Integrating Artificial Intelligence into Human Resource Development in Higher Education: Opportunities and Challenges
Integrating Artificial Intelligence into Human Resource Development in Higher Education: Opportunities and Challenges

Summary/Abstract: Purpose – Artificial intelligence (AI) is increasingly integrated into higher education institutions (HEIs), yet its implications for human resource development (HRD) systems remain insufficiently synthesized. This paper addresses this gap by examining how AI-enabled approaches are applied in HRD in HEIs and the opportunities and challenges that emerge from their adoption.Aims(s) – The primary aim of this study is to synthesize evidence on the integration of AI into HRD in higher education. Specifically, the study aims to (1) identify AI-supported HRD opportunities and categorize them by HRD domains and outcome levels, (2) examine reported risks and challenges, and (3) identify conditions that enable responsible AI implementation in HRD systems.Design/methodology/approach – A systematic literature review following the PRISMA 2020 framework was conducted. Searches in Scopus and Web of Science identified peer-reviewed empirical studies published between 2018 and 2026. After screening and eligibility assessment, seven studies were included in the qualitative synthesis, and findings were analyzed using structured narrative synthesis.It should be noted that the final synthesis was based on seven studies, reflecting the nascent state of this evidence base; conclusions should therefore be interpreted as indicative rather than definitive.Findings – Results indicate that AI-supported HRD opportunities primarily relate to AI literacy development, generative AI-supported performance support for faculty development, and institution-wide learning through governance and guidelines. Key challenges include skills gaps, reliability concerns, ethical and privacy issues, and trust-related adoption barriers.Limitations of the study – The study is limited by a small, emerging evidence base and the predominance of perception-based or early-stage studies. Future research should examine long-term HRD outcomes and multi-level organizational impacts.Practical implications – HEIs should approach AI adoption as a capability-building and governance initiative, emphasizing structured AI literacy programs, differentiated development pathways, and responsible-use guidelines.Originality/value – This study provides one of the first systematic syntheses of AI-enabled HRD practices in higher education and offers a framework linking AI adoption with HRD domains, outcomes, and implementation conditions.

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