Artificial Intelligence-Assisted Learning for Nursing Drug Calculation

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.

Medication administration errors are a significant challenge in nursing practice, particularly in high-acuity environments such as cardiovascular and critical care units. This study evaluates the effectiveness of an Artificial Intelligence-driven educational intervention designed to bridge the gap between theoretical knowledge and clinical application in drug calculations. Study Design This study employed a mixed-methods design comprising a quasi-experimental pretest-posttest approach with a control group, complemented by a qualitative focus group component. Participants were allocated to either an experimental group receiving Artificial Intelligence-assisted learning or a control group receiving traditional instruction The Intervention (ٍStudy Group) Participants in the experimental group used Artificial Intelligence-assisted learning software designed to enhance their educational experience through several advanced features. The software provides Adaptive Learning Paths, which adjust calculation complexity in accordance with the nurse's performance. Additionally, it offers Real-Time Feedback, ensuring immediate corrections and step-by-step guidance for complex drug dosing. Lastly, the software incorporates Artificial Intelligence-based Clinical Simulations that create high-pressure clinical decision-making scenarios for learners. The Control Group Participants in the control group received traditional teaching methods that encompassed standard lectures and paper-based practice sessions specifically aimed at drug calculation. This approach covered the same core curriculum as the experimental group but did not incorporate any Artificial Intelligence assistance. The study evaluated three key areas before and after the intervention: Nursing Knowledge, assessed using a standardized drug calculation examination. Clinical Decision-Making, measured with a validated nursing decision-making scale. Self-Efficacy, evaluated through a standardized self-efficacy scale to assess confidence in clinical calculations. Data were analyzed using the Statistical Package for the Social Sciences to compare the mean scores between the experimental and control groups. Qualitative Component (Focus Group Study) To complement the quantitative findings, a qualitative focus group study was conducted with participants from the experimental group. The aim was to explore nurses' acceptance of the Artificial Intelligence platform, perceived usefulness, usability, visibility of learning progress, and overall opinions regarding the Artificial Intelligence-assisted teaching strategies. Focus group discussions were audio-recorded, transcribed, and analyzed using thematic analysis to identify recurring patterns and themes related to user experience, perceived educational value, and readiness to integrate AI-based learning into clinical education. This qualitative component provided deeper insight into participants' attitudes toward AI integration in nursing education and enriched the interpretation of the quantitative outcomes.