Nursing EducationNCT07448259
Artificial Intelligence-Assisted Learning for Nursing Drug Calculation
A study testing Artificial Intelligence-Assisted Drug Calculation Platform and Traditional Nursing Education in people with Nursing Education and Clinical Competence, run by Alexandria University at 1 site in Egypt.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Nursing Education, Clinical Competence, Medication Errors
- What is being tested?
- Artificial Intelligence-Assisted Drug Calculation Platform and Traditional Nursing Education
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 56 participants
- Where
- 1 siteEgypt
- Study dates
- 22 Sep 2025to 30 Dec 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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)
Trial locations and contacts
1 site in 1 country.
Egypt1 site
Faculty of Nursing, Alexandria University
Alexandria, Alexandria Governorate, 2500
About this study
Official title: Adaptive AI-Based Mobile Simulation for Drug Calculation Competency in Nurses: A Mixed-Methods RCT
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.
Read more
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.
Read the detailed description
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.
- Conditions
- Nursing Education, Clinical Competence, Medication Errors
- Being tested
- Artificial Intelligence-Assisted Drug Calculation Platform, Traditional Nursing Education
- Keywords
- Artificial Intelligence, Drug Calculation, Nursing Knowledge, Clinical Decision-Making, Self-Efficacy, Scenario-Based Learning
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence-Assisted Drug Calculation PlatformDevice
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.
Traditional Nursing EducationOther
Standard classroom-based instruction consists of theoretical lectures and paper-based practice focusing on medication dosage calculations.
| Study group | Role | What they receive |
|---|---|---|
| Artificial Intelligence-Assisted Learning Group | Experimental | Use an Artificial Intelligence-assisted platform providing scenario-based learning and real-time feedback for drug calculations. Receives: Device: Artificial Intelligence-Assisted Drug Calculation Platform |
| Traditional Learning Group | Experimental | Participants receive the standard curriculum through traditional lectures and paper-based practice sessions. Receives: Other: Traditional Nursing Education |
What is being measured
The primary outcome is the main question the study is designed to answer.
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%).
Measured: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
Secondary outcomes (3)
Nurses' Drug Calculation Decision-Making Scale
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).
Minimum and Maximum Values: The total score ranges from a minimum of 8 to a maximum of 40.
Interpretation: Higher scores indicate a better outcome (greater clinical competence and safer decision-making).
High (30-40): High competence.
Moderate (19-29): Moderate ability; requires supervision.
Low (8-18): Poor confidence and judgment.
Measured: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
General Self-Efficacy Scale
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).
Scale Range: The total score ranges from a minimum of 10 to a maximum of 40.
Interpretation: Higher scores indicate a better outcome (stronger perceived competence and higher self-efficacy).
High (31-40): Strong perceived competence and self-belief.
Moderate (21-30): Moderate confidence in managing demands.
Low (10-20): Low belief in ability to cope with challenges.
Measured: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
Nurses' Perception and Satisfaction with Artificial Intelligence-Assisted Learning (Qualitative)
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.
Method of Analysis: Results will be analyzed using Thematic Analysis to identify recurring patterns and themes.
Unit of Measure: This is a qualitative outcome; results will be reported as narrative themes (for example: "Improved Calculation Confidence" or "User Interface Satisfaction").
Measured: 2 weeks after the completion of the AI-assisted training
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Mar 2026
- Study start22 Sep 2025
- Primary completion30 Dec 2025
- Study completion30 Dec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Nursing EducationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- 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.
- Masking
- None (open label)
- Enrolment
- 56 participants (actual)
Sponsor and investigators
- Lead sponsor
- Alexandria University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Mohamed Fakhry Ahmed Salem, Alexandria University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07448259
- Sponsor's protocol ID
- AU-20-6-393
- Status last verified
- Feb 2026
- First submitted
- 18 Feb 2026
- Last updated
- 1 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared to protect the privacy and confidentiality of the participating nurses
Publications
Salem MFA, Sharaf AK, Abdelhafeez Abdelmonseif Younis A. Adaptive AI-based mobile simulation for drug calculation competency in nurses: a mixed-methods RCT. BMC Nurs. 2026 Sep 8;25(1):828. doi: 10.1186/s12912-026-05313-4.
