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Proposal pack · RO-LD04-01 v1

Do professionals trained with AI lose the skill without it a year later?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-04 debate on 14 October 2026, not a finding. Checked by an independent AI reviewer with fresh context (not a person), 14 October 2026 (review of PR #72): verdicts, nearest-work reading labels and limits checked; query lists corrected to the 8 queries run where they differed. Every check so far was done by AI; no person or domain expert has checked it yet. The literature screen below was done by AI web search and can miss relevant work. Version v1, permanent address https://sociolens-web.pages.dev/opportunities/ro-ld04-01/v1/.

Not yet measured A PhD project Low to medium cost Partly addressed

Research question

Among people learning a professional or technical skill (clinical reasoning, programming, legal drafting), are those randomly assigned to learn with generative AI less able to do the work without it 6 to 12 months later than comparable learners trained without it, measured against an unassisted baseline test?

Why it matters

The hollowing worry is "never-skilling", that novices who learn with AI never build the skill AI does for them. The randomized evidence stops at a single session or three months, and the only professional trial has no baseline skill test. Longer-horizon evidence comes from school pupils and is observational. Without a randomized professional test over a year, training programmes cannot tell whether AI-assisted training is a shortcut or a debt.

The debate lists this among what we still do not know: Do people who learn a task with AI become less able to do it without AI over months or years ("never-skilling")?

Evidence that motivates it

  1. In a three-month randomized trial with 133 patent lawyers, AI assistance raised graded drafting quality by 0.38 standard deviations at 90 days; on a later task done without AI, treated seniors scored 0.45 standard deviations higher than control seniors, treated juniors no higher (−0.03), and the senior–junior difference was statistically significant. (LD04-SL-C10 v1.0)
    After three months of AI-assisted patent drafting, seniors improved on an unaided task and juniors did not; the study has no baseline skill test and stops at three months.
    Cites: Autor et al. (2026), “Does AI Assistance Enhance or Erode…”
  2. In a field experiment with nearly 1,000 Turkish high-school students, practising with a standard GPT-4 interface raised practice scores by 48% but left students 17% worse than controls on a closed-book exam later in the same session, while a tutor version with safeguards raised practice by 127% and left exam scores indistinguishable from controls. (LD04-SL-C11 v1.0)
    Students who practised with a standard GPT-4 interface did 17% worse on a closed-book exam in the same session, while a tutor version with safeguards caused no detectable loss; the test was short-term only.
    Cites: Bastani et al. (2025), “Generative AI without guardrails…”
  3. In a randomized experiment with 52 Python programmers learning an unfamiliar library, those given an AI assistant scored 17% lower on a later quiz of concepts, code reading and debugging, with no significant speed gain; participants who delegated code to the AI scored lowest. (LD04-SL-C12 v1.0)
    Programmers learning a new library with AI scored 17% lower on a quiz straight afterwards, and those who delegated code scored lowest; one session only.
    Cites: Shen & Tamkin (2026), “How AI Impacts Skill Formation”
  4. In an online experiment with 1,174 Argentine adults, AI access shrank the performance gap between education groups on a business problem-solving task from 0.548 to 0.139 standard deviations; in a follow-up module without AI in the same session, a gap of 0.200 standard deviations remained. (LD04-SL-C03 v1.0)
    A module done without AI immediately after an AI-assisted task showed no loss, and the authors call it a diagnostic rather than a test of skill formation.
    Cites: Cruces et al. (2026), “Does generative AI narrow…”

Claims are shown as they read on the day this pack was built; claim pages keep every earlier version.

A possible design

Randomized controlled trial inside an existing training programme, for example a university programming course, a law-school drafting clinic or a residency simulation curriculum. Give every participant an unassisted baseline skill test, then randomize cohorts or sections to three arms: no AI, unrestricted AI, and AI with learning guardrails (hints and explanations rather than answers). Test without AI at the end of training and again at 6 and 12 months, using tasks graded blind by assessors who do not know the arm. Record how AI was used (logs where consented) so that delegation and explanation-seeking can be compared.

Data needed

Access to a training cohort large enough to randomize by section; validated unassisted skill tests with parallel forms; consented AI-use logs; retention of participants over a year (incentives for follow-up tests).

Feasibility

A PhD project. Low to medium; the main costs are participant incentives for follow-up tests and grader time; the AI tools themselves are cheap. Randomizing AI access within a course raises fairness concerns, and one semester-long trial had to merge its arms after student protests, so randomizing by cohort or section, with the guardrailed arm as an acceptable alternative, is safer. Contamination (control learners using AI privately) must be measured, not assumed away.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 14 October 2026; not yet checked in depth).

