R0029/2026-03-27/Q005 — Assessment¶
BLUF¶
Submitting AI-generated output as one's own work is extensively documented in workplace and academic contexts. In the workplace, 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 nearly 7,000 UK students were formally caught in 2023-24 (triple the prior year). Software engineering-specific misrepresentation data is sparse — AI code tool usage is extensively measured (84% of developers use AI), but the "misrepresentation" framing does not map cleanly to engineering culture where AI assistance is often expected.
Probability¶
Rating: N/A — this is a descriptive question about evidence existence
Confidence in assessment: High
Confidence rationale: The workplace data comes from one of the largest AI behavior surveys ever conducted (48K+). Academic data comes from multiple independent sources (BestColleges, UK institutional data, Stanford longitudinal). The software engineering gap is a genuine absence, confirmed by targeted searching.
Reasoning Chain¶
- KPMG/Melbourne study (48K+ respondents, 47 countries) finds 57% of workers present AI work as their own. This is the single strongest data point. [SRC01-E01, High reliability, High relevance]
- BestColleges data shows 22% of college students admit using ChatGPT despite believing it's cheating. 43% have used AI tools for academic work. [SRC02-E01, Medium reliability, High relevance]
- UK institutional data shows 7,000 formal AI cheating cases in 2023-24, triple the prior year. [SRC03-E01, Medium-High reliability, High relevance]
- Stanford research shows overall cheating rates (60-70%) unchanged pre/post-ChatGPT, suggesting AI substitutes for existing cheating methods. [SRC04-E01, High reliability, Medium relevance]
- Software engineering: multiple searches found extensive data on AI code tool adoption (84% of developers, 42% of code AI-generated) but no surveys specifically measuring misrepresentation. This is JUDGMENT: the "misrepresentation" framing may not apply in engineering contexts where AI assistance is normalized.
- JUDGMENT: The evidence clearly documents the behavior in workplace and academic contexts. The software engineering gap is real but may reflect different cultural norms rather than absence of the behavior.
Evidence Base Summary¶
| Source | Description | Reliability | Relevance | Key Finding |
|---|---|---|---|---|
| SRC01 | KPMG/Melbourne workplace | High | High | 57% hide AI use |
| SRC02 | BestColleges student | Medium | High | 22% admit despite believing it's cheating |
| SRC03 | UK formal cases | Medium-High | High | 7,000 cases (3x increase) |
| SRC04 | Stanford longitudinal | High | Medium | Cheating rates unchanged |
Collection Synthesis¶
| Dimension | Assessment |
|---|---|
| Evidence quality | Robust for workplace and academic; absent for software engineering |
| Source agreement | High — all academic/workplace sources confirm the behavior exists |
| Source independence | High — KPMG, BestColleges, UK institutions, Stanford are independent |
| Outliers | Stanford's stable-rates finding is a useful counterpoint but not a contradiction |
Detail¶
The workplace and academic evidence is strong and consistent. The KPMG 57% figure is particularly striking because it comes from a well-designed, large-scale survey. The Stanford finding that overall cheating rates are unchanged provides important nuance — AI is a new tool for an old behavior, not a new behavior.
Gaps¶
| Missing Evidence | Impact on Assessment |
|---|---|
| Software engineering misrepresentation surveys | Cannot answer the query fully for all three requested contexts |
| Longitudinal workplace data (pre/post-ChatGPT) | Cannot assess whether workplace misrepresentation increased or always existed |
| Non-Western academic data | UK and US dominate; other countries' experiences unknown |
Researcher Bias Check¶
Declared biases: No researcher profile provided for this run.
Influence assessment: The query frames AI use as "submitting as own work" — this framing embeds a negative judgment. In software engineering, AI code assistance is often expected, so the same behavior is not "misrepresentation." This framing bias was surfaced and addressed in the analysis.
Cross-References¶
| Entity | ID | File |
|---|---|---|
| Hypotheses | H1, H2, H3 | hypotheses/ |
| Sources | SRC01, SRC02, SRC03, SRC04 | sources/ |
| ACH Matrix | — | ach-matrix.md |
| Self-Audit | — | self-audit.md |