AI-Enhanced Career Guidance Systems as Intelligent Labour-Market Intermediaries for Reducing Skills Mismatch: A Critical Integrative Review and Conceptual Framework
DOI:
https://doi.org/10.70670/sra.v3i2.2468Keywords:
Artificial Intelligence, Career Guidance; Skills Mismatch, Labour-Market Intelligence, Workforce Development; Human–AI CollaborationAbstract
The persistent skills mismatch (the gap between the skills of the workforce and the skills needed to fulfil job requirements) has adverse effects on productivity, job mobility and the inclusiveness of technological transition. While there is a conceptual disconnection and potential ethical issues in how AI can support career guidance, its potential benefits for timely and personalized career guidance are promising. This paper reviews the concept of AI enhanced career guidance as a smart labour-market intermediary and proposes an integrated framework to understand the interactions between the emergence of AI and the identification, anticipation, and mitigation of skills mismatch. A critical integrative review that combines literature published primarily from 2015 to 2025 from the literature sources of labour economics, career development, educational technology, information systems, and responsible AI, while maintaining the basic theoretical literature sources. Thematic synthesis and mechanism-oriented synthesis were used to analyse evidence. AI can boost competency profiling, skills intelligence, occupational scenario analysis, personalised learning pathways, and learner/counsellor/institution/employer/workforce agency coordination. It is most useful when it helps to lessen information asymmetry and can facilitate adaptive career agency instead of providing prescriptive job advice. Data quality, digital accessibility, AI literacy, institutional preparedness, labour-market structure and governance moderate effectiveness. Biases, privacy violations, hallucination, automation bias, opaque recommendations, and unequal infrastructure can perpetuate or exacerbate the existing disadvantage. The paper argues for a human-in-the-loop, governed environment where AI gets the analytical and informational tasks that scale through the number of cases, but the role of counsellors remains open to the contextual interpretation, relational support, ethical judgment and accountability. It provides principles for implementation, a matrix of human and machine roles to allocate, and a research agenda of priority emphasis on longitudinal, cross-national, and equity-sensitive evaluation.
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