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Objective <p>Medical 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.</p> Methods <p>Sixty 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.</p> Results <p>The AI paradigm significantly improved diagnostic performance (AUC: β = 0.072, <i>P</i> &lt; 0.001; accuracy: +5.8%, <i>P</i> = 0.004) and reduced cognitive load (NASA-TLX, η²=0.12, <i>P</i> &lt; 0.01). Factorial analysis revealed both components independently increased AUC (Δ ≈ 0.05, <i>P</i> &lt; 0.01) with a positive synergistic interaction (β = 0.021, <i>P</i> = 0.03), and cognitive load reduction was maximal when combined (Δ=–17.6, <i>P</i> = 0.01).</p> Conclusion <p>The 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.</p>

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Rebuilding ability acquisition in medical imaging teaching: a multi-level AI-driven teaching paradigm based on cognitive-information theory

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月30日

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  • Jie An,
  • Xi Leng,
  • Yujie Liu,
  • Jie Zhou,
  • Yi Liang,
  • Yuna Chen,
  • Shijun Qiu

摘要

Objective

Medical 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.

Methods

Sixty 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.

Results

The 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).

Conclusion

The 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.