No randomized or matched study was found that assigns people learning a professional or technical skill to learn with or without generative AI, gives a baseline unassisted test, and repeats unassisted tests at 6 to 12 months or later. Randomized delayed tests found stop at 45 days (students who studied with ChatGPT scored lower on a surprise test) and one week. The longest professional field trial, in patent drafting, runs three months with no baseline test. Longer-horizon evidence is observational and about school pupils: a reported 30-month panel of Chinese secondary students and a ten-year US maths-platform panel. A medical-education perspective states that direct evidence from medical training is absent.

Where the search could not reach. Screened by web search (8 queries) and arXiv abstract pages on the date shown; Google Scholar, ERIC, PubMed, Scopus and medical-education journals were not searched directly. Two nearest works were seen only through listings and press coverage. A small trial with medical students and a one-semester observational programming-course study appeared in listings and were not opened. Absence of a hit is not proof that the work does not exist.

Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.

Nearest work found

References

Sources behind the motivating claims:

BibTeX

The first entry cites this opportunity; then its nearest work; then the sources above. Nearest work and sources are recorded as reference strings, so each goes in whole as the title; add authors and year from the original before you cite it.

@misc{sociolens_ro_ld04_01_v1,
  author = {{SocioLens project}},
  title = {Do professionals trained with AI lose the skill without it a year later?},
  year = {2026},
  date = {2026-10-14},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld04-01/v1/}},
  note = {Research opportunity RO-LD04-01, version v1}
}

@misc{ro_ld04_01_nearest_1,
  title = {Strömberg, Lei \& Wu, "The Generative AI Learning Penalty: Evidence from Chinese Secondary Education," CEPR Discussion Paper DP21577 (2026)},
  url = {https://cepr.org/publications/DP21577},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only; Some details not confirmed}
}

@misc{ro_ld04_01_nearest_2,
  title = {Rismanchian, Uzun, Matayoshi, Cosyn \& Kurd-Misto, "Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build," arXiv:2605.21629 (2026)},
  url = {https://arxiv.org/abs/2605.21629},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld04_01_nearest_3,
  title = {Barcaui, "ChatGPT as a cognitive crutch: Evidence from a randomized controlled trial on knowledge retention," Social Sciences \& Humanities Open 12, 102287 (2025)},
  url = {https://www.sciencedirect.com/science/article/pii/S2590291125010186},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld04_01_nearest_4,
  title = {Contractor \& Reyes, "Experimental Evidence on the Learning Impact of Generative AI," arXiv:2607.08849 (2026)},
  url = {https://arxiv.org/abs/2607.08849},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld04_01_nearest_5,
  title = {Benedek \& Sziklai, "Impact of AI Tools on Learning Outcomes: Decreasing Knowledge and Over-Reliance," arXiv:2510.16019 (2025)},
  url = {https://arxiv.org/abs/2510.16019},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld04_01_nearest_6,
  title = {Fan, "Toward Measuring AI's Effects on Skill Formation: The Stock-Formation Gap," arXiv:2605.16283 (2026)},
  url = {https://arxiv.org/abs/2605.16283},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{src_sl_autor_et_al_nber_35720,
  title = {Autor, Rodchenko, Martin, Iscenko, Strand, Pearl \& Ferere, “Does AI Assistance Enhance or Erode Expertise? Evidence from a Three-Month Field Experiment in Patent Drafting,” NBER Working Paper 35720, 2026},
  url = {https://doi.org/10.3386/w35720},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_bastani_et_al_pnas_2025,
  title = {Bastani, Bastani, Sungu, Ge, Kabakcı \& Mariman, “Generative AI without guardrails can harm learning: Evidence from high school mathematics,” PNAS 122(26), 2025},
  url = {https://doi.org/10.1073/pnas.2422633122},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_shen_tamkin_arxiv_2601_20245,
  title = {Shen \& Tamkin, “How AI Impacts Skill Formation,” arXiv:2601.20245 v2, February 2026},
  url = {https://arxiv.org/abs/2601.20245v2},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_cruces_et_al_arxiv_2608_04198,
  title = {Cruces, Fernández Meijide, Galiani, Gálvez \& Lombardi, “Does generative AI narrow education-based productivity gaps? Evidence from a randomized experiment,” arXiv:2608.04198, submitted 4 August 2026},
  url = {https://arxiv.org/abs/2608.04198},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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