Review Gold Open Access 2025

Strategic Human Resource Development for Emerging Technologies: A Scoping Review in ICT, Robotics, Data Science, and CPS

IEEE Access
Journal · Vol. 13 · pp. 133994-134013
Abstract

The rapid advancement of ICT, robotics, data science, and cyber-physical systems (CPS) has intensified the need for agile, future-ready human resource development (HRD) strategies. This scoping review explores current HRD practices, challenges, and opportunities across these technological domains. Guided by the PRISMA-ScR framework, 791 records were initially retrieved from four databases, with 43 peer-reviewed publications ultimately included following a rigorous screening process. Thematic analysis revealed a strong emphasis on digital and AI literacy, interdisciplinary training, and the adoption of immersive technologies such as virtual reality and AI-driven competency tracking systems. Persistent gaps, including misalignment between academic training and industry needs, a lack of scalable HRD models, and underdeveloped policy frameworks. Statistical comparisons revealed no significant differences across fields, indicating the cross-cutting relevance of the identified strategies. The findings underscore the need for structured, empirically grounded, and industry-integrated HRD approaches to prepare the workforce for the demands of the Fourth Industrial Revolution. © IEEE. 2013 IEEE.

Keywords

Author Keywords

Workforce development Digital literacy blended learning Scoping review ICT Programming skills data science AI competency AI-driven training CPS cyber-physical system robotics

Index Keywords

E-learning Engineering education Personnel training Workforce development Digital literacies Personnel scoping review Industry 4.0 Virtual reality Blended learning Programming skills ICT Embedded systems AI competency AI-driven training Cybe-physical systems Cyber-physical systems Robotics
Author Affiliations
Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, Japan
Department of Artificial Intelligence and Data Analytics, Hochschule Neu-Ulm, Bavaria, Germany
Funding & Acknowledgements
Grant: JPFR24010102
This work was supported by the Commissioned Research fund provided by Fukushima Institute for Research, Education and Innovation (F-REI) under Grant JPFR24010102.
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