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Proposal pack · RO-LD04-04 v1

What AI set-ups do employers actually give trainees and junior staff?

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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-ld04-04/v1/.

Institutional practice unknown Master's thesis Low cost No study found

Research question

In law firms, hospitals and residency programmes, and software firms, what generative-AI tools, settings and rules do trainees and junior staff actually get, do they differ from what seniors get, and do any employers use the learning protections (hints instead of answers, AI-off practice) that experiments show protect skill?

Why it matters

Experiments show that how AI is configured and used decides whether learners lose skill: a tutor version with safeguards caused no detectable exam loss where a standard interface did, and explanation-seeking protected understanding where delegation did not. Whether that evidence matters for careers depends on what employers deploy for juniors, and nobody has documented it. A description of real practice would show whether the protective designs exist outside the lab and where policy guidance is needed.

The debate lists this among what we still do not know: What do employers actually deploy for juniors: guardrails, learning modes, or unrestricted tools?

Evidence that motivates it

  1. 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)
    Practice with a standard GPT-4 interface lowered exam performance while a tutor version with safeguards did not detectably: configuration decides the learning effect.
    Cites: Bastani et al. (2025), “Generative AI without guardrails…”
  2. 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)
    Learners who used AI for concepts scored well and those who delegated scored lowest, so rules about how juniors use AI could matter as much as access.
    Cites: Shen & Tamkin (2026), “How AI Impacts Skill Formation”
  3. In two preregistered lab experiments and a field study, LLM access had no overall effect on learning: students who used it to generate solutions covered more topics but understood each less, students who asked it for explanations understood more, and the gap between students with low and high prior knowledge widened. (LD04-SL-C13 v1.0)
    Asking an LLM for explanations raised understanding while generating solutions lowered it.
    Cites: Lehmann et al. (2025), “AI Meets the Classroom: When Do…”
  4. In an experiment where participants studied a technical domain with or without an LLM, gains were highly uneven and were predicted not by grades or prior knowledge but by skill at eliciting, filtering and verifying AI output; participants low on that skill saw limited or negative returns, and a concept-map scaffold reduced the spread. (LD04-SL-C06 v1.0)
    A concept-map scaffold reduced the spread of gains from LLM-assisted study, an example of a design an employer could adopt.
    Cites: Idan & Anand (2026), “Generative AI and the Productivity…”

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

A possible design

Two parts. First, a document analysis of published AI policies and guidance for trainees and junior staff: a sample of large law firms, NHS trusts and US residency programmes, professional bodies (law societies, medical colleges) and software firms' public engineering handbooks, coded for access rules by seniority, permitted uses, required verification and any learning protections. Second, a short survey of training leads (training principals, programme directors, engineering managers) on what is configured in practice and why, with follow-up interviews for a subsample.

Data needed

Public policy documents and guidance; contact lists of training leads from professional bodies; a coding frame with inter-rater reliability checks.

Feasibility

Master's thesis. Low; documents are public and the survey can use free tools; time for recruitment is the main cost. Published policies may differ from practice, which is why the survey is needed. Response rates from busy training leads may be low; working through professional bodies helps.

Has this been done?

The search found no study that does the core design (first-pass screen of 14 October 2026; not yet checked in depth).

What was found is surveys of whether law firms have any AI policy or training at all (not split by seniority), a text-mining study of 160 industry AI guidelines with no trainee focus in its abstract, an audit of AI documents at US medical schools (educators, not employers), single policies or guidance for residents and trainees, and one small study of junior software engineers reporting that organisational policy constrains their AI use. No study was found that documents what employers configure for trainees or juniors, whether that differs from seniors, or whether any employer uses learning protections.

Where the search could not reach. Screened by web search (8 queries) and arXiv abstract pages on the date shown. The sites of ACGME, AAMC, the Law Society of England and Wales and US bar ethics bodies were not searched directly, nor PubMed or medical-education journals. Every policy or guidance document was seen through search listings only. 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_04_v1,
  author = {{SocioLens project}},
  title = {What AI set-ups do employers actually give trainees and junior staff?},
  year = {2026},
  date = {2026-10-14},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld04-04/v1/}},
  note = {Research opportunity RO-LD04-04, version v1}
}

@misc{ro_ld04_04_nearest_1,
  title = {Jiao, Afroogh, Chen, Atkinson \& Dhurandhar, "Generative AI and LLMs in Industry: A text-mining Analysis and Critical Evaluation of Guidelines and Policy Statements Across Fourteen Industrial Sectors," arXiv:2501.00957 (2026)},
  url = {https://arxiv.org/abs/2501.00957},
  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_04_nearest_2,
  title = {Feng, Yun \& Wang, "From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineering," arXiv:2602.00496 (2026)},
  url = {https://arxiv.org/abs/2602.00496},
  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_04_nearest_3,
  title = {"An audit of AI-related documents across U.S. medical schools: A framework-based qualitative content analysis," Medical Teacher (2025)},
  url = {https://hsrc.himmelfarb.gwu.edu/gwhpubs/7889},
  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_04_nearest_4,
  title = {8am, 2026 Legal Industry Report, as reported by LawNext (March 2026)},
  url = {https://www.lawnext.com/2026/03/ai-adoption-among-legal-professionals-has-more-than-doubled-in-a-year-new-8am-report-finds-but-firms-lag-far-behind-individual-practitioners.html},
  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_04_nearest_5,
  title = {Faculty of Intensive Care Medicine (UK), "Use of Generative AI in ICM Training and Revalidation" (interim guidance, July 2025)},
  url = {https://ficm.ac.uk/use-of-generative-ai-in-icm-training-and-revalidation},
  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{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_lehmann_cornelius_sting_arxiv_2409_09047,
  title = {Lehmann, Cornelius \& Sting, “AI Meets the Classroom: When Do Large Language Models Harm Learning?,” arXiv:2409.09047 v2, March 2025},
  url = {https://arxiv.org/abs/2409.09047v2},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_idan_anand_arxiv_2605_18143,
  title = {Idan \& Anand, “Generative AI and the Productivity Divide: Human-AI Complementarities in Education,” arXiv:2605.18143, May 2026},
  url = {https://arxiv.org/abs/2605.18143},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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