Artificial Intelligence Assisted Teaching of Structured Nursing Care According to ERAS Protocol

Summary

This study provides an original contribution to the literature by structuring nursing care for gyneco-oncology patients according to the ERAS protocol and delivering it through AI-assisted, case-based education.

This randomized controlled, fully experimental study was conducted in the second semester of the 2024-2025 academic year with third-year students who had successfully completed the Obstetrics and Gynecology Nursing course at the Faculty of Health Sciences, Department of Nursing, at a state university located in western Turkey. Seventy students participated in the study and were assigned to either the AI-assisted case-based education group or the control group using block randomization. Data were collected using the ERAS Protocol Nursing Care Knowledge Test and the Multidimensional 21st Century Skills Scale. All statistical analyses were conducted using IBM SPSS Statistics (Version 27) and the PROCESS macro (Version 4.2) developed by Hayes. Descriptive statistics were used to summarize demographic variables and baseline characteristics. Group equivalence at baseline was assessed using independent samples t-tests for continuous variables and chi-square tests for categorical variables. Effect sizes were calculated using Cohen's d and Cramér's V, as appropriate. To examine the effect of the intervention on 21st-century skills, a mixed-design ANOVA was performed with time (pretest, posttest) as the within-subjects factor and group (intervention, control) as the between-subjects factor. Partial eta squared (η²) was reported as a measure of effect size for within- and between-subjects effects. An independent samples t-test was also used to compare posttest knowledge scores between the groups. To explore the mechanisms underlying the effect of the intervention on knowledge acquisition, mediation and moderation analyses were performed using PROCESS Models 4 and 1, respectively. In the mediation model, the change in 21st-century skills (posttest-pretest difference) was tested as a mediator. In the moderation model, academic achievement was examined as a moderator of the group-knowledge relationship. Both models controlled for academic achievement where applicable. Statistical significance was set at p \< .05, and 95% confidence intervals (CI) were reported for all relevant estimates.

Conditions

• AI (Artificial Intelligence)
• Case-based Learning
• Nursing Students
• ERAS
• Education Nursing

Interventions

OTHER

Case-based learning (CBL)

During the training session, students were given a detailed explanation of an artificial intelligence (AI)-assisted case presentation structured according to the ERAS protocol. They were briefed on the data collection tools they would need to complete before and after the session. A convenient time was set, considering all participants' accessibility, and students were provided a link they could access from their mobile phones, tablets, or computers. Students participated in the AI-assisted case session online using this link. During the training process, a case scenario developed by the researchers and optimized with AI was presented to students on a digital platform. The case encompassed the entire patient care process, from diagnosis to discharge, simulating a real-life clinical environment. On the AI-assisted platform, students followed the case flow step by step, actively participating by entering their own responses at each stage of the clinical decision-making process. The plat

Sponsors & Collaborators

• Sakarya University

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

OTHER

Masking

NONE

Model

PARALLEL

Eligibility

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2025-06-27

Primary Completion

2025-07-10

Completion

2025-07-22

Countries

• Turkey (Türkiye)

Study Locations