Work-Integrated Learning in an AI-Driven Era: A Bibliometric Review of Research Themes and Emerging Trends
DOI:
https://doi.org/10.38140/obp5-2026-01Keywords:
Work-Integrated learning, artificial intelligence, higher education, bibliometrics, text mining, thematic analysisAbstract
As artificial intelligence (AI) transforms the nature of learning and work, higher education faces an urgent challenge. The pertinent question is not whether AI will reshape Work-Integrated Learning (WIL), but rather how institutions can reimagine it for an AI-driven workplace. This reality has incited extensive academic interest, resulting in a global surge of WIL scholarship over the past decade. However, research at the intersection of WIL and AI lacks a coherent synthesis, leaving critical gaps in understanding how intelligent technologies redefine WIL. This chapter addresses this gap by presenting one of the first bibliometric mappings of WIL scholarship in AI-driven contexts. Drawing on 4,975 records from the Web of Science (2015-2025), the chapter examines publication trends, prolific contributors, citation impact, and thematic evolution. The findings reveal an inflection point post-2020, with an increase in publications signalling AI’s role as a disruptive catalyst. Thematic analysis indicates a shift from traditional models of WIL practices towards a new paradigm centred on generative AI, virtual internships, and intelligent mentoring. While confirming the continued dominance of traditional WIL research hubs, the analysis highlights the rising influence of East Asian scholarship and a persistent underrepresentation of the Global South. The chapter identifies the convergence of WIL with educational technology research as forming an essential interdisciplinary core. This review underscores the necessity for future research to interrogate AI-integrated WIL models across diverse contexts. From a practical perspective, the findings advocate for the establishment of pedagogical and policy guidelines to support the integration of AI in WIL ecosystems.
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