%). A comprehensive framework recommends multi-station objective structured viva examinations (OSVEs), hybrid delivery, combined rubrics, and artificial intelligence (AI)-assisted scoring for optimization. Limitations included generalizability (91.3%) and small samples (49.5%). Conclusions This review outlines key challenges and strategies to improve the quality of oral examinations. Standardized oral examinations, supported by technology, offer fair and reliable assessments. The findings provide a framework for educators and researchers to optimize their use, while future studies should validate predictive validity and explore AI-driven solutions to reduce bias and enhance scalability.