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Development of the AI Literacy-Healthcare Scale for Nurses

Development and Validation of an AI Literacy–Healthcare Measurement Tool for Nurses

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0011765
Enrollment
591
Registered
2026-03-23
Start date
2026-03-30
Completion date
Unknown
Last updated
2026-03-30

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

None listed

Sponsors

Korea University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1) Focus Group Interview (n=8) Participants will be selected using purposive sampling in order to identify the components of the “AI Literacy-Healthcare” measurement scale for nurses and to confirm the clinical applicability of the derived components. The selection criteria are as follows. ? Nurses currently working in medical institutions who directly provide nursing care to patients in clinical settings ? Nurses with at least 3 years of clinical experience ? Individuals who have experience using AI-based medical devices or healthcare information systems (e.g., DeepCARS, VUNO) in clinical practice 2) Expert Panel for Content Validity (n=8) An expert panel will be formed using purposive sampling to verify the content validity of the initial items of the “AI Literacy-Healthcare” measurement scale for nurses. The selection criteria are as follows. ? Nurses in managerial positions who perform management and coordination roles for nurses in university hospitals or medical institutions ? Nursing professors with academic expertise and professional knowledge in nursing education or digital health in nursing ? Experts with experience in the development, operation, or clinical application of medical AI systems (e.g., DeepCARS, VUNO) A total of eight experts will be recruited, including three nurse managers (head nurses or team leaders) from university hospitals, three nursing professors, and two AI experts related to medical devices, to evaluate the content validity of the initial items. 3) Pilot Test (n=20) The pilot test will be conducted with nurses who have at least one year of clinical experience and are working in hospitals located in Seoul. The specific inclusion criteria are as follows. ? Nurses with at least one year of clinical experience working in hospitals located in Seoul ? Individuals who can communicate in Korean and understand and respond to the survey questionnaire 4) Survey of Nurses The main survey will be conducted among nurses with at least one year of clinical experience working in hospitals located in Seoul, and only those who understand the purpose of the study and voluntarily agree to participate will be included. ? First survey (n=222) The selection criteria are as follows. ? Nurses with at least one year of clinical experience working in hospitals located in Seoul ? Individuals who can communicate in Korean and understand and respond to the survey questionnaire ? Second survey (n=222) The selection criteria are as follows. ? Nurses with at least one year of clinical experience working in hospitals located in Seoul ? Individuals who can communicate in Korean and understand and respond to the survey questionnaire ? Third survey (n=111) The selection criteria are as follows. ? Nurses with at least one year of clinical experience working in hospitals located in Seoul ? Individuals who can communicate in Korean and understand and respond to the survey questionnaire ? Individuals who participated in the second survey of this study and agreed to participate again

Exclusion criteria

Exclusion criteria: 1) Focus Group Interview (n=8) The exclusion criteria are as follows. ? Individuals who are not currently working in clinical settings ? Individuals with less than 3 years of clinical experience ? Individuals with no experience using AI-related medical devices or healthcare information systems 2) Expert Panel for Content Validity (n=8) The exclusion criteria are as follows. ? Individuals with less than 5 years of experience in the relevant field ? Individuals with insufficient practical experience in medical or AI-related fields 3) Pilot Test (n=20) Individuals who meet any of the following conditions will be excluded from the study. ? Nurses performing administrative duties who do not directly participate in patient care ? Individuals who are unable to respond to the online survey ? Individuals with cognitive impairment or mental illness 4) Survey of Nurses ? First survey (n=222) Individuals who meet any of the following conditions will be excluded from the study. ? Nurses performing administrative duties who do not directly participate in patient care ? Individuals who are unable to respond to the online survey ? Individuals with cognitive impairment or mental illness ? Individuals who participated in the pilot test ? Second survey (n=222) Individuals who meet any of the following conditions will be excluded from the study. ? Nurses performing administrative duties who do not directly participate in patient care ? Individuals who are unable to respond to the online survey ? Individuals with cognitive impairment or mental illness ? Individuals who participated in the first survey of this study ? Third survey (n=111) Individuals who meet any of the following conditions will be excluded from the study. ? Nurses performing administrative duties who do not directly participate in patient care ? Individuals who are unable to respond to the online survey ? Individuals with cognitive impairment or mental illness

Design outcomes

Primary

MeasureTime frame
Content validity of the Nurses’ AI Literacy Scale (NAILS);Construct validity and convergent validity of the Nurses’ AI Literacy Scale (NAILS)

Secondary

MeasureTime frame
Internal consistency reliability (Cronbach’s a) of the Nurses’ AI Literacy Scale (NAILS)

Countries

Korea, Republic of

Contacts

Public ContactWon-Oak Oh

Korea University

wooh@korea.ac.kr+82-2-3290-4034

Outcome results

None listed

Source: CRIS (via WHO ICTRP) · Data processed: Apr 4, 2026