PubMed 42717352
Study at a glance
From the registry record, last updated by the sponsor on 1 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Adaptive AI-Based Mobile Simulation for Drug Calculation Competency in Nurses: A Mixed-Methods RCT
- ClinicalTrials.gov ID
- NCT07448259
- Acronym / sponsor ID
- AU-20-6-393
- Conditions
- Nursing Education, Clinical Competence, Medication Errors
- Keywords
- Artificial Intelligence, Drug Calculation, Nursing Knowledge, Clinical Decision-Making, Self-Efficacy, Scenario-Based Learning
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 56 participants (actual)
- Lead sponsor
- Alexandria UniversityUniversity, hospital or other
- Study start
- 22 Sep 2025 (actual)
- Primary completion
- 30 Dec 2025 (actual)
- Study completion
- 30 Dec 2025 (actual)
- First posted
- 4 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- 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.
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence-Assisted Learning Group | Experimental | Device: Artificial Intelligence-Assisted Drug Calculation PlatformUse an Artificial Intelligence-assisted platform providing scenario-based learning and real-time feedback for drug calculations. |
| Traditional Learning Group | Experimental | Other: Traditional Nursing EducationParticipants receive the standard curriculum through traditional lectures and paper-based practice sessions. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (1 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence-Assisted Drug Calculation PlatformAn 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. | Device | Artificial Intelligence-Assisted Learning Group |
| Traditional Nursing EducationStandard classroom-based instruction consists of theoretical lectures and paper-based practice focusing on medication dosage calculations. | Other | Traditional Learning Group |
Primary outcome
Nurses' Knowledge of Drug Calculation
Time frame: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
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%).
3 secondary outcomes
Nurses' Drug Calculation Decision-Making Scale
Time frame: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
General Self-Efficacy Scale
Time frame: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
Nurses' Perception and Satisfaction with Artificial Intelligence-Assisted Learning (Qualitative)
Time frame: 2 weeks after the completion of the AI-assisted training
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Apr 2026.
- Derived from this study: PubMed 42717352Salem MFA, Sharaf AK, Abdelhafeez Abdelmonseif Younis A. Adaptive AI-based mobile simulation for drug calculation competency in nurses: a mixed-methods RCT. BMC Nurs. 2026 Sep 8;25(1):828. doi: 10.1186/s12912-026-05313-4.
- Individual participant dataSharing planned: No. Individual participant data will not be shared to protect the privacy and confidentiality of the participating nurses
Related trials for Nursing Education
Not yet recruiting
Effect of AI-Based Image Generation on Children's Ecological Footprint Awareness
Environmental Awareness, Ecological Footprint
Active, not recruiting
AI-Assisted Child Health Podcast Education for Nursing Students
Nursing Education
Not yet recruiting
AI-Driven Video Avatars vs. Customized GPTs: Professional English Learning Among Nursing Students
Artifical Intelligence, Generative Artificial Intelligence
Active, not recruiting
Enhancing Male Nursing Students' Competence in Maternity Care: A Quasi-Experimental Study in Egypt
Maternity Nursing, Obstetric Nursing
Recruiting
Effect of Solution-Focused Approach Education on Breastfeeding Avoidance and Breastfeeding Self-Efficacy in Primiparous Mothers
Breastfeeding Avoidance and Breastfeeding Self-efficacy
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence-Assisted Drug Calculation Platform and Traditional Nursing Education in people with Nursing Education and Clinical Competence, run by Alexandria University at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 1 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Nurses' Knowledge of Drug Calculation (time frame: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07448259, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Apr 2026.