待翻譯：Toward Measuring AI's Effects on Skill Formation: The Stock-Formation Gap

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--> [Submitted on 12 Apr 2026 (v1), last revised 9 Aug 2026 (this version, v3)] Title:Toward Measuring AI's Effects on Skill Formation: The Stock-Formation Gap View a PDF of the paper titled Toward Measuring AI's Effects on Skill Formation: The Stock-Formation Gap, by Aysa Xuemo Fan View PDF HTML (experimental) Abstract:Large-scale AI deployment data and controlled learning experiments characterize different consequences of the same technology. Deployment telemetry shows that AI use is concentrated in skilled work and frequently supports immediate task performance. It observes tasks, interaction patterns, and outputs, however, not whether users become more capable of performing those tasks independently. Controlled studies measure independent capability more directly, but only in narrower populations and settings, with outcomes that vary substantially by interaction design. We formulate this discrepancy as a stock--formation measurement gap: current systems observe the use of existing expertise more readily than the formation of future expertise. Because formation has historically been society's recovery mechanism through technological change, the gap matters well beyond any single classroom. We synthesize the experimental and observational evidence by identification strength, use public deployment data as a descriptive illustration of the gap, and identify the missing bridge between interaction traces and unassisted retention and transfer. We then propose a research program that links consented usage records to independent assessments while experimentally varying whether AI supplies answers, hints, feedback, or evaluation. The claim is not that AI has been shown to erode skill formation at population scale. It is that existing measurement cannot determine whether it does, and that this question is both measurable and designable. Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI) Cite as: arXiv:2605.16283 [cs.CY] (or arXiv:2605.16283v3 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2605.16283 arXiv-issued DOI via DataCite Submission history From: Aysa Fan [view email] [v1] Sun, 12 Apr 2026 05:42:20 UTC (179 KB) [v2] Fri, 22 May 2026 12:26:41 UTC (165 KB) [v3] Sun, 9 Aug 2026 23:41:19 UTC (84 KB) Full-text links: Access Paper: View a PDF of the paper titled Toward Measuring AI's Effects on Skill Formation: The Stock-Formation Gap, by Aysa Xuemo Fan View PDF HTML (experimental) TeX Source view license Current browse context: cs.CY new | recent | 2026-05 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)