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The Effect of AI-Assisted Nursing Process Training on Nursing Process Competence, Perception and Attitudes Towards Artificial Intelligence in Nurses: A Randomized Controlled Study

24 de mayo de 2026 actualizado por: Seher Dağlı, University of Yalova

Hemşirelerde Yapay Zeka Destekli Hemşirelik Süreci Eğitiminin Hemşirelik Süreci Yetkinliğine, Yapay Zeka Algı ve Tutumuna Etkisi: Randomize Kontrollü Bir Çalışma

This study aims to determine how applied artificial intelligence (AI) training affects nurses' ability to manage the nursing process and their perceptions and attitudes toward AI technology

  • The nursing process is a scientific, six-stage approach used by nurses to identify patient needs and provide holistic care

The research is a randomized controlled trial involving 78 nurses at Yalova Education and Research Hospital

. Participants will be split into two groups: Both groups will receive standard theoretical training on the nursing process

. The intervention group will receive additional specialized training on using AI tools (such as ChatGPT and Deepseek) to help create nursing care plans through practical case studies

. Nurses' skills and views will be measured using specific scales before the training and one month after the intervention to evaluate the training's effectiveness

  • This study is expected to provide valuable insights into how AI can support clinical decision-making and help healthcare providers adapt to new technologies
  • The research has been approved by the Yalova University Ethics Committee (Protocol 2026/183) and will be conducted between May and December 2026

Descripción general del estudio

Descripción detallada

This randomized controlled, quasi-experimental study is designed to evaluate the impact of an applied artificial intelligence (AI)-supported nursing process training program on nurses' professional competence and their attitudes toward AI technology. The primary objective is to determine how the integration of AI tools into clinical decision-making affects nursing process efficiency and perception among healthcare professionals

. Methodology and Randomization: The study population consists of 414 nurses working at Yalova Education and Research Hospital

  • Based on power analysis (power=0.95, alpha=0.05), a total of 78 nurses will be recruited and randomized into two groups: an intervention group (n=39) and a control group (n=39)
  • Randomization will be conducted following the collection of baseline (pre-test) data

Intervention Protocol:

Phase 1 (Common Foundation): Both the intervention and control groups will receive a "Theoretical Training on the Nursing Process" to ensure baseline knowledge standardization . Phase 2 (AI Training - Intervention Group only): The intervention group will receive "AI-Supported Nursing Process Theoretical Training," which includes technical guidance on using AI tools (such as ChatGPT and Deepseek) for clinical care

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. Phase 3 (Practical Application - Intervention Group only): Participants will engage in hands-on workshops using structured clinical cases. They will apply AI tools to generate care plans based on NANDA-I, NIC, and NOC taxonomies

  • This phase includes structured debriefing and feedback sessions led by the researcher

The control group will only receive the standard theoretical nursing process education and will not have access to the AI training modules until the study is completed .

Data Collection and Assessment: Data will be collected using three instruments:

The Nurse Information Form (demographics and AI usage habits) . The Nursing Process Competence Scale (to measure clinical workflow skills)

. The Artificial Intelligence Perception and Attitude Scale (YAZAT-24) (to measure attitudes toward AI integration)

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. Measurements will be conducted at two time points: baseline (pre-test) and one month following the intervention (post-test) to assess long-term retention and impact

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. Statistical Analysis: Data analysis will be performed using SPSS 22.0. Normality will be assessed via the Kolmogorov-Smirnov test. Analysis will include descriptive statistics, independent samples t-test or Mann-Whitney U for group comparisons, and Repeated Measures ANOVA or Friedman tests for within-group changes over time

Tipo de estudio

Intervencionista

Inscripción (Estimado)

78

Fase

  • No aplica

Contactos y Ubicaciones

Esta sección proporciona los datos de contacto de quienes realizan el estudio e información sobre dónde se lleva a cabo este estudio.

Estudio Contacto

  • Nombre: Seher Gul Yavas, RN
  • Número de teléfono: +90 541 685 8806
  • Correo electrónico: seher.daglii@gmail.com

Copia de seguridad de contactos de estudio

  • Nombre: Seyda can, Assoc. Prof. Dr.
  • Número de teléfono: +90 536 685 0312
  • Correo electrónico: seyda.cann@hotmail.com

Ubicaciones de estudio

    • Yalova
      • Yalova, Yalova, Turquía (Türkiye)
        • Yalova Training and Research Hospital
        • Contacto:

Criterios de participación

Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.

