Deskilling and upskilling with AI systems
Introduction. Deskilling is a long-standing prediction of the use of information technology, raised anew by the increased capabilities of AI (AI) systems. A review of studies of AI applications suggests that deskilling (or levelling of ability) is a common outcome, but systems can also require new...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Borås
2025-03-01
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| Series: | Information Research: An International Electronic Journal |
| Subjects: | |
| Online Access: | https://publicera.kb.se/ir/article/view/47143 |
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| Summary: | Introduction. Deskilling is a long-standing prediction of the use of information technology, raised anew by the increased capabilities of AI (AI) systems. A review of studies of AI applications suggests that deskilling (or levelling of ability) is a common outcome, but systems can also require new skills, i.e., upskilling.
Method. To identify which settings are more likely to yield deskilling vs. upskilling, we propose a model of a human interacting with an AI system for a task. The model highlights the possibility for a worker to develop and exhibit (or not) skills in prompting for, and evaluation and editing of system output, thus yielding upskilling or deskilling.
Findings. We illustrate these model-predicted effects on work with examples of current studies of AI-based systems.
Conclusions. We discuss organizational implications of systems that deskill or upskill workers and suggest future research directions.
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| ISSN: | 1368-1613 |