PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 26, 2026Emerging Topics in Life Sciences2 citationsOpen Access

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

View Full Paper
RMRuchira MannSTSabrina TosiDTDavid Tree

Key Points

  • The aim is to adapt OSCE methodology to improve authentic assessment in laboratory-based biosciences amid challenges from generative AI.
  • Reformulated traditional microscopy assessments into an OSCE-style practical evaluation.
  • Implemented a 20-minute performance demonstration format.
  • Maintained existing grade distributions while minimizing AI exploitation risk.
  • Evaluated impacts on learning outcomes and assessment methods.
  • The redesigned assessment maintained consistent grade distributions.
  • Real-time performance demonstration significantly reduced AI vulnerability.
  • Close alignment between learning outcomes and assessment methods was achieved.
  • Examined equity implications, noting both potential barriers and benefits for diverse student needs.

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.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Mann et al. (2025) studied this question.

synapsesocial.com/papers/69c4cd49fdc3bde44891975dhttps://doi.org/10.1042/etls20253021
Ask AI
Helpful
Bookmark
Share
View Full Paper