aceModerate evidence
Published recordScienceProblem Space
More
How Heavily Are PhD Students Offloading Their Scientific Writing to AI? Evidence from US STEM PhD Dissertations
- Truvace record
- TRV-2026-2486raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Observational studyPreprint, not yet peer reviewed
- Sources
- 1 sourcePeer-reviewed
- sha256
- afa866183038…check it
Large language models are diffusing rapidly into science, raising concerns that delegating research writing to AI may undermine the development of expertise that scientific training is supposed to build. We measure AI-generated writing in the dissertations of a near-census of US STEM PhD graduates from 2019 to 2026 and link graduates to their subsequent employment. AI writing is absent before 2023, present in 29% of dissertations filed in 2026, and rapidly growing. Use is more common among students from non-Engl…
The problem
A working paper covering about 226,000 US STEM PhD graduates found AI-written abstracts in 29.4% of 2026 dissertations, and US-origin graduates with them were 7.4 percentage points less likely to hold academic jobs in 2026.
What this doesn’t fix
The authors say the employment analysis is correlational, not causal, and could reflect students who do not want research careers writing lower-effort dissertations; the paper also says the evidence cannot resolve whether AI use degrades research preparation.
Sources
1 on file- NBER Working Paperdoi:10.3386/w35859
Cite this record
v1Truvace Impact Record TRV-2026-2486, v1: “How Heavily Are PhD Students Offloading Their Scientific Writing to AI? Evidence from US STEM PhD Dissertations.” Truvace, 2026-11-12. /record/TRV-2026-2486 (accessed on the day you copy it). sha256 afa866183038ce0f…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
Certified into the record
Check this record hasn’t been altered
How to check it yourself, without trusting this page
Download any version from /api/record/TRV-2026-2486 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.