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Artificial intelligence in human resource development: An umbrella review protocol.
Yoo, Sangok; Nimon, Kim; Patole, Sanket Ramchandra.
Afiliación
  • Yoo S; Human Resource Development, The University of Texas at Tyler, Tyler, Texas, United States of America.
  • Nimon K; Human Resource Development, The University of Texas at Tyler, Tyler, Texas, United States of America.
  • Patole SR; Human Resource Development, The University of Texas at Tyler, Tyler, Texas, United States of America.
PLoS One ; 19(9): e0310125, 2024.
Article en En | MEDLINE | ID: mdl-39250462
ABSTRACT
The recent surge in artificial intelligence (AI) has significantly transformed work dynamics, particularly in human resource development (HRD) and related domains. Scholars, recognizing the significant potential of AI in HRD functions and processes, have contributed to the growing body of literature reviews on AI in HRD and related domains. Despite the valuable insights provided by these individual reviews, the challenge of collectively interpreting them within the HRD domain remains unresolved. This protocol outlines the methodology for an umbrella review aiming to systematically synthesize existing reviews on AI in HRD. The review seeks to address key research questions regarding AI's contributions to HRD functions and processes, as well as the opportunities and threats associated with its implementation by employing a technology-aided systematic approach. The coding framework will be used to synthesize the contents of the selected systematic reviews such as their search strategies, data synthesis approaches, and HRD-related findings. The results of this umbrella review are expected to provide insights for HRD scholars and practitioners, promoting continuous improvement in AI-driven HRD initiatives. This protocol is preregistered on the Open Science Framework (https//doi.org/10.17605/OSF.IO/Z8NM6) on May 27, 2024.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article