Ignite Creation Date:
2026-03-26 @ 3:15 PM
Ignite Modification Date:
2026-03-30 @ 9:50 PM
Study NCT ID:
NCT07448259
Status:
COMPLETED
Last Update Posted:
2026-03-04
First Post:
2026-02-18
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Artificial Intelligence-Assisted Learning for Nursing Drug Calculation
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'HEALTH_SERVICES_RESEARCH', 'interventionModel': 'PARALLEL', 'interventionModelDescription': "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."}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 56}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2025-09-22', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2025-12-30', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2026-02-28', 'studyFirstSubmitDate': '2026-02-18', 'studyFirstSubmitQcDate': '2026-02-28', 'lastUpdatePostDateStruct': {'date': '2026-03-04', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-03-04', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12-30', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': "Nurses' Knowledge of Drug Calculation", 'timeFrame': 'Baseline (Pre-test) and 2 weeks post-intervention (Post-test)', 'description': '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".\n\nScale Range: The total score ranges from a minimum of 0 to a maximum of 16.\n\nInterpretation: Higher scores indicate a better outcome (greater mastery of calculation principles).\n\nHigh (13-16): Competent level (\\> 80%).\n\nModerate (10-12): Acceptable but incomplete knowledge (60%-80%).\n\nLow (0-9): Deficient understanding (\\< 60%).'}], 'secondaryOutcomes': [{'measure': "Nurses' Drug Calculation Decision-Making Scale", 'timeFrame': 'Baseline (Pre-test) and 2 weeks post-intervention (Post-test)', 'description': 'This scale is an 8-item self-report tool designed to assess the clinical judgment and confidence of nurses regarding medication dosage calculations. Each item is rated on a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree).\n\nMinimum and Maximum Values: The total score ranges from a minimum of 8 to a maximum of 40.\n\nInterpretation: Higher scores indicate a better outcome (greater clinical competence and safer decision-making).\n\nHigh (30-40): High competence.\n\nModerate (19-29): Moderate ability; requires supervision.\n\nLow (8-18): Poor confidence and judgment.'}, {'measure': 'General Self-Efficacy Scale', 'timeFrame': 'Baseline (Pre-test) and 2 weeks post-intervention (Post-test)', 'description': "Description: A 10-item psychometric scale used to assess nurses' perceived confidence and self-belief in their ability to perform drug calculations and clinical tasks effectively under various conditions. Each item is scored on a 4-point Likert scale: 1 (Not at all true), 2 (Hardly true), 3 (Moderately true), and 4 (Exactly true).\n\nScale Range: The total score ranges from a minimum of 10 to a maximum of 40.\n\nInterpretation: Higher scores indicate a better outcome (stronger perceived competence and higher self-efficacy).\n\nHigh (31-40): Strong perceived competence and self-belief.\n\nModerate (21-30): Moderate confidence in managing demands.\n\nLow (10-20): Low belief in ability to cope with challenges."}, {'measure': "Nurses' Perception and Satisfaction with Artificial Intelligence-Assisted Learning (Qualitative)", 'timeFrame': '2 weeks after the completion of the AI-assisted training', 'description': 'Description: Assessment of participants\' acceptance, perceived usefulness, usability, and satisfaction with the Artificial Intelligence-assisted learning platform. Data will be collected through focus group discussions.\n\nMethod of Analysis: Results will be analyzed using Thematic Analysis to identify recurring patterns and themes.\n\nUnit of Measure: This is a qualitative outcome; results will be reported as narrative themes (for example: "Improved Calculation Confidence" or "User Interface Satisfaction").'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial Intelligence', 'Drug Calculation', 'Nursing Knowledge', 'Clinical Decision-Making', 'Self-Efficacy', 'Scenario-Based Learning'], 'conditions': ['Nursing Education', 'Clinical Competence', 'Medication Errors']}, 'referencesModule': {'availIpds': [{'url': 'https://www.alexu.edu.eg/index.php/en/', 'type': 'Study Protocol', 'comment': 'Available upon request from the Principal Investigator for legitimate research purposes'}]}, 'descriptionModule': {'briefSummary': "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.\n\nWhile 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.\n\nThe researchers aim to determine whether the AI approach leads to:\n\nImproved 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.\n\nIn 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.", 'detailedDescription': "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.\n\nStudy Design\n\nThis 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\n\nThe Intervention (ٍStudy Group)\n\nParticipants 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.\n\nThe Control Group\n\nParticipants 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.\n\nThe study evaluated three key areas before and after the intervention:\n\nNursing Knowledge, assessed using a standardized drug calculation examination.\n\nClinical Decision-Making, measured with a validated nursing decision-making scale.\n\nSelf-Efficacy, evaluated through a standardized self-efficacy scale to assess confidence in clinical calculations.\n\nData were analyzed using the Statistical Package for the Social Sciences to compare the mean scores between the experimental and control groups.\n\nQualitative Component (Focus Group Study)\n\nTo 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.\n\nFocus 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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\nNurses working in multiple clinical settings, including medical-surgical, cardiovascular, or critical care units..etc.\n\nNurses are responsible for medication administration and drug dosage calculations as part of their daily clinical duties.\n\nWillingness to participate in the Artificial Intelligence-assisted learning program and sign the informed consent.\n\nExclusion Criteria:\n\nNurses who had recently received specific training in drug-calculation or had any prior exposure to AI-based educational tools (within the last 6 months)'}, 'identificationModule': {'nctId': 'NCT07448259', 'briefTitle': 'Artificial Intelligence-Assisted Learning for Nursing Drug Calculation', 'organization': {'class': 'OTHER', 'fullName': 'Alexandria University'}, 'officialTitle': 'Transforming Nursing Practice Through Artificial Intelligence: The Effectiveness of Artificial Intelligence-Based Learning in Drug Dose Calculation on Knowledge, Clinical Decisions, and Self-Efficacy', 'orgStudyIdInfo': {'id': 'AU-20-6-393'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Artificial Intelligence-Assisted Learning Group', 'description': 'Use an Artificial Intelligence-assisted platform providing scenario-based learning and real-time feedback for drug calculations.', 'interventionNames': ['Device: Artificial Intelligence-Assisted Drug Calculation Platform']}, {'type': 'EXPERIMENTAL', 'label': 'Traditional Learning Group', 'description': 'Participants receive the standard curriculum through traditional lectures and paper-based practice sessions.', 'interventionNames': ['Other: Traditional Nursing Education']}], 'interventions': [{'name': 'Artificial Intelligence-Assisted Drug Calculation Platform', 'type': 'DEVICE', 'description': '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.', 'armGroupLabels': ['Artificial Intelligence-Assisted Learning Group']}, {'name': 'Traditional Nursing Education', 'type': 'OTHER', 'description': 'Standard classroom-based instruction consists of theoretical lectures and paper-based practice focusing on medication dosage calculations.', 'armGroupLabels': ['Traditional Learning Group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '2500', 'city': 'Alexandria', 'state': 'Alexandria Governorate', 'country': 'Egypt', 'facility': 'Faculty of Nursing, Alexandria University', 'geoPoint': {'lat': 31.20176, 'lon': 29.91582}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'Individual participant data will not be shared to protect the privacy and confidentiality of the participating nurses'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Alexandria University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Lecturer of Medical-Surgical Nursing', 'investigatorFullName': 'Mohamed Fakhry Ahmed Salem', 'investigatorAffiliation': 'Alexandria University'}}}}