Evidence map›Paper›PMID 42102014›Full record

ArticleMedical principles and practice : international journal of the Kuwait University, Health Science Centre2026

Blending Artificial Intelligence with Expertise: A Comparative Study of Concept Maps in Anatomy Education.

Salem Albuainain, Yousif Alkubaisi, Sami Zaqout

Abstract read
In one paragraph

Article in Medical principles and practice : international journal of the Kuwait University, Health Science Centre, 2026. 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.

Salem AlbuainainDepartment of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha, Qatar.
Yousif AlkubaisiDepartment of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha, Qatar.
Sami ZaqoutDepartment of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha, Qatar, sami.zaqout@qu.edu.qa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveConcept maps are valuable tools in anatomy education, helping students organize complex information visually. With the rise of generative artificial intelligence (AI), tools like large language models (LLMs) and large action models (LAMs) offer new opportunities to automate concept map creation. However, their effectiveness compared with human-generated maps remains unclear.

methodsIn this cross-sectional study, 74 medical students at Qatar University evaluated three concept maps - one generated by a LLM, one by a LAM, and one by a human expert. Students rated each map on clarity, structure, and scientific accuracy using Likert-scale items and selected their preferred map based on explanation, engagement, and future use. Data were analyzed using non-parametric and categorical tests.

resultsHuman-generated maps demonstrated the highest ratings for contribution to understanding, while clarity ratings were comparable between the human-generated and LLM maps. Maps generated by the action-based model received lower ratings overall. Students consistently preferred the human-generated map across all categories, including explanation, engagement, and future use. While overall differences in categorical preferences were not statistically significant consistently, the selected pairwise comparisons showed significant differences favoring the human-generated map.

conclusionsHuman-generated concept maps remain the most effective and preferred learning tool in anatomy education. AI models, particularly LLMs, show promising potential as supportive tools but do not yet surpass expert-designed materials.

Indexed as

Anatomy educationArtificial intelligence-generated educational toolsConcept mapsLarge language modelsVisual learning

Identifiers

PMID42102014
PMCPMC13341128

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.