Evidence map›Paper›PMID 41874440›Full record

ArticleEmerging topics in life sciences2025

Clinical assessment meets laboratory science: adapting OSCE methodology for authentic biosciences evaluation in the age of generative AI.

Ruchira Mann, Sabrina Tosi, David Tree

Abstract read
In one paragraph

Article in Emerging topics in life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ruchira MannDepartment of Biosciences, Brunel University of London, Uxbridge, UB8 3PH, U.K.ORCID 0009-0002-2437-9097
Sabrina TosiDepartment of Biosciences, Brunel University of London, Uxbridge, UB8 3PH, U.K.ORCID 0000-0002-0036-0191
David TreeDepartment of Biosciences, Brunel University of London, Uxbridge, UB8 3PH, U.K.ORCID 0000-0002-8427-3334

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The proliferation of generative artificial intelligence (AI) tools has fundamentally challenged traditional written assessments across higher education, with particular implications for laboratory-based disciplines where written work may substitute for demonstration of practical competence, necessitating approaches that prioritise direct performance. This study presents the adaptation of objective structured clinical examination (OSCE) methodology from medical education to laboratory biosciences, demonstrating a practical framework for authentic assessment in the AI era. We describe and evaluate the transformation of a microscopy assessment in FHEQ Level 4 Biomedical Sciences from a traditional laboratory report to a 20-minute OSCE-style practical evaluation. The redesigned assessment maintained grade distributions while eliminating AI vulnerability through real-time performance demonstration and conversational examination. The implementation achieved close alignment between learning outcomes and assessment methods while providing inherent resistance to generative AI exploitation through direct performance requirements. Equity implications are complex and context-dependent, with potential barriers for students with communication differences alongside potential benefits for others, such as those with written communication difficulties, emphasising the importance of balanced assessment portfolios and appropriate reasonable adjustments. The cross-disciplinary adaptation demonstrates that OSCE methodology offers a scalable solution to AI-era assessment challenges, with performance-focused design maintaining academic integrity more effectively than restrictive policies while enhancing authenticity and equity outcomes.

Indexed as

academic integrityauthentic assessmentgenerative AIlaboratory assessmentOSCE

Identifiers

PMID41874440
PMCPMC13085831

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.