Skip to content

R0029/2026-03-27/Q005 — Query Definition
Research R0029 — Plural Voice Attribution
Run 2026-03-27
Query Q005

Query as Received

Are there documented cases, studies, or surveys about people submitting AI-generated output as their own work? Look specifically at software engineering, academic, and professional contexts with quantitative data where available.

Query as Clarified

  • Subject: Documented instances and survey data on people presenting AI-generated content as their own work
  • Scope: Three contexts: software engineering (code), academic (student submissions), professional (workplace deliverables)
  • Evidence basis: Surveys with quantitative data; formal investigations; institutional reports
  • Temporal sensitivity: High — the phenomenon is post-ChatGPT (November 2022+)

Ambiguities Identified

  1. "Submitting as their own work" encompasses a spectrum from undisclosed use of grammar tools to wholesale submission of AI-generated essays. The threshold matters.
  2. The software engineering context may not frame AI code assistance as "misrepresentation" the same way academia does — different norms apply.
  3. "Documented cases" could mean formal misconduct findings or self-reported survey data. Both are relevant but have different evidentiary weight.

Sub-Questions

  1. What do surveys say about students submitting AI-generated academic work?
  2. What do surveys say about workers presenting AI-generated professional output as their own?
  3. Is there quantitative data from the software engineering context specifically?
  4. How prevalent is this behavior based on the best available data?

Hypotheses

ID Hypothesis Description
H1 Yes — widespread and well-documented Multiple surveys with large samples document significant rates of AI output misrepresentation across all three contexts
H2 No — limited or anecdotal evidence only No substantial quantitative data exists; evidence is mainly anecdotal or speculative
H3 Documented in academic and workplace contexts but limited data for software engineering specifically Academic and workplace surveys exist but software engineering data is sparse