Preview: not announced. Search engines asked not to index.

Proposal pack · RO-LD04-03 v1

Do newcomers get less feedback and real work once AI coding tools arrive?

To keep this as a PDF or print it, use your browser's Print command (Ctrl+P, or Cmd+P on a Mac) and choose "Save as PDF". Links print with their addresses. This pack gathers what the opportunity page already holds; nothing in it is new.

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-ld04-03/v1/.

Not yet measured One PhD chapter or paper Low cost Partly addressed

Research question

In open-source projects, after AI coding tools are adopted, do first-time contributors receive less substantive review feedback, less mentoring and fewer tasks handed to them by experienced maintainers than in comparable projects that have not adopted them?

Why it matters

The apprenticeship-gap position rests on a mechanism, that experts stop handing novices the work and feedback through which skill is built. It is documented for surgical robots and formalised in theory, but not measured for generative AI. Open-source projects record every review comment and assignment publicly, so they allow a direct test of the feedback novices actually receive, which existing studies of newcomer counts and onboarding speed do not measure.

The debate lists this among what we still do not know: Does AI reduce the real work, feedback and supervised struggle that seniors hand to juniors?

Evidence that motivates it

  1. A two-year comparative ethnography of robotic and open surgery, with interviews at 13 US teaching hospitals, found that approved routes for learning failed in robotic surgery; only a minority of trainees came to competence, through tolerated but unapproved practices, and these practices led to hyperspecialisation and a shrinking supply of experts relative to demand. (LD04-SL-C18 v1.0)
    In robotic surgery, approved learning routes failed when the technology removed trainees from hands-on work, and the supply of experts shrank: the mechanism this study would test for generative AI.
    Cites: Beane (2019), “Shadow Learning: Building Robotic Surgical…”
  2. A theoretical model in which novices acquire tacit knowledge by working with experts shows that automating entry-level work can raise output yet lower long-run growth, even without cutting entry-level employment, by moving novices away from the most productive experts. (LD04-SL-C19 v1.0)
    A model shows that automating entry-level work can lower long-run growth by moving novices away from the best experts, even without cutting entry-level jobs; who novices work with is the variable to measure.
    Cites: Ide (2026), “Automation, AI, and the Intergenerational…”
  3. In US résumé data covering 65 million workers, firms that began integrating generative AI saw junior employment fall by about 9% relative to senior employment after six quarters, compared with non-adopters, mainly because they hired fewer juniors, while junior promotion rates rose slightly. (LD04-SL-C15 v1.0)
    US firms that adopt generative AI hire fewer juniors while promotion rates of those hired rise slightly; what happens to juniors' learning inside adopting organisations is unmeasured.
    Cites: Hosseini Maasoum & Lichtinger (2026), “Generative AI as…”

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

A possible design

Staggered difference-in-differences on public repositories. Date each project's adoption of AI coding tools (assistant configuration files, AI co-authored commits or pull requests, or tool-specific markers) and match adopters to non-adopters on size, language, activity and newcomer inflow before adoption. For each first-time contributor, measure the number and length of review comments received, whether comments explain (classified with a validated coding scheme, checked by hand on a sample), whether maintainers assign issues to them, and whether they return and become regular contributors. Cover both the 2021–2023 assistant wave and the 2024–2026 agent wave. Interviews with a sample of maintainers can test whether any change is deliberate.

Data needed

GitHub event data (GH Archive or the GitHub API), review-comment text, issue-assignment events; a coding scheme for explanatory feedback with inter-rater reliability; markers of AI-tool adoption, validated against a hand-checked sample.

Feasibility

One PhD chapter or paper. Low; public data and free tools; the main cost is hand-coding a validation sample. Adoption markers are noisy and partly invisible (private assistant use), so the treatment is "visible adoption" and must be described as such. Open source is not employment; the study would show whether the mechanism exists where it can be seen, not its size in firms.

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).

Open-source studies already estimate newcomer inflow, first pull-request acceptance, time to first response, retention and the supply of beginner-labelled issues after AI coding-agent adoption, with conflicting results (one finds no decline; another finds newcomers' share of participation down 3.7 points). None found measures the content or amount of review feedback directed at first-time contributors, explicit mentoring, or issue assignment from maintainers to newcomers, and both adoption studies cover the agent era from 2023 rather than the earlier assistant wave. In firms, the delegation channel rests on interview studies; no study using staffing or assignment data was found.

Where the search could not reach. Screened by web search (5 queries), arXiv abstract pages and a keyword search of two full texts on the date shown. ACM Digital Library, IEEE Xplore and the 2026 MSR and ICSE proceedings were not searched systematically. A study of about 23,000 AI-assisted pull requests was seen only through a bibliography page and could not be identified. Legal-industry evidence found was surveys and firm announcements, not staffing data. 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_03_v1,
  author = {{SocioLens project}},
  title = {Do newcomers get less feedback and real work once AI coding tools arrive?},
  year = {2026},
  date = {2026-10-14},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld04-03/v1/}},
  note = {Research opportunity RO-LD04-03, version v1}
}

@misc{ro_ld04_03_nearest_1,
  title = {Xu, Cui, Ye \& Zhou, "Decoupling Code Complexity from Newcomer Participation: A Causal Study of AI Coding Agent Adoption in OSS," arXiv:2607.01810 (2026)},
  url = {https://arxiv.org/abs/2607.01810},
  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_03_nearest_2,
  title = {Zhang, Jiang \& Koziolek, "Augmentation with Dilution: A Large-Scale Empirical Study of Human Contributor Ecosystems After AI Coding Agent Adoption," arXiv:2606.26289 (2026)},
  url = {https://arxiv.org/abs/2606.26289},
  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_03_nearest_3,
  title = {Gao, Banyongrakkul, Guan, Zahedi \& Treude, "On Autopilot? An Empirical Study of Human-AI Teaming and Review Practices in Open Source," arXiv:2601.13754 (2026)},
  url = {https://arxiv.org/abs/2601.13754},
  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_03_nearest_4,
  title = {Xu, Medappa, Tunc, Vroegindeweij \& Fransoo, "AI-Assisted Programming Decreases the Productivity of Experienced Developers by Increasing the Technical Debt and Maintenance Burden," arXiv:2510.10165 (2026)},
  url = {https://arxiv.org/abs/2510.10165},
  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_03_nearest_5,
  title = {Song, Agarwal \& Wen, "The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot," arXiv:2410.02091},
  url = {https://arxiv.org/abs/2410.02091},
  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_03_nearest_6,
  title = {Yu \& Moon, "Who Will Become the Next Senior? How Generative AI Erodes the Development Pathway in Software Engineering," arXiv:2607.17067 (2026)},
  url = {https://arxiv.org/abs/2607.17067},
  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_beane_asq_2019,
  title = {Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64(1), 2019},
  url = {https://doi.org/10.1177/0001839217751692},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_ide_arxiv_2507_16078_v9,
  title = {Ide, “Automation, AI, and the Intergenerational Transmission of Knowledge,” arXiv:2507.16078 v9, June 2026},
  url = {https://arxiv.org/abs/2507.16078v9},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_hosseini_maasoum_lichtinger_ssrn_5425555,
  title = {Hosseini Maasoum \& Lichtinger, “Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data,” SSRN Working Paper 5425555, version of 6 June 2026},
  url = {https://doi.org/10.2139/ssrn.5425555},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

Back to the opportunity · All research opportunities