Exploring the impact of artificial intelligence on business talent development in higher education:A systematic literature review and research agenda

Abstract

Against the strategic backdrop of the digital and intelligent transformation of global higher education, the emerging cluster of technologies with artificial intelligence (AI) at its core is fundamentally reshaping the operational logic and value ecosystem of business education systems. To comprehensively understand the current research landscape and progress of AI and business talent development, this study conducts a systematic literature review, retrieving 192 research articles published between 2015 and 2024 from the Scopus and Web of Science databases. Based on descriptive statistics and CiteSpace bibliometric analysis, this study summarizes the major progress and key findings of the past decade across four domains of business talent development in higher education. The study further provides practical implications for business school administrators and faculty, highlighting insufficient attention to macro-level issues such as core AI competencies, curriculum restructuring, and institutional resource support. It also notes the lack of in-depth reflection on the mechanisms through which AI is embedded in business talent development. In addition, through further analysis of theoretical frameworks and research methods, this study suggests that future academic research should explore emerging frontier topics such as artificial general intelligence and quantum algorithms, promote interdisciplinary integration of business education with neuroscience, social sciences, and environmental science, place greater emphasis on longitudinal research, and adopt research paradigms driven by both data and mechanisms. • Through Keyword co-occurrence analysis revealed that "big data analytics", "business analytics", "curriculum design", "decision making" and "artificial intelligence" occupy central positions. • Identified and constructed a knowledge map of research, namely the overall framework of the four major thematic modules presented in Fig. 9 , and elucidated the internal logic of the research framework. • Notes shallow interdisciplinarity, lacks holistic design focus, limited theory innovation, and weak longitudinal extension. • Proposes an innovation-ecosystem, data/mechanism-driven approach for AI-shaped talent, curricula redesign, and resource support.

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