Evidence map›Paper›PMID 41356944›Full record

ArticleCureus2025

Generative AI: A Disruptor to Health Professions Learner Assessment.

Leo Morjaria, Bhavya Gandhi, Levi Burns, Matthew Sibbald

Abstract readEditorial
In one paragraph

Article in Cureus, 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

4 authors.

Leo MorjariaSchool of Medicine, McMaster University, Hamilton, CAN.
Bhavya GandhiInternal Medicine, McMaster University, Hamilton, CAN.
Levi BurnsRadiation Oncology, University of Toronto, Toronto, CAN.
Matthew SibbaldCardiology, McMaster University, Hamilton, CAN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the widespread release of generative artificial intelligence (GenAI) tools in recent years, there has been a dramatic impact on health professions education, particularly in the context of learner assessment. As GenAI continues to evolve, traditional paradigms in health professions education are changing, requiring students, administrators, and educators to navigate ongoing disruption to their practice. We examine how GenAI will specifically impact assessment practices, offering three key postulates to guide future teaching and learning: (1) educators must re-examine assessment constructs to align with GenAI-enhanced learning environments, (2) GenAI can outperform median learner performance with appropriate prompting, and (3) GenAI will become a required tool for health professions assessment. Ultimately, we believe that rather than viewing the advent of GenAI as a threat, educators should harness its potential to empower students and augment learning.

Indexed as

artificial intelligence in assessmentgenerative artificial intelligencehealth professions educationlearner assessmentmedical education

Identifiers

PMID41356944
PMCPMC12675138

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.