Artificial Intelligence Adoption in Human Resource Practices: A Review of Literature Across Educational and Corporate Sectors

Abstract

Artificial intelligence (AI) has become a transformative force in human resource management (HRM), reshaping practices across corporate and educational contexts. Despite a growing body of empirical work, no prior systematic review has directly compared AI adoption in HRM across these two distinct yet converging sectors. Guided by the SPAR-4-SLR search protocol, reported according to PRISMA 2020 standards, and grounded in the 5W+H framework for research question formulation, this systematic review synthesises 62 peer-reviewed studies published in Scopus and Web of Science between 2015 and 2024. How results appear follows the TCCM blueprint, yet trust checks rely on five distinct POWER signs. Four questions steer the research - how AI steps into HR across areas comes first; what benefits and snags tag along with AI-HR tools shows up next; differences in how schools use AI-HR compared to businesses takes third place; last is where researchers might look later. Findings point to faster HR work - hiring times drop by about 75 percent, profits climb close to 20 percent - even as intelligent tutors boost learning outcomes between 20 and 30 percent. Still, problems linger - biased systems, shaky data protection, murky decision paths, resistance among workers. Viewed through TCCM lenses, hidden gaps emerge, hinting strongly at future efforts cantered on fair standards, extended cross-sector studies, experimental models, and places usually ignored.