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

arXiv: https://arxiv.org/abs/2605.16283 · HTML: https://mlsanity.com/paper/2605.16283/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 12 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance](https://mlsanity.com/paper/2412.09315/) (2024)
- [Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study](https://mlsanity.com/paper/2504.05570/) (2025)
- [Promises and challenges of generative artificial intelligence for human learning](https://mlsanity.com/paper/2408.12143/) (2024)
- [The StudyChat Dataset: Analyzing Student Dialogues With ChatGPT in an Artificial Intelligence Course](https://mlsanity.com/paper/2503.07928/) (2025)
- [Disentangling Learning from Judgment: Representation Learning for Open Response Analytics](https://mlsanity.com/paper/2512.23941/) (2025)
- [Practicing with Language Models Cultivates Human Empathic Communication](https://mlsanity.com/paper/2603.15245/) (2026)
- [Does the Doer Effect Exist Beyond WEIRD Populations? Toward Analytics in Radio and Phone-Based Learning](https://mlsanity.com/paper/2412.20923/) (2024)
- [Toward Sufficient Statistical Power in Algorithmic Bias Assessment: A Test for ABROCA](https://mlsanity.com/paper/2501.04683/) (2025)
- [LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics](https://mlsanity.com/paper/2601.18685/) (2026)

## Abstract (arXiv)

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.