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Background <p>This study evaluated whether a structured pedagogical framework integrating large language models (LLMs) into residency training could develop clinical reasoning competencies that transfer to independent performance.</p> Methods <p>In this prospective randomized controlled trial, residents were assigned to Teacher–Student–Machine Interaction Autonomous Learning (TSM-AL) group or traditional teaching group. The TSM-AL framework positioned LLMs as supervised cognitive auxiliaries within a five-step case analysis process featuring controlled information release, predefined reasoning tasks, and structured utilization guidelines. Six formative assessments were conducted over 16 weeks, followed by summative examinations under both LLM-assisted and unassisted conditions. Outcomes included clinical reasoning scores, entrustable professional activity (EPA) levels, global faculty ratings, expert-aligned diagnostic accuracy, and standardized patient examination performance.</p> Results <p>Significant between-group differences in diagnostic pathway completeness emerged at week 14 (3.53 ± 1.36 vs. 2.68 ± 1.49, <i>P</i> = 0.018) and week 16 (3.35 ± 1.43 vs. 2.37 ± 1.51, <i>P</i> = 0.009). In the unassisted final examination, the TSM-AL group demonstrated significantly higher clinical reasoning scores (7.32 ± 2.30 vs. 5.12 ± 1.66, <i>P</i> &lt; 0.001). EPA distributions differed significantly (<i>P</i> = 0.034), with fewer TSM-AL residents requiring complete supervision (11.76% vs. 29.41%). Global faculty ratings also differed significantly (<i>P</i> = 0.019), with 23.53% of TSM-AL residents achieving Level 5 compared to none in the traditional group. Expert alignment for core problem identification (67.65% vs. 44.12%, <i>P</i> = 0.042) and differential diagnosis matching (73.53% vs. 50.00%, <i>P</i> = 0.046) favored the TSM-AL group. Standardized patient examination scores were significantly higher in the TSM-AL group (80.88 ± 3.82 vs. 78.71 ± 3.90, <i>P</i> = 0.025). Notably, TSM-AL residents performed better without LLM assistance than with assistance.</p> Conclusions <p>The TSM-AL framework significantly enhanced clinical reasoning competencies that transferred to independent performance, demonstrating that structured LLM integration develops autonomous reasoning rather than fostering technological dependency.</p>

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Teacher–student–machine interaction autonomous learning: a structured LLM-integrated framework for developing independent clinical reasoning in residency training

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Pengru Wang,
  • He Li,
  • Dingyuan Tu,
  • Huilong Qu,
  • Mengli Chang,
  • Qiying Zhang,
  • Gan Xu,
  • Bo Li,
  • Yingtian Wang,
  • Wei Xu

摘要

Background

This study evaluated whether a structured pedagogical framework integrating large language models (LLMs) into residency training could develop clinical reasoning competencies that transfer to independent performance.

Methods

In this prospective randomized controlled trial, residents were assigned to Teacher–Student–Machine Interaction Autonomous Learning (TSM-AL) group or traditional teaching group. The TSM-AL framework positioned LLMs as supervised cognitive auxiliaries within a five-step case analysis process featuring controlled information release, predefined reasoning tasks, and structured utilization guidelines. Six formative assessments were conducted over 16 weeks, followed by summative examinations under both LLM-assisted and unassisted conditions. Outcomes included clinical reasoning scores, entrustable professional activity (EPA) levels, global faculty ratings, expert-aligned diagnostic accuracy, and standardized patient examination performance.

Results

Significant between-group differences in diagnostic pathway completeness emerged at week 14 (3.53 ± 1.36 vs. 2.68 ± 1.49, P = 0.018) and week 16 (3.35 ± 1.43 vs. 2.37 ± 1.51, P = 0.009). In the unassisted final examination, the TSM-AL group demonstrated significantly higher clinical reasoning scores (7.32 ± 2.30 vs. 5.12 ± 1.66, P < 0.001). EPA distributions differed significantly (P = 0.034), with fewer TSM-AL residents requiring complete supervision (11.76% vs. 29.41%). Global faculty ratings also differed significantly (P = 0.019), with 23.53% of TSM-AL residents achieving Level 5 compared to none in the traditional group. Expert alignment for core problem identification (67.65% vs. 44.12%, P = 0.042) and differential diagnosis matching (73.53% vs. 50.00%, P = 0.046) favored the TSM-AL group. Standardized patient examination scores were significantly higher in the TSM-AL group (80.88 ± 3.82 vs. 78.71 ± 3.90, P = 0.025). Notably, TSM-AL residents performed better without LLM assistance than with assistance.

Conclusions

The TSM-AL framework significantly enhanced clinical reasoning competencies that transferred to independent performance, demonstrating that structured LLM integration develops autonomous reasoning rather than fostering technological dependency.