Effect of a Localized ICU AI Teaching Agent on Rotating ICU Residents

Summary

This trial is an ongoing single-center, pragmatic, parallel-group randomized controlled superiority trial currently in participant recruiting phase, conducted within intensive care unit teaching wards at Peking Union Medical College Hospital, Beijing, China. The scheduled trial implementation period spans March 2026 to June 2026, aiming to evaluate whether an institution-specific, protocol-bound retrieval-augmented AI educational agent (named ICU-Tutor) can reduce residents' extraneous cognitive load and improve standardized ICU protocol task performance compared with free access to unrestricted commercial general-purpose large language model AI tools during early ICU clinical rotation.

The trial plans to screen a total of 44 first-time ICU rotating resident candidates, with pre-defined exclusion standards to eliminate unqualified individuals; approximately 44 eligible residents will undergo 1:1 stratified randomization and be split into two research arms: 22 participants assigned to the ICU-Tutor intervention group and 22 assigned to the unrestricted general AI control group.

All enrolled subjects will complete standardized 14-day follow-up assessments as pre-specified in the trial protocol. Both study cohorts receive unified 15-minute standardized training covering standardized safe AI clinical application rules prior to formal intervention initiation. ICU-Tutor is strictly built on a curated knowledge base including 247 ICU institutional protocols validated by senior attending intensivists, with all AI outputs traceable back to original local protocol documents and constrained within verified institutional guidance content only. The control arm allows participants to select and utilize any mainstream general large-model AI tools per personal preference without content or access limitations, consistent with real-world daily resident clinical practice.

Two co-primary endpoints are uniformly scheduled to be measured on the 7th day after randomization, including total completion duration of standardized ICU protocol task battery and Paas 9-point validated cognitive load scale score reflecting participants' subjective mental workload during task execution. Three confirmatory secondary endpoints are pre-defined for centralized assessment: composite task performance score on Day7, written institutional protocol knowledge retention score tested on Day14, and 0-100-point visual analog scale (VAS) evaluating resident satisfaction toward allocated AI support on Day7. Individual sub-station scores of three split practical ICU skill modules are set as exploratory secondary endpoints for post-hoc descriptive analysis only.

The statistical analysis framework is pre-specified to follow intention-to-treat principle entirely. Analysis of covariance (ANCOVA) is selected as core analytical method for all continuous outcomes, with Day3 baseline assessment result and participants' academic training background set as pre-planned covariates. Bonferroni multiple-testing correction is applied for dual co-primary endpoints, while Benjamini-Hochberg false discovery rate (FDR) correction is pre-specified to control type I error across three confirmatory secondary outcomes. Effect sizes will be quantified via Cohen's d after raw data collection and database lock.

The trial has obtained formal ethical approval from the Institutional Review Board of Peking Union Medical College Hospital (Approval ID: I-26ZM0024). Every enrolled resident provides written informed consent before random assignment.

Conditions

• Cognitive Load and Task Performance in Rotating Residents

Interventions

DEVICE

ICU-Tutor AI Agent

retrieval-augmented generation (RAG) AI system built exclusively on verified local ICU protocols covering mechanical ventilation, hemodynamic management, sedation/analgesia, CRRT, antimicrobial stewardship, emergency response, nutrition, delirium, VTE prevention, and end-of-life care. All responses are limited to pre-approved content with embedded source citations.

OTHER

General-Purpose Large Language Models

Participants may use any mainstream general AI platforms (e.g., ChatGPT, Claude, Gemini) without restrictions on tool type, usage frequency, or content scope. No centralized usage logging will be performed for this arm.

Sponsors & Collaborators

• Peking Union Medical College Hospital

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

OTHER

Masking

SINGLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Max Age

40 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2026-03-20

Primary Completion

2026-06-30

Completion

2026-07-05

Countries

• China

Study Locations