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Evaluating Eye Tracking Technology in Nursing Education: A Scoping Review on Medication Administration Training
Jiranut Chaichana1, Rob Eley2, Chris Watling3
1School of Nursing & Midwifery, University of Southern Queensland, 11 Salisbury Road, Ipswich, QLD 4305, Australia.
Nursing Reports (Pavia, Italy)
|June 25, 2025
Summary
Eye tracking technology enhances nursing education by analyzing visual focus during drug administration training. This technology reveals differences between novice and expert nurses, improving attention and situational awareness, though technical barriers remain.
Area of Science:
- Nursing Education
- Human-Computer Interaction
- Medical Simulation
Background:
- Eye tracking technology analyzes eye movements to reduce medication errors in nursing.
- It offers insights into student learning, cognitive load, and instructional design for personalized education.
- Despite challenges like technical expertise and cost, it provides real-time feedback to improve teaching and learning.
Purpose of the Study:
- To explore the evidence on eye tracking technology's application in training nursing students for drug administration.
- To identify how eye tracking can enhance medication administration skills.
- To assess the effectiveness of eye tracking in nursing education.
Main Methods:
- Systematic review of peer-reviewed empirical papers (qualitative and quantitative) and reports.
- Searches conducted across nine databases, including English-language articles.
- Risk of bias assessed using JBI SUMARI; results synthesized narratively.
Main Results:
- Ten studies focusing on medication training were identified from 739 initial studies.
- Eye tracking revealed differences in visual focus between novice and expert nurses.
- Specific interventions using eye tracking improved attention and concentration in nursing students.
Conclusions:
- Eye tracking technology shows significant potential for nursing education, particularly in improving attention and situational awareness for medication administration.
- Limitations include small sample sizes, technical challenges, and a lack of long-term data.
- Future research should focus on larger samples, diverse populations, and extended follow-up periods to address current limitations.
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