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Proposal pack · RO-LD04-07 v1

Does handing work to the model cause both skill loss and sameness?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-04 and LD-09 debates on 16 October 2026, not a finding. Not yet reviewed. 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-ld04-07/v1/.

Causal mechanism not isolated One PhD chapter or paper Low to medium cost Partly addressed

Research question

When people learning a writing or analysis task are randomly assigned to use a language model that does the work for them, a model that only explains while they do the work, or no model, do the same participants who lose more skill once the model is removed also produce work more similar to each other's and to the model's, and does the way the model is used account for both?

Why it matters

LD-04 and LD-09 each point to the way people use the model, without measuring the other debate's outcome. In LD-04, learners who delegated code to an assistant scored lowest on a later quiz, and students who asked for explanations understood more than those who asked for solutions. In LD-09, the loss of diversity in co-written essays came from the text the model contributed, not from the users' own text, and assisted gains did not carry over once the model was taken away. If delegation drives both skill loss and convergence, the two debates describe one phenomenon and one safeguard (explanation instead of answers) could address both; if not, they need different safeguards.

Evidence that motivates it

  1. 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 an AI assistant scored 17% lower on a later quiz, and those who delegated code scored lowest; the authors were Anthropic Fellows, a conflict the claim records.
    Cites: Shen & Tamkin (2026), “How AI Impacts Skill Formation”
  2. 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)
    Students who used a model to generate solutions covered more but understood less, while those who asked for explanations understood more.
    Cites: Lehmann et al. (2025), “AI Meets the Classroom: When Do…”
  3. 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)
    A standard chat interface left students worse on a later closed-book exam, while a tutor version with safeguards did not.
    Cites: Bastani et al. (2025), “Generative AI without guardrails…”
  4. In argumentative essays co-written with a language model, only the instruction-tuned model (InstructGPT) made essays more alike; the base model (GPT-3) did not, and the reduction in diversity came from the text the model contributed, while the users’ own text remained unaffected. (LD09-SL-C14 v1.0)
    The reduction in essay diversity came from the text the model contributed; users' own text was unaffected.
    Cites: Padmakumar & He (2024), “Does Writing with Language…”
  5. In two preregistered experiments, language-model assistance gave short-term boosts during assisted tasks only in some measures (in the divergent-thinking task, originality during exposure was similar across conditions), did not raise originality or fluency in later unassisted tasks, and participants who had received strategy coaching from the model produced more similar ideas afterwards. (LD09-SL-C18 v1.0)
    Assistance did not raise originality in later unassisted tasks, and those coached by the model produced more similar ideas afterwards.
    Cites: Kumar et al. (2025), “Human Creativity in the Age of…”
  6. In a preregistered online experiment, writers given one or up to five story ideas from GPT-4 wrote stories rated 5.4% and 8.1% more novel and 3.7% and 9.0% more useful than writers without AI, with the largest gains for less creative writers; but their stories were 5.2% and 5.0% more similar to the AI ideas and more similar to each other. (LD09-SL-C10 v1.0)
    Writers given AI ideas wrote stories rated more novel but more similar to each other and to the AI ideas.
    Cites: Doshi & Hauser (2024), “Generative AI enhances individual…”

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

A possible design

Run a preregistered experiment over several sessions with three arms (delegation mode, explanation mode, no model) on a writing or data-analysis task new to participants. Measure, for each person, unaided performance on a held-out task at the end and some weeks later, and the similarity of their work to other participants' work and to the model's own output, with semantic and idea-level measures validated against human judgement. Log actual use to measure how much each person delegated within each arm. Test whether the mode of use, and the logged share of delegated text, predict both outcomes, and whether the two outcomes are correlated within persons.

Data needed

Participants (students or trainees); a task battery with unaided tests; interaction logs; similarity measures validated by human raters.

Feasibility

One PhD chapter or paper. Low to medium; a multi-session online or classroom experiment with participant payments and human rating of similarity. Participants in the explanation arm may still copy text, so the logged share of delegated text is needed alongside the assigned arm. The delayed test needs good retention of participants.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 16 October 2026; not yet checked in depth).

One small multi-session study measured essay homogeneity together with recall and ownership among people writing with a language model, and in a fourth session moved some model users to writing without a tool, which comes close to a measure after removal; several studies measure either retained learning or diversity alone. No study was found that manipulates delegation against explanation and measures both retained skill and convergence in the same participants. The search was web-based and partial.

Where the search could not reach. Screened by four web searches and arXiv and Crossref records on the date shown; education and psychology databases were not searched, and this is a fast-moving area. 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_07_v1,
  author = {{SocioLens project}},
  title = {Does handing work to the model cause both skill loss and sameness?},
  year = {2026},
  date = {2026-10-16},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld04-07/v1/}},
  note = {Research opportunity RO-LD04-07, version v1}
}

@misc{ro_ld04_07_nearest_1,
  title = {Kosmyna and others, "Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task", arXiv:2506.08872 (2025)},
  url = {https://arxiv.org/abs/2506.08872},
  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_07_nearest_2,
  title = {Barcaui, "ChatGPT as a cognitive crutch: Evidence from a randomized controlled trial on knowledge retention", Social Sciences \& Humanities Open 12 (2025)},
  url = {https://doi.org/10.1016/j.ssaho.2025.102287},
  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_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_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_padmakumar_he_iclr_2024,
  title = {Padmakumar \& He, “Does Writing with Language Models Reduce Content Diversity?,” ICLR 2024, arXiv:2309.05196},
  url = {https://arxiv.org/abs/2309.05196},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_kumar_et_al_chi_2025,
  title = {Kumar, Vincentius, Jordan \& Anderson, “Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking,” CHI 2025, arXiv:2410.03703},
  url = {https://doi.org/10.1145/3706598.3714198},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_doshi_hauser_science_advances_2024,
  title = {Doshi \& Hauser, “Generative AI enhances individual creativity but reduces the collective diversity of novel content,” Science Advances 10, eadn5290, 2024},
  url = {https://doi.org/10.1126/sciadv.adn5290},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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