University Education and the Transformation of Professional Work: Task Change, Early-Career Learning, and Institutional Adaptation
Hugo Roger Paz · Zenodo (CERN European Organization for Nuclear Research) · 27 Oct 2026
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Where this sits in the debate
- Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)
- Will AI destroy more jobs than it creates?
Direction of the finding as read from the abstract by an AI model (AI-written).
Abstract
This study examines how generative artificial intelligence is transforming professional work and the processes through which individuals acquire professional expertise. Rather than focusing exclusively on occupational displacement, it addresses a less visible but fundamental question: How will future professionals develop expertise when AI increasingly performs tasks traditionally assigned to early-career workers? Drawing on empirical research from the United States, Denmark, and Germany, the paper distinguishes technological exposure, actual adoption, productivity gains, employment changes, and professional learning. Recent experimental evidence reveals contrasting outcomes: AI assistance can enhance productivity and expand the range of tasks individuals can perform, but improved assisted performance does not necessarily translate into equivalent gains in independent knowledge or professional judgment. The study introduces the "experience gap" as a provisional conceptual framework encompassing three dimensions: execution, knowledge transfer, and professional development trajectories. It examines how AI may accelerate, replace, or reorganize the experiences through which professional competence is acquired. Particular attention is given to the implications for university education, emphasizing the importance of disciplinary foundations, supervised practice, transferable competencies, and the development of independent professional judgment. Argentina is examined as a contextual case, exploring how international evidence can inform educational and institutional adaptation without assuming uniform technological trajectories or deterministic predictions. The paper also proposes an institutional observation framework and a testable pilot research design to investigate the relationship between technological change, professional learning, and university education. Research type: Documentary and analytical study with an international comparative perspective. Publication st
About this publication
Preprint. A preprint — posted publicly before or without journal peer review; findings may still be revised.
- Published
- 2026 (recently)
- Citations
- 0 (OpenAlex)
0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.
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