Artificial Intelligence-Assisted Learning for Nursing Drug Calculation

NCT07448259 | Completed

• 1 other identifier: AU-20-6-393
• Study Type: interventional
• Target: 56
• Locations: 1 country
• Sites: 1

Brief Summary

The purpose of this study is to evaluate how an Artificial Intelligence -assisted learning platform affects nurses' ability to calculate medication dosages accurately. Drug calculation is a critical skill in nursing, and errors can significantly impact patient safety. While traditional teaching methods are standard, they may not provide the personalized feedback needed for such a high-stakes task. This study compares two groups of nurses: one group using an Artificial Intelligence-driven software that provides interactive scenarios and real-time guidance, and another group receiving traditional classroom instruction. The researchers aim to determine whether the AI approach leads to: Improved theoretical knowledge of drug calculations. Enhanced clinical decision-making during medication administration. Increased nurses' confidence (self-efficacy) in performing these tasks in real clinical settings. In addition, a qualitative component conducted using focus group discussions to explore participants' acceptance, perceived usefulness, usability, and overall perceptions of the AI-assisted learning platform. This qualitative inquiry provides a deeper insight into nurses' experiences, attitudes toward AI integration in education, and their opinions regarding the effectiveness of the teaching and learning strategies used within the platform.

Conditions

Nursing Education; Clinical Competence; Medication Errors

Keywords

Artificial Intelligence; Drug Calculation; Nursing Knowledge; Clinical Decision-Making; Self-Efficacy; Scenario-Based Learning

Outcome Measures

Primary Outcomes (1)

• Nurses' Knowledge of Drug Calculation

  A 16-item assessment tool designed to evaluate the theoretical and practical knowledge of nurses regarding drug calculation principles (e.g., unit conversions, flow rate, and dose calculations). Each correct answer is scored "1" and each incorrect answer is scored "0". Scale Range: The total score ranges from a minimum of 0 to a maximum of 16. Interpretation: Higher scores indicate a better outcome (greater mastery of calculation principles). High (13-16): Competent level (\> 80%). Moderate (10-12): Acceptable but incomplete knowledge (60%-80%). Low (0-9): Deficient understanding (\< 60%).

  Baseline (Pre-test) and 2 weeks post-intervention (Post-test)

Secondary Outcomes (3)

• Nurses' Drug Calculation Decision-Making Scale

  Baseline (Pre-test) and 2 weeks post-intervention (Post-test)

• General Self-Efficacy Scale

  Baseline (Pre-test) and 2 weeks post-intervention (Post-test)

• Nurses' Perception and Satisfaction with Artificial Intelligence-Assisted Learning (Qualitative)

  2 weeks after the completion of the AI-assisted training

Study Arms (2)

Artificial Intelligence-Assisted Learning Group

EXPERIMENTAL

Use an Artificial Intelligence-assisted platform providing scenario-based learning and real-time feedback for drug calculations.

Device: Artificial Intelligence-Assisted Drug Calculation Platform

Traditional Learning Group

EXPERIMENTAL

Participants receive the standard curriculum through traditional lectures and paper-based practice sessions.

Other: Traditional Nursing Education

Interventions

Artificial Intelligence-Assisted Drug Calculation Platform DEVICE

An innovative Artificial Intelligence software enhances nursing accuracy in drug calculations and clinical reasoning through scenario-based learning, providing real-time feedback and adaptive learning paths.

Artificial Intelligence-Assisted Learning Group

Traditional Nursing Education OTHER

Standard classroom-based instruction consists of theoretical lectures and paper-based practice focusing on medication dosage calculations.

Traditional Learning Group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Nurses working in multiple clinical settings, including medical-surgical, cardiovascular, or critical care units..etc.
• Nurses are responsible for medication administration and drug dosage calculations as part of their daily clinical duties.
• Willingness to participate in the Artificial Intelligence-assisted learning program and sign the informed consent.

You may not qualify if:

• Nurses who had recently received specific training in drug-calculation or had any prior exposure to AI-based educational tools (within the last 6 months)

Sponsors & Collaborators

• Alexandria University: lead

Study Sites (1)

Faculty of Nursing, Alexandria University

Alexandria, Alexandria Governorate, 2500, Egypt

Location

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Lecturer of Medical-Surgical Nursing

Model Details: This study employed a mixed-methods approach, utilizing a two-arm randomized controlled trial (RCT) design for the quantitative component and a focus group discussion for the qualitative component. This design was used to evaluate the effect of Artificial Intelligence assisted learning on nursing staff's drug calculation knowledge, clinical decision-making, and self-efficacy, while simultaneously exploring their perceptions and experiences with the AI platform.

Study Record Dates

• First Submitted: February 18, 2026
• First Posted: March 4, 2026
• Study Start: September 22, 2025
• Primary Completion: December 30, 2025
• Study Completion: December 30, 2025
• Last Updated: April 1, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

EG (1)