Criterio de elegibilidad

Edades elegibles para estudiar

  • Adulto
  • Adulto Mayor

Acepta Voluntarios Saludables

Sí

Descripción

Inclusion Criteria:

  • Volunteering to participate in the study.
  • Working actively as a nurse in the specified institution (Yalova Training and Research Hospital).
  • Not having previously used artificial intelligence in the nursing process.

Exclusion Criteria:

  • Refusing to participate in the study.
  • Having previously used artificial intelligence in the nursing process. Submitting incomplete data collection forms.
  • Requesting to withdraw from the study.

Plan de estudios

Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.

¿Cómo está diseñado el estudio?

Detalles de diseño

  • Propósito principal: Investigación de servicios de salud
  • Asignación: Aleatorizado
  • Modelo Intervencionista: Asignación paralela
  • Enmascaramiento: Ninguno (etiqueta abierta)

Armas e Intervenciones

Grupo de participantes/brazo
Intervención / Tratamiento
Experimental: Intervention Group
Participants will receive a standard theoretical education session on the nursing process. Following this, they will receive an applied artificial intelligence-supported nursing process training and engage in case study practices using AI tools.
Participants will receive theoretical education on the artificial intelligence-supported nursing process and engage in applied case studies using AI tools in small groups.
Participants will receive a standard theoretical education session on the nursing process.
Comparador activo: Control Group
Participants will receive only the standard theoretical education session on the nursing process. They will not receive the artificial intelligence-supported training or case study practices during the study period.
Participants will receive a standard theoretical education session on the nursing process.

¿Qué mide el estudio?

Medidas de resultado primarias

Medida de resultado
Medida Descripción
Periodo de tiempo
Change in Nursing Process Competence
Periodo de tiempo: Baseline (pre-test) and 1 month after the intervention (post-test)
This outcome is measured using the Nursing Process Competence Scale. The scale consists of 24 items and 5 sub-dimensions evaluated on a 5-point Likert scale. The average score ranges from 1 to 5, and higher scores indicate higher nursing process competence
Baseline (pre-test) and 1 month after the intervention (post-test)

Medidas de resultado secundarias

Medida de resultado
Medida Descripción
Periodo de tiempo
Change in Artificial Intelligence Perception and Attitude
Periodo de tiempo: Baseline (pre-test) and 1 month after the intervention (post-test).
This outcome is measured using the Artificial Intelligence Perception and Attitude Scale (YAZAT-24). The scale consists of 24 items and 4 sub-dimensions evaluated on a 7-point Likert scale. Higher total scores indicate more positive perceptions and attitudes towards artificial intelligence.
Baseline (pre-test) and 1 month after the intervention (post-test).

Colaboradores e Investigadores

Aquí es donde encontrará personas y organizaciones involucradas en este estudio.

Patrocinador

Fechas de registro del estudio

Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados ​​por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.

Fechas importantes del estudio

Inicio del estudio (Estimado)

1 de junio de 2026

Finalización primaria (Estimado)

31 de diciembre de 2026

Finalización del estudio (Estimado)

31 de diciembre de 2026

Fechas de registro del estudio

Enviado por primera vez

24 de mayo de 2026

Primero enviado que cumplió con los criterios de control de calidad

24 de mayo de 2026

Publicado por primera vez (Actual)

1 de junio de 2026

Actualizaciones de registros de estudio

Última actualización publicada (Actual)

1 de junio de 2026

Última actualización enviada que cumplió con los criterios de control de calidad

24 de mayo de 2026

Última verificación

1 de mayo de 2026

Más información

Términos relacionados con este estudio

Términos MeSH relevantes adicionales

Otros números de identificación del estudio

  • 2026/183

Plan de datos de participantes individuales (IPD)

¿Planea compartir datos de participantes individuales (IPD)?

NO

Información sobre medicamentos y dispositivos, documentos del estudio

Estudia un producto farmacéutico regulado por la FDA de EE. UU.

No

Estudia un producto de dispositivo regulado por la FDA de EE. UU.

No

Esta información se obtuvo directamente del sitio web clinicaltrials.gov sin cambios. Si tiene alguna solicitud para cambiar, eliminar o actualizar los detalles de su estudio, comuníquese con register@clinicaltrials.gov. Tan pronto como se implemente un cambio en clinicaltrials.gov, también se actualizará automáticamente en nuestro sitio web. .

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