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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University of Borås
2025-03-01
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| Series: | Information Research: An International Electronic Journal |
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| Online Access: | https://publicera.kb.se/ir/article/view/47143 |
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| author | Kevin Crowston Francesco Bolici |
| author_facet | Kevin Crowston Francesco Bolici |
| author_sort | Kevin Crowston |
| collection | DOAJ |
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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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| format | Article |
| id | doaj-art-28d15ecc7f6341eba2c2334d17582083 |
| institution | OA Journals |
| issn | 1368-1613 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | University of Borås |
| record_format | Article |
| series | Information Research: An International Electronic Journal |
| spelling | doaj-art-28d15ecc7f6341eba2c2334d175820832025-08-20T01:57:20ZengUniversity of BoråsInformation Research: An International Electronic Journal1368-16132025-03-0130iConf10.47989/ir30iConf47143Deskilling and upskilling with AI systemsKevin Crowston0Francesco Bolici1Syracuse University, United States of AmericaUniversità degli studi di Cassino e del Lazio Meridionale 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. https://publicera.kb.se/ir/article/view/47143Generative AIDeskillingUpskilling |
| spellingShingle | Kevin Crowston Francesco Bolici Deskilling and upskilling with AI systems Information Research: An International Electronic Journal Generative AI Deskilling Upskilling |
| title | Deskilling and upskilling with AI systems |
| title_full | Deskilling and upskilling with AI systems |
| title_fullStr | Deskilling and upskilling with AI systems |
| title_full_unstemmed | Deskilling and upskilling with AI systems |
| title_short | Deskilling and upskilling with AI systems |
| title_sort | deskilling and upskilling with ai systems |
| topic | Generative AI Deskilling Upskilling |
| url | https://publicera.kb.se/ir/article/view/47143 |
| work_keys_str_mv | AT kevincrowston deskillingandupskillingwithaisystems AT francescobolici deskillingandupskillingwithaisystems |