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Proposal pack · RO-LD04-08 v1

What work do junior staff in Greek firms still learn by doing once AI arrives?

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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 as an undergraduate-scale project, after the 17 October 2026 reader tests found no opportunity a final-year student could take on on 18 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-ld04-08/v1/.

Untested beyond the original setting Undergraduate thesis Low cost Partly addressed

Research question

In Greek accounting offices, law firms and software companies that use generative AI, which tasks that juniors used to learn on are now done with AI or by seniors, and how do juniors and their supervisors describe the feedback and practice juniors now get?

Why it matters

LD-04 asks whether AI removes the work through which novices become experts. The evidence it cites is about hiring in the United States and Sweden, an ethnography of surgical training and a theoretical model; no study SocioLens found measures whether the tasks juniors learn on are shrinking, and the interview studies found are of software engineers in South Korea and elsewhere. A small interview study cannot say how common a change is, but it can describe whether and how it happens in occupations and a country the debate does not cover, and give a later survey better questions.

Evidence that motivates it

  1. 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)
    In US résumé data, firms that began integrating generative AI saw junior employment fall about 9% relative to senior employment, mainly through fewer junior hires.
    Cites: Hosseini Maasoum & Lichtinger (2026), “Generative AI as…”
  2. 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)
    When surgical robots took trainees out of hands-on work, approved routes for learning failed and only a minority of trainees reached competence, through tolerated but unapproved practice.
    Cites: Beane (2019), “Shadow Learning: Building Robotic Surgical…”
  3. 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 theoretical model shows that automating entry-level work can lower long-run growth even without cutting entry-level jobs, by moving novices away from the best experts.
    Cites: Ide (2026), “Automation, AI, and the Intergenerational…”
  4. 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 language model to generate solutions understood less than those who asked it for explanations, so how juniors use the tool may matter as much as whether they use it.
    Cites: Lehmann et al. (2025), “AI Meets the Classroom: When Do…”

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

A possible design

Semi-structured interviews with about 12 to 16 people in two or three occupations (for example accounting, law and software), pairing where possible a junior with under three years in the job and a supervisor in the same firm. Ask about concrete recent tasks: who did each step, with which tool, who checked it and what feedback followed, and how the same task was done before generative AI. Analyse with thematic analysis and compare juniors' and supervisors' accounts. Report the findings as descriptions of what happens in these firms, not as estimates of how common it is.

Data needed

Access to 12 to 16 interviewees through the university careers office, professional associations or personal networks; consent and anonymisation under the university's ethics procedure; recording and transcription (free tools are enough).

Feasibility

Undergraduate thesis. Very low; interviews in person or by video call and free transcription software, with no specialist data. Suited to a final-year sociology thesis with training in qualitative methods. Firms may be reluctant to discuss AI use, so recruit individuals rather than firms, and ask for examples rather than opinions. It complements RO-LD04-03 and RO-LD04-04, which need larger designs.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 18 October 2026; not yet checked in depth).

Interview studies already describe this mechanism among junior and senior software engineers in South Korea (14 interviews) and among 13 junior software developers whose country the abstract does not state. No interview study was found in Greece, or of junior accountants or lawyers, that pairs juniors with their supervisors and asks what happened to specific tasks juniors used to learn on. The search was web-based and partial.

Where the search could not reach. Screened by three web searches (two in English, one in Greek) and arXiv records on the date shown; Greek university repositories, the National Archive of PhD Theses and education and management databases 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_ld04_08_v1,
  author = {{SocioLens project}},
  title = {What work do junior staff in Greek firms still learn by doing once AI arrives?},
  year = {2026},
  date = {2026-10-18},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld04-08/v1/}},
  note = {Research opportunity RO-LD04-08, version v1}
}

@misc{ro_ld04_08_nearest_1,
  title = {Yu and Moon, "Who Will Become the Next Senior? How Generative AI Erodes the Development Pathway in Software Engineering", arXiv:2607.17067 (2026; accepted to AIES 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{ro_ld04_08_nearest_2,
  title = {Andriotte and Ribeiro, "Verification-Conditioned Use: A Qualitative Study on How Generative AI Reshapes Learning, Autonomy, and Market Entry for Junior Software Developers", arXiv:2607.24606 (2026)},
  url = {https://arxiv.org/abs/2607.24606},
  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_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}
}

@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_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}
}

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