Trial reportBMC medical education2026
Rebuilding ability acquisition in medical imaging teaching: a multi-level AI-driven teaching paradigm based on cognitive-information theory.
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. Not yet cited in PubMed.
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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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveMedical imaging education faces challenges like learner variability and delayed feedback, hindering efficiency and skill transfer. While AI shows promise, no previous studies have systematically tested multi-layer AI teaching frameworks using dual-level randomized experiments. This study evaluates a cognitive informatics–driven “three-level–four-module” AI teaching paradigm.
methodsSixty postgraduate imaging students participated in a course-level RCT (AI paradigm vs. traditional teaching, 8–12 weeks) and a 2 × 2 factorial experiment to dissect the effects of “explainable guidance” and “adaptive scheduling.” Data were analyzed using linear mixed-effects models and DeLong tests.
resultsThe AI paradigm significantly improved diagnostic performance (AUC: β = 0.072, P < 0.001; accuracy: +5.8%, P = 0.004) and reduced cognitive load (NASA-TLX, η²=0.12, P < 0.01). Factorial analysis revealed both components independently increased AUC (Δ ≈ 0.05, P < 0.01) with a positive synergistic interaction (β = 0.021, P = 0.03), and cognitive load reduction was maximal when combined (Δ=–17.6, P = 0.01).
conclusionThe AI-driven paradigm enhances learning effectiveness and reduces cognitive load, addressing a critical gap by providing scalable, evidence for multi-layer AI integration in medical imaging instruction.
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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.