From execution to judgement: Reconfiguring higher education policy and outcome-based education for the integration of youth in the GenAI economy

Abstract

The emergence of generative artificial intelligence (GenAI) has substantially reshaped corporate processes, organisational structures, and human capital requirements, specifically impacting entry-level roles. This research uses a methodology that integrates four international frameworks: ISCO-08, O*NET, JRC-Eurofound, and CEDEFOP, to propose an original ‘Four Axes Task and Job Classification Framework for Entry-Level Profiles’ (Cognitive Complexity, Digital Intensity, Autonomy, and Social Dimension). By deconstructing jobs into tasks, the study identifies how GenAI increasingly automates routine cognitive activities, such as information processing, report drafting, and error debugging, that traditionally served as foundational training for recent grads. To assess task exposure and substitutability, this research maps entry-level vacancies from leading job portals against the proposed framework using a structured coding approach. Findings are then compared with international labour market reports to contrast theoretical expectations with observed corporate outcomes.

The analysis reveals emerging barriers to youth employment. First, a ‘Seniority Bias’, meaning that companies prefer experienced professionals to manage AI outputs over hiring juniors. Second, the automation of entry-level tasks eliminates the ‘training phase’ incentive, as the marginal cost of AI subscriptions and token consumption is significantly lower than the cost of on-the-job training. Finally, a ‘triple transmission-belt’ effect shows a chronological decoupling between fast technological innovation, the pace of corporate adoption, and slower university curricular updates.

The study concludes that, within an outcomes-based education approach, the value proposition for recent grads must shift from operational execution to strategic judgement and supervision. To remain relevant, higher education must address the gap created by the automation of traditional entry-level jobs, nurturing ‘New Collar’ specialists capable of using critical thinking to validate algorithmic outputs.