Impact of AI-Supported Teaching on Clinical Decision-Making in Nursing Students

NCT06999447 | Completed

• 1 other identifier: 202312Y0718
• Study Type: interventional
• Target: 66
• Locations: 1 country
• Sites: 1

Brief Summary

This clinical trial aims to explore whether an AI-supported teaching method can help nursing students improve their clinical decision-making skills and knowledge during case-based learning. The study focuses on third-year nursing students enrolled in an emergency care course. Participants are divided into two groups: one group receives traditional case-based instruction, while the other uses ChatGPT (an AI language model developed by OpenAI- (Chat Generative Pre-trained Transformer)) to support their case-solving activities. All students complete a pretest and posttest to assess their knowledge and perceptions of clinical decision-making. The main goals are to find out whether the AI-supported group performs better than the traditional group and to evaluate the relationship between students' knowledge and their clinical decision-making scores. By comparing these two teaching methods, researchers aim to understand whether integrating AI tools into nursing education can enhance learning outcomes.

Conditions

Nursing Education Research

Keywords

Clinical decision-making; Artificial intelligence; Case-based learning; Surgical nursing; Pediatric nursing

Outcome Measures

Primary Outcomes (1)

• Clinical decision-making skills

  This outcome measures clinical decision-making using the Clinical Decision-Making in Nursing Scale (CDMNS), developed by Jenkins (1983) and validated in Turkish by Durmaz (2012). The 40-item scale is rated on a 5-point Likert scale ("always" to "never") and includes four subdimensions: Search for Alternatives or Options (SAO), Canvassing of Objectives and Values (COV), Evaluation and Re-evaluation of Consequences (ERC), and Search for Information and Unbiased Assimilation of New Information (SIUANI). Each subdimension includes 10 items. Total scores range from 40 to 200; higher scores reflect stronger decision-making. Of the 40 items, 22 are positively worded and 18 are negatively worded (reverse-scored). Minimum and maximum scores for subdimensions are not explicitly defined in the original scale; instead, changes in subdimension scores were analyzed based on increase or decrease. The scale was applied at pretest and posttest.

  From baseline (before intervention) to immediately after the intervention session (same day)

Secondary Outcomes (1)

• Case-Specific Knowledge Test Score

  Immediately after the intervention session (same day)

Study Arms (2)

C-CASE Group (AI-Supported Case-Based Education)

EXPERIMENTAL

In this arm, participants received a behavioral intervention involving AI-supported education during a structured, classroom-based case-solving session. After informed consent and a pretest, students were divided into groups of six. Each group selected a representative with access to ChatGPT-4 Premium via researcher-provided credentials. These representatives interacted directly with the AI while others collaborated in real time to solve a pediatric surgical emergency case. The case included 10 structured questions and one open-ended item, based on the Bowtie model used in NCLEX. Each question was addressed in 5-minute intervals through team-based discussion. The intervention aimed to enhance clinical decision-making and case-specific knowledge. No drug, device, or clinical procedure was used; the AI-supported education was conducted entirely in an academic classroom setting using digital tools.

Other: ChatGPT-Supported Case-Based AI Education (C-CASE)

Standard Education Group (Traditional Case-Based Learning)

ACTIVE COMPARATOR

Participants in this group engaged in traditional, instructor-led case-solving sessions without access to AI tools. Students were divided into groups of six and analyzed the same pediatric surgical emergency case used in the intervention group. To ensure no use of AI platforms like ChatGPT, a classroom monitoring application was implemented. Instead, students used institutional academic databases and library resources. The activity's structure, including group size, timing, question sequence, and classroom setup, mirrored the AI-supported group's experience to ensure consistency. This arm served as the control condition to compare traditional education with AI-assisted learning in terms of clinical decision-making and knowledge development. No drug, device, or clinical procedure was used; this was a classroom-based educational activity only.

Other: Standard Education

Interventions

ChatGPT-Supported Case-Based AI Education (C-CASE) OTHER

In the intervention group (C-CASE), after informed consent and pretest completion (including a sociodemographic form and CDMNS), the case scenario was introduced by the course instructor. Students were divided into small groups, and each group selected a representative who accessed ChatGPT-4.0 Premium via credentials provided by the research team. Using a collaborative problem-solving format, each group worked through a structured case scenario involving pediatric surgical emergencies. Questions were distributed sequentially, with 5-minute intervals allocated per question. Students used ChatGPT to support reasoning and clinical decision-making within their group. After each interval, responses were submitted, and the next question was handed out. Sessions were proctored by research assistants, and the full implementation, including discussion, lasted approximately two hours. The intervention aimed to foster decision-making, teamwork, and AI literacy in a clinical nursing education.

C-CASE Group (AI-Supported Case-Based Education)

Standard Education OTHER

In the control group (Standard Education), students followed the same structured case-based learning session as the intervention group, without access to AI tools. After providing informed consent and completing the pretest (sociodemographic form and CDMNS), the case scenario was introduced by the instructor. Students were divided into small groups and selected a representative to use a personal computer during the session. To ensure no access to AI-based tools, the Mobile Guardian app was installed to block websites such as ChatGPT. Students were allowed to use only academic databases and the university's online library. Each group answered a series of timed case questions (5 minutes per item), submitting responses before receiving the next question. Research assistants monitored the session in both classrooms to ensure standardization and prevent external support. The session concluded with a class-wide case discussion, led by the course instructor.

Standard Education Group (Traditional Case-Based Learning)

Eligibility Criteria

• Age: 18 Years - 25 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• Successful completion of prerequisite courses (Fundamentals of Nursing I-II,
• Medical-Surgical Diseases Nursing, and Pediatric Nursing) along with associated clinical internships
• Enrollment in the Emergency Care course during the study period
• Volunteering to participate and providing written informed consent
• Must be a third-year undergraduate nursing student.
• Must be enrolled in the "Emergency Care Nursing" course during the 2024-2025 spring semester.
• Must be attending the Faculty of Health Sciences, Department of Nursing, at Yeditepe University.
• Completion of all data collection forms

You may not qualify if:

• Failure to complete prerequisite courses or required clinical internships
• Irregular attendance in the Emergency Care course
• Declining to participate or failure to provide written informed consent
• Submission of incomplete data collection forms

Sponsors & Collaborators

• Yeditepe University: lead

Study Sites (1)

Yeditepe University

Istanbul, Atasehir, 34755, Turkey (Türkiye)

Location

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Graduate Research Assistant

Model Details: This study used a parallel assignment model in which participants were randomly assigned to either an experimental group receiving AI-supported case-based education (C-CASE) using ChatGPT-4 or a control group receiving standard case-based instruction. Each participant remained in their assigned group throughout the study, and both groups completed pretest and posttest evaluations.

Study Record Dates

• First Submitted: May 22, 2025
• First Posted: May 31, 2025
• Study Start: March 26, 2025
• Primary Completion: March 26, 2025
• Study Completion: March 26, 2025
• Last Updated: June 6, 2025

Record last verified: 2025-06

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)