Systematic bibliometric review of artificial intelligence in human resource development: insights for HRD researchers, practitioners and policymakers

• Published in: EUROPEAN JOURNAL OF TRAINING AND DEVELOPMENT
• Main Authors: Hamouche, Salima; Rofa, Norffadhillah; Parent-Lamarche, Annick
• Format: Review; Early Access
• Language: English
• Published: EMERALD GROUP PUBLISHING LTD 2023
• Subjects: Business & Economics
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001129375400001
• ISSN: 2046-9012; 2046-9020
• DOI: 10.1108/EJTD-10-2023-0152

PurposeArtificial intelligence (AI) is a significant game changer in human resource development (HRD). The launch of ChatGPT has accelerated its progress and amplified its impact on organizations and employees. This study aims to review and examine literature on AI in HRD, using a bibliometric approach.Design/methodology/approachThis study is a bibliometric review. Scopus was used to identify studies in the field. In total, 236 papers published in the past 10 years were examined using the VOSviewer program.FindingsThe obtained results showed that most cited documents and authors are mainly from computer sciences, emphasizing machine learning over human learning. While it was expected that HRD authors and studies would have a more substantial presence, the lesser prominence suggests several interesting avenues for explorations.Practical implicationsThis study provides insights and recommendations for researchers, managers, HRD practitioners and policymakers. Prioritizing the development of both humans and machines becomes crucial, as an exclusive focus on machines may pose a risk to the sustainability of employees' skills and long-term career prospects.Originality/valueThere is a dearth of bibliometric studies examining AI in HRD. Hence, this study proposes a relatively unexplored approach to examine this topic. It provides a visual and structured overview of this topic. Also, it highlights areas of research concentration and areas that are overlooked. Shedding light on the presence of more research originating from computer sciences and focusing on machine learning over human learning represent an important contribution of this study, which may foster interdisciplinary collaboration with experts from diverse fields, broadening the scope of research on technologies and learning in workplaces.