R0029/2026-03-27/Q005
Query: 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.
BLUF: Extensive quantitative data documents AI output misrepresentation in workplace and academic contexts. In workplaces, 57% of employees hide AI use and present AI work as their own (KPMG, 48K+ respondents). In academia, 22% of college students admit using ChatGPT despite believing it constitutes cheating, and 7,000 UK students were formally caught in 2023-24. Software engineering-specific misrepresentation data is sparse — AI code tool adoption is well-measured (84% use AI), but the "misrepresentation" framing does not map cleanly to engineering culture.
Answer: H3 (Documented in academic/workplace, sparse for SE) · Confidence: High
Summary
| Entity |
Description |
| Query Definition |
Question as received, clarified, ambiguities, sub-questions |
| Assessment |
Full analytical product |
| ACH Matrix |
Evidence × hypotheses diagnosticity analysis |
| Self-Audit |
ROBIS-adapted 4-domain process audit |
Hypotheses
| ID |
Statement |
Status |
| H1 |
Widespread across all contexts |
Partially supported |
| H2 |
Limited/anecdotal evidence only |
Eliminated |
| H3 |
Documented in academic/workplace, sparse for SE |
Supported |
Key Statistics
| Context |
Metric |
Value |
Source |
| Workplace |
Workers hiding AI use |
57% |
KPMG/Melbourne 2025 (48K+) |
| Workplace |
Using AI without verifying |
66% |
KPMG/Melbourne 2025 |
| Academic |
Students admitting AI use despite cheating belief |
22% |
BestColleges |
| Academic |
UK students formally caught (2023-24) |
~7,000 |
UK institutional data |
| Academic |
Year-over-year increase in UK cases |
3x |
UK institutional data |
| Academic |
AI submissions undetected by markers |
94% |
University of Reading |
| Academic |
Overall cheating rate (stable) |
60-70% |
Stanford |
| SE |
Developers using AI tools |
84% |
Stack Overflow 2025 |
| SE |
AI-generated code proportion |
42% |
Various surveys |
Searches
| ID |
Target |
Type |
Outcome |
| S01 |
Academic AI cheating surveys |
WebSearch |
3 selected, 17 rejected |
| S02 |
Workplace AI hiding behavior |
WebSearch |
1 selected, 19 rejected |
Sources
Revisit Triggers
- Software engineering-specific misrepresentation survey published
- KPMG/Melbourne 2026 study with updated workplace data
- Major employer or professional body issues formal AI code attribution policy