AI-assisted Scoring for Oral Exams

According to PLOS ONE (Torab-Miandoab et al., 2026), a systematic review of 102 studies found structured oral exams improve reliability and fairness compared with traditional viva formats.

• From a pool of 25, 594 records screened through June 23, 2025, the review ultimately included 102 studies, according to PLOS ONE (Torab-Miandoab et al., 2026).
• According to PLOS ONE (Torab-Miandoab et al., 2026), 56.9% of included studies used structured oral examinations (SOEs), and examiner variability was reported in 66.7% of studies.
• According to PLOS ONE (Torab-Miandoab et al., 2026), technology appears in 39.2% of cases and the authors state, "Standardized oral examinations, supported by technology, offer fair and reliable assessments."
• According to PLOS ONE (Torab-Miandoab et al., 2026), reported reliability metrics included a median Cronbach's alpha of 0.75 and inter-rater ICCs ranging from 0.47 to 0.82.

According to PLOS ONE (Torab-Miandoab et al., 2026), the authors conducted a PRISMA-based systematic review across 14 databases up to June 23, 2025, screening 25, 594 records to include 102 eligible studies.

According to PLOS ONE (Torab-Miandoab et al., 2026), the review coded challenges, needs, and solutions using thematic analysis and reported quantitative outcomes such as Cronbach's alpha (median 0.75) and ICC ranges (0.47–0.82).

According to PLOS ONE (Torab-Miandoab et al., 2026), common challenges were examiner-related variability (66.7%), student anxiety (41.2%), and logistical constraints (34.3%), and common solutions were examiner training (60.8%), standardization (56.9%), and technology use (27.5%).

For medical educators, the evidence in PLOS ONE (Torab-Miandoab et al., 2026) means prioritize structure, calibration, and selective technology to improve fairness and reliability.

According to PLOS ONE (Torab-Miandoab et al., 2026), programs that implemented examiner training and scoring rubrics saw inter-rater reliability improve (ICCs moving toward 0.82), so prioritize faculty calibration workshops and multi-examiner designs.

For assessment researchers, PLOS ONE (Torab-Miandoab et al., 2026) highlights research gaps: only 39.2% of studies used technology and 91.3% noted limited generalizability, so pursue multicenter validation and longitudinal studies when testing AI scoring.

According to PLOS ONE (Torab-Miandoab et al., 2026), structured oral exams combined with technology and examiner calibration produce more reliable, fairer outcomes than unstructured viva formats.

For qualitative researchers and assessment teams, the practical next steps are to record a representative pilot, apply structured rubrics, and use automated transcription plus thematic analysis to identify rater patterns and content validity threats.

If you want to pilot AI-assisted qualitative analysis for oral exams, consider tools that integrate transcription, coding, and cross-segment analysis; learn more on the Evidano features page.

To try an integrated pipeline for transcription, qualitative coding, and AI-assisted review, Try Evidano for free.