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

A new arXiv paper by Aysa Fan, submitted April 12, 2026 and revised August 9, 2026, identifies a 'stock-formation gap' in measuring AI's effects on skill formation, arguing that current deployment telemetry observes AI use in skilled work but not whether users become more capable independently, while controlled studies measure independent capability only in narrow settings. The paper proposes a research program linking consented usage records to independent assessments, varying whether AI supplies answers, hints, feedback, or evaluation, and concludes that existing measurement cannot determine whether AI erodes skill formation at population scale.

Computer Science Computers and Society Submitted on 12 Apr 2026 v1 https://arxiv.org/abs/2605.16283v1 , last revised 9 Aug 2026 this version, v3 Title:Toward Measuring AI's Effects on Skill Formation: The Stock-Formation Gap View PDF /pdf/2605.16283 HTML experimental https://arxiv.org/html/2605.16283v3 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. Submission history From: Aysa Fan view email /show-email/2722ec1a/2605.16283 Sun, 12 Apr 2026 05:42:20 UTC 179 KB v1 /abs/2605.16283v1 Fri, 22 May 2026 12:26:41 UTC 165 KB v2 /abs/2605.16283v2 v3 Sun, 9 Aug 2026 23:41:19 UTC 84 KB References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender 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 https://info.arxiv.org/labs/index.html .