Innovative Electrocardiogram Training Using Artificial Intelligence Clinical Scenarios for Nursing Staff

Impact of Innovative ECG Training Using AI-Supported Clinical Scenarios on Knowledge, Clinical Reasoning, and Self-Efficacy Among Nursing Staff

Background and Purpose Accurate interpretation of an Electrocardiogram is a vital skill for nursing staff to ensure patient safety and timely intervention in cardiovascular care. Traditional training methods often lack the interactive and complex nature of real-life clinical situations. This study aims to evaluate the effectiveness of an innovative training program that uses Artificial Intelligence to create realistic clinical scenarios. The goal is to determine if this technology-enhanced approach improves nurses' knowledge, their ability to make clinical decisions (clinical reasoning), and their confidence in performing these tasks (self-efficacy).

Study Design and Methodology The researchers will conduct a study involving nursing staff to compare their performance before and after the training intervention. Participants will engage with Artificial Intelligence supported clinical scenarios specifically designed for Electrocardiogram interpretation.

Data Collection

To measure the impact of the training, the study will use four primary tools:

An Electrocardiogram Interpretation Knowledge Test to measure theoretical understanding.

An assessment of Nursing Decision-Making in Electrocardiogram Interpretation to evaluate practical clinical reasoning.

A Self-Efficacy Scale for Artificial Intelligence-based Electrocardiogram Training to measure the participants' confidence in their skills.

Focus group discussions will be held at the end of the study to gain deeper qualitative insights into the nursing staff's experiences and perceptions of using technology in their professional development.

Study Overview

• Status: Completed
• Conditions: Artificial Intelligence; Nursing Education; Clinical Reasoning; Clinical Decision-Making; Electrocardiogram Interpretation; Cardiovascular Care
• Intervention / Treatment: Device: Artificial Intelligence Driven Scenario-Based Learning Software; Other: Traditional Electrocardiogram Educational Program

• Study Type: Interventional
• Enrollment (Actual): 64
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Egypt
  • Alexandria, Egypt, 21511
    • Faculty of Nursing, Alexandria University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria

• Nursing staff currently employed in clinical practice.
• Willingness to participate in the study and provide written informed consent.
• Ability to use basic computer software or mobile applications to interact with the Artificial Intelligence platform.

