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Updated: Jan 13, 2026

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Desarrollo de un Agente de Educación en Investigación de Enfermería Utilizando Grafos de Conocimiento y Modelos de

Yingchun Zeng1, Hongxia Xie, Xiaofeng Zhou

  • 1Author Affiliations: Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (Drs Zeng, Jiang, and Lau); School of Computers and Computing Sciences, Hangzhou City University, Hangzhou, China (Ms Xie and Dr Xu); and School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China (Mr Zhou).

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Un nuevo Agente de Educación en Investigación de Enfermería, que combina grafos de conocimiento (KG) y modelos de lenguaje grandes (LLM), muestra una gran promesa para mejorar las habilidades de investigación y estadísticas de los estudiantes de enfermería. Los educadores lo encontraron pedagógicamente sólido y efectivo.

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agente de IAactividades profesionales confiablesgrafo de conocimientomodelo de lenguaje grandeinvestigación de enfermería

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Área de la Ciencia:

  • Educación en Enfermería
  • Inteligencia Artificial en la Atención Médica
  • Tecnología Educativa

Sus antecedentes:

  • Los grafos de conocimiento (KG) y los modelos de lenguaje grandes (LLM) ofrecen un potencial significativo para avanzar en la educación de enfermería, particularmente en la metodología de investigación y la alfabetización estadística.
  • Un estudio de prueba de concepto desarrolló un agente especializado para ayudar a los estudiantes de enfermería a comprender los diseños de investigación y los conceptos estadísticos.

Objetivo del estudio:

  • Evaluar la viabilidad y la idoneidad pedagógica del agente desarrollado en un entorno de educación de enfermería.
  • Evaluar el rendimiento de la inteligencia artificial (IA) del agente utilizando herramientas de evaluación establecidas.

Principales métodos:

  • El agente integró KG estructurados de conocimiento de investigación de enfermería con LLM para proporcionar respuestas interactivas en lenguaje natural.
  • Diez educadores de enfermería evaluaron el agente utilizando la Escala de Evaluación de Ajuste Pedagógico y la Escala de Evaluación de Rendimiento de IA.

Principales resultados:

  • Los educadores informaron altas calificaciones para el ajuste pedagógico (M=4.20, DE=0.63) y el rendimiento de la IA (M=4.10, DE=0.56).
  • La retroalimentación positiva destacó la relevancia clínica del agente, la precisión y su capacidad para fomentar habilidades de pensamiento crítico entre los estudiantes.
  • Se consideró factible la integración del agente en los planes de estudio de enfermería.

Conclusiones:

  • Los agentes que integran KG y LLM demuestran un potencial considerable para mejorar la educación en investigación de enfermería.
  • Se recomiendan investigaciones adicionales y ensayos a gran escala para explorar completamente las capacidades e impacto de estos agentes integrados.