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Proposal pack · RO-LD09-02 v1

Does the convergence caused by writing with AI persist or compound over months of use?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-09 debate on 15 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-ld09-02/v1/.

Not yet measured A PhD project Needs a budget (medium or higher) Partly addressed

Research question

When the same people write or brainstorm with a language model repeatedly over a term or a year, does the similarity of their work to each other's grow, stay level or fade, and does it carry over into what they write without the model?

Why it matters

Nearly all evidence of homogenization comes from single sessions. One experiment found that creativity gains vanished once the model was taken away while coached participants' ideas stayed more alike, which suggests the collective cost could outlast the individual gain. If convergence compounds over repeated use, single-session effects understate it; if it fades, the narrowing position overstates the cost.

The debate lists this among what we still do not know: Does convergence persist or compound over months and years of use, and over generations of students trained with AI?

Evidence that motivates it

  1. 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)
    Help from a model did not raise later unassisted originality, and strategy coaching left ideas more similar afterwards.
    Cites: Kumar et al. (2025), “Human Creativity in the Age of…”
  2. 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)
    Stories written with AI ideas were rated better and were more similar to each other, in one session.
    Cites: Doshi & Hauser (2024), “Generative AI enhances individual…”
  3. 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)
    Homogenization came from the model's contributed text, not the users' own text, within a session.
    Cites: Padmakumar & He (2024), “Does Writing with Language…”

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

A possible design

A preregistered longitudinal experiment in writing courses. Sections are randomized to a model-assisted condition, a plural-model or diverse-prompt condition, or no model, for a term. Students complete matched writing and ideation tasks with and without assistance at baseline, mid-term, end of term and three months later. Measure pairwise similarity within section at the level of ideas (coded by blind raters) and of style (embeddings), separately, and each student's distance from model outputs.

Data needed

Access to course sections willing to be randomized, with consent; rater coding of ideas; model logs for the assisted conditions.

Feasibility

A PhD project. Medium; mainly rater time and participant incentives. Contamination across conditions is likely when students use models outside class; measure out-of-class use and analyse by intention to treat.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 15 October 2026; not yet checked in depth).

No randomized panel was found that follows the same writers over months of model use and measures convergence with and without the model. Nearest work is observational population trends, single-session essay experiments, and one experiment reported to follow students for about two weeks.

Where the search could not reach. Screened by two web searches and arXiv record lookups on the date shown; education databases (ERIC) and learning-sciences conferences were not searched. 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_ld09_02_v1,
  author = {{SocioLens project}},
  title = {Does the convergence caused by writing with AI persist or compound over months of use?},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld09-02/v1/}},
  note = {Research opportunity RO-LD09-02, version v1}
}

@misc{ro_ld09_02_nearest_1,
  title = {Lee, Borchers, Alvero, Joachims \& Kizilcec, "The Digital Divide in Generative AI: Evidence from Large Language Model Use in College Admissions Essays," arXiv:2602.17791 (2026)},
  url = {https://arxiv.org/abs/2602.17791},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld09_02_nearest_2,
  title = {Inoshita, Omura, Yamanaka, Maeda \& Tsuji, "Does AI Homogenize Student Thinking? A Multi-Dimensional Analysis of Structural Convergence in AI-Augmented Essays," arXiv:2603.21228 (2026)},
  url = {https://arxiv.org/abs/2603.21228},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld09_02_nearest_3,
  title = {Sourati, Karimi-Malekabadi, Ozcan, McDaniel, Ziabari, Trager et al., "The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models," arXiv:2502.11266 (2025), reported published in Nature Human Behaviour},
  url = {https://arxiv.org/abs/2502.11266},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld09_02_nearest_4,
  title = {Susanto, Woo, Yeung, Lo-Philip \& Yeung, "The Crutch or the Ceiling? How Different Generations of LLMs Shape EFL Student Writings," arXiv:2604.15460 (2026)},
  url = {https://arxiv.org/abs/2604.15460},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld09_02_nearest_5,
  title = {Christenson, de Langis, Hayati \& Kang, "Effects of Varying LLM Access on Essay Writing Behavior," arXiv:2606.00250 (2026), BEA Workshop 2026},
  url = {https://arxiv.org/abs/2606.00250},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld09_02_nearest_6,
  title = {Randomized experiment with 210 undergraduates allowed or forbidden to use AI, with a retest about a week later, NBER conference paper f236743 (authors and year not confirmed)},
  url = {https://conference.nber.org/conf_papers/f236743.pdf},
  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_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}
}

@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}
}

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