Trial reportBMC medical education2026
Application and effectiveness analysis of AI Intelligent tutoring system combined with knowledge graph in basic medical pathology teaching.
Trial report in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- A Case-Driven and Knowledge Graph-Supported Approach to Pathology Education: Effects on Knowledge Retention and Clinical Reasoning.Advances in medical education and practice · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundBasic medical pathology teaching suffers from fragmented knowledge and insufficient personalized learning, which impairs students' systematic knowledge construction and learning efficiency. This study aimed to apply an AI and knowledge graph integrated teaching model to address these challenges and provide references for basic medical education reform.
methodsAn 18-week randomized controlled trial was conducted on 360 nursing students (180 each in experimental/control groups). The control group used traditional teaching, while the experimental group adopted the "AI + knowledge graph" integrated model with AI-assisted learning and knowledge graph reviews. SPSS was used for quantitative and qualitative data analysis.
resultsThe experimental group showed significantly better outcomes: final score (84.2 ± 6.9 vs. 74.1 ± 7.6), after adjusting for class-level clustering using a two-level mixed-effects model, adjusted mean difference = 8.2, 95% CI: 4.7-11.7, P = 0.012, Cohen's d = 1.13 (95% CI not computed), indicating a large effect size. knowledge mastery rate (90.3% vs.75.8%), clinical case analysis score (28.5±3.2 vs. 21.3±4.1, P<0.001). It also reduced ineffective learning time.
conclusionThis integrated model effectively promotes structured knowledge construction and learning efficiency, realizing the synergistic effect of AI and knowledge graph. It provides a replicable and practical reference for the digital and intelligent reform of basic medical education courses.
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What OpenQuestion holds
Registered trials
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.