Exclusion Criteria

• Nurses who have attended advanced Electrocardiogram certification courses or specialized training within the past three months to avoid bias in the baseline knowledge assessment.
• Nurses who have previously participated in formal training or research studies involving Artificial Intelligence-driven educational platforms or clinical decision-support systems to ensure responses and perceived self-efficacy are not influenced by prior familiarity.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Artificial Intelligence Driven Training Group; Participants in this group will utilize an original, specifically designed learning software developed by the researcher that integrates Artificial Intelligence to provide dynamic clinical scenarios. The intervention focuses on interactive training for Electrocardiogram interpretation. Each scenario is tailored to the learner's performance, providing immediate feedback and simulating real-world cardiovascular care challenges. This group will complete pre-test and post-test assessments, followed by focus group discussions to explore their qualitative experiences with the software.	Device: Artificial Intelligence Driven Scenario-Based Learning Software; This intervention consists of an original educational software designed and developed by the researcher. The software utilizes Artificial Intelligence to generate interactive and adaptive clinical scenarios focused on Electrocardiogram interpretation. Participants interact with high-fidelity simulations where the Artificial Intelligence engine adjusts the complexity of the case based on the user's responses. The software provides immediate feedback, rationales for correct nursing decisions, and tracks the progress of the nursing staff in real-time. Training sessions are structured to enhance clinical reasoning and self-efficacy through immersive, technology-enhanced learning
Active Comparator: Traditional Training Control Group; Participants in this group will receive the standard educational intervention for Electrocardiogram interpretation used in traditional nursing education. This typically includes conventional classroom lectures, printed educational materials, and standard presentation slides without the interactive or adaptive features of Artificial Intelligence. This group will complete the same pre-test and post-test assessments as the intervention group to provide a baseline for comparing the effectiveness of the new technology-enhanced method.	Other: Traditional Electrocardiogram Educational Program; This intervention represents the standard educational approach for nursing staff. It includes traditional classroom-based lectures and the use of static educational materials such as printed manuals and PowerPoint presentations. The content covers the same theoretical and practical principles of Electrocardiogram interpretation as the intervention group but without the use of Artificial Intelligence or interactive clinical scenarios. The sessions are led by an instructor in a conventional learning environment, focusing on passive knowledge acquisition and standardized clinical examples.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Electrocardiogram Interpretation Knowledge Score	A comprehensive assessment tool designed to evaluate the theoretical and practical knowledge of nursing staff: Consists of 15 multiple-choice questions specifically designed to assess the cognitive knowledge level of nurses regarding the fundamental principles of Electrocardiogram interpretation.such as analysis of basic waveform components, calculating heart rate, identification of common arrhythmias, atrioventricular conduction abnormalities, and evaluation of life-threatening cardiac rhythms. Each correct answer is awarded one point, with a total possible score of 15. where scores range from a minimum of 0 to a maximum of 15, Higher scores indicate a higher level of knowledge in Electrocardiogram interpretation.	Baseline (Pre-test) and 2 weeks post-intervention (Post-test)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Nursing Decision-Making Scale in Electrocardiogram Interpretation	This is an 8-item self-report tool designed to assess the clinical judgment and confidence of nursing staff regarding Electrocardiogram interpretation. Each item is rated on a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Scoring and Interpretation: Minimum and Maximum Values: The total score ranges from a minimum of 8 to a maximum of 40. High Competence (30-40): Indicates high clinical judgment and professional confidence in interpreting heart rhythms. Moderate Ability (19-29): Indicates moderate clinical judgment; the nurse may require supervision or further guidance. Low Competence (8-18): Indicates poor confidence and a high risk of errors in clinical judgment. Higher total scores reflect a better outcome, representing greater clinical competence and safer decision-making in cardiovascular care.	Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
General Self-Efficacy Scale for Electrocardiogram Interpretation and Clinical Tasks	A 10-item psychometric scale adapted to assess the nursing staff's perceived confidence and self-belief in their ability to perform Electrocardiogram interpretation and associated clinical tasks effectively under various conditions. Scoring System: Each item is scored on a 4-point Likert scale: Not at all true; Hardly true; Moderately true; Exactly true Interpretation of Total Score (Range 10-40): High Self-Efficacy (31-40): Indicates strong perceived competence and high self-belief in managing cardiovascular care challenges. Moderate Self-Efficacy (21-30): Indicates moderate confidence in managing clinical demands. Low Self-Efficacy (10-20): Indicates low belief in the ability to cope with professional challenges in Electrocardiogram interpretation. Higher total scores indicate a better outcome, representing stronger perceived competence and higher self-efficacy among the nursing staff.	Baseline (Pre-test) and two weeks after the completion of the training intervention (Follow-up test).
Nurses' Perception and Satisfaction with Artificial Intelligence-Assisted Learning in Electrocardiogram Interpretation	This qualitative outcome assesses the participants' acceptance, perceived usefulness, usability, and satisfaction with the Artificial Intelligence-assisted learning platform specifically designed for Electrocardiogram interpretation. Data Collection and Analysis: Method: Data will be collected through focus group discussions to explore the nursing staff's lived experiences and feedback. Method of Analysis: Results will be analyzed using Thematic Analysis to identify recurring patterns, themes, and sub-themes related to the intervention.	Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
Nursing Clinical Decision-Making Score using Case Vignettes	Measured using a specialized assessment tool consisting of 10 clinical case vignettes. Each vignette presents a specific patient scenario related to cardiovascular care and requires the participant to analyze Electrocardiogram findings and choose the most appropriate nursing intervention through Multiple-Choice Questions. This tool evaluates the nursing staff's ability to apply theoretical knowledge to practical, real-life clinical situations, where scores range from a minimum of 0 to a maximum of 10, with higher scores indicating a better outcome, Higher total scores across the 10 vignettes indicate superior clinical decision-making skills and better professional judgment in Electrocardiogram interpretation.	Baseline (Pre-test) and 2 weeks post-intervention (Post-test)

Collaborators and Investigators

• Sponsor: Alexandria University

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2025
• Primary Completion (Actual): February 10, 2026
• Study Completion (Actual): February 10, 2026

Study Registration Dates

• First Submitted: March 3, 2026
• First Submitted That Met QC Criteria: March 3, 2026
• First Posted (Actual): March 6, 2026

Study Record Updates

• Last Update Posted (Actual): March 13, 2026
• Last Update Submitted That Met QC Criteria: March 11, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: Electrocardiogram Interpretation; Artificial Intelligence in Nursing Education; Clinical Reasoning and Decision-Making; Nursing Staff Competency; Self-Efficacy in Clinical Practice; Innovative Simulation Training
• Other Study ID Numbers: AU-20-6-392

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No