The growing use of artificial intelligence (AI) is transforming human resources management in all industries, including higher education institutions, where the ability to manage talent effectively is crucial for academic quality and institutional stability. Notwithstanding increasing recognition of the potential for AI in HR, research to date has been fragmented and focused on functional or technical aspects often leaving ethical governance and socio-technical fit in academic settings underexplored. It is this research gap that the current study aims to fill through an integrated and comprehensive conceptual model of AI-learning for talent management in higher education. Taking a qualitative narrative review method, the research integrates interdisciplinary literature from talent management to SHRM as well as higher education and AI governance. Grounded in the resource-based view, human capital theory, and socio-technical systems theory, this article investigates how AI is conceptually situated throughout the ITR of attraction, development, and retention. The synthesis underscores the strategic promise of AI-based talent management in enabling data-driven decision-making and indiv
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