This systematic review synthesizes the existing literature on the impact of artificial intelligence (AI) and automation on Human Resource Development (HRD) practices and outcomes. The study explores how AI and automation affect HRD, highlighting specific HRD processes affected and their influence on outcomes. A comprehensive search was conducted across academic databases, HRD journals, and conference proceedings, resulting in a selection of relevant studies. The findings were analyzed through a narrative synthesis, with subgroup analyses based on specific HRD processes. The review provides insights into AI and automation implications for HRD researchers and practitioners. It also identifies research gaps and future directions.

This paper argues that national human resource development (NHRD) practices, policy imperatives and challenges in sub-Saharan Africa (SSA) are linked to colonial experiences and coloniality. In doing this, the essay provides a brief overview of the intersections between postcolonialism and NHRD theory and practice, arguing that postcolonial insights can help overcome the ahistorical nature that characterizes much NHRD theorizing and also its reification. By demonstrating how current challenges to education and skills development in SSA are linked to colonial heritage and coloniality, the paper seeks to stimulate HRD theorists and practitioners to think differently and work beyond dominant neoliberal constructs and taken-for-granted assumptions about NHRD in postcolonial contexts. The paper also argues for the decolonization of NHRD and proposes some ideas about how knowledge about NHRD theory and practices might be made global and inclusive, thereby contributing to a tradition of paradigmatic critique in HRD theorizing.

In the age of artificial intelligence (AI) and automation, understanding the factors that contribute to successful upskilling and reskilling is crucial for workforce adaptability. Research on AI and automation has shown that they affect the workforce, but there is still a gap in understanding what factors contribute to successful upskilling and reskilling initiatives, especially from an organizational perspective. This study employs fuzzy-set qualitative comparative analysis (fsQCA) to analyze these factors and identify effective combinations for promoting upskilling and reskilling. By examining complex causal relationships and incorporating case studies, this article aims to enhance the understanding of upskilling initiatives in the AI and automation era. The findings provide insights into key factors driving successful upskilling and reskilling and offer implications for organizations seeking to improve their initiatives, ultimately promoting workforce adaptability and competitiveness.

As artificial intelligence (AI) technologies become increasingly embedded in higher education, from adaptive learning platforms to algorithmic assessment tools, there is a growing imperative to examine the ethical and governance implications of such systems. While AI holds significant promise for improving efficiency and personalisation in education, its deployment also raises concerns around surveillance, algorithmic bias, data privacy, and equity. This article critically explores how transparent AI governance frameworks can be systematically integrated into academic curricula. It argues that ethical oversight and trust must be embedded not merely at the institutional policy level but within the design, delivery, and evaluation of educational programmes themselves. Drawing on interdisciplinary insights from digital governance, education policy, and data ethics, the paper develops a conceptual model for curriculum reform, articulates the ethical dimensions of AI in education, and presents illustrative case studies of institutions that have begun embedding such oversight into their teaching practices. By aligning curriculum design with emerging ethical principles, this study proposes that universities and higher education institutions can develop graduates who are not only technically proficient but ethically literate, capable of navigating and interrogating the socio-technical dimensions of AI in their future professions.

Inaugural editorial of the African Journal of Education, Skills and Innovation.