University Education and the Transformation of Professional Work: Task Change, Early-Career Learning, and Institutional Adaptation

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 status: Preprint, Version 1.0 (October 2026). Not peer-reviewed.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-27
DOI
https://doi.org/10.5281/zenodo.23688109
Primary Topic
Higher Education and Employability
Type
preprint
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preprint

University Education and the Transformation of Professional Work: Task Change, Early-Career Learning, and Institutional Adaptation

Hugo Roger Paz
5 links
Zenodo (CERN European Organization for Nuclear Research)
Higher Education and Employability
preprint

University Education and the Transformation of Professional Work: Task Change, Early-Career Learning, and Institutional Adaptation

Hugo Roger Paz
preprint en

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 status: Preprint, Version 1.0 (October 2026). Not peer-reviewed.

Zenodo (CERN European Organization for Nuclear Research)
Quality education, Decent work and economic growth
Higher Education and Employability
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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) (2026) | TGRS Research Map | TGRS