{"slug": "toward-measuring-ai-s-effects-on-skill-formation-the-stock-formation-gap", "title": "Toward Measuring AI's Effects on Skill Formation: The Stock-Formation Gap", "summary": "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.", "body_md": "# Computer Science > Computers and Society\n\n[Submitted on 12 Apr 2026 (\n\n[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\n\n[View PDF](/pdf/2605.16283)\n\n[HTML (experimental)](https://arxiv.org/html/2605.16283v3)\n\nAbstract: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.\n\n## Submission history\n\nFrom: Aysa Fan [[view email](/show-email/2722ec1a/2605.16283)]\n\n**Sun, 12 Apr 2026 05:42:20 UTC (179 KB)**\n\n[[v1]](/abs/2605.16283v1)**Fri, 22 May 2026 12:26:41 UTC (165 KB)**\n\n[[v2]](/abs/2605.16283v2)**[v3]** Sun, 9 Aug 2026 23:41:19 UTC (84 KB)\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth 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.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/toward-measuring-ai-s-effects-on-skill-formation-the-stock-formation-gap", "canonical_source": "https://arxiv.org/abs/2605.16283", "published_at": "2026-08-26 19:28:46+00:00", "updated_at": "2026-08-26 19:44:00.225124+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-research", "ai-ethics"], "entities": ["Aysa Fan", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/toward-measuring-ai-s-effects-on-skill-formation-the-stock-formation-gap", "markdown": "https://wpnews.pro/news/toward-measuring-ai-s-effects-on-skill-formation-the-stock-formation-gap.md", "text": "https://wpnews.pro/news/toward-measuring-ai-s-effects-on-skill-formation-the-stock-formation-gap.txt", "jsonld": "https://wpnews.pro/news/toward-measuring-ai-s-effects-on-skill-formation-the-stock-formation-gap.jsonld"}}