{"resourceId":"nber-w35859-ai-writing-us-stem-phd-dissertations-careers-202610","versions":[{"version":"external-2726b8946a415c8c055388b80cd3df82963946f8047e1aaf83fc96895e763dbc","resource":{"id":"nber-w35859-ai-writing-us-stem-phd-dissertations-careers-202610","title":"NBER working paper: AI writing appears in 29% of 2026 US STEM PhD dissertations, and its users are less likely to enter academic research","organization":"National Bureau of Economic Research (Daniel P. Gross, Dror Shvadron, Hansen Zhang); reported by Nature 16 Oct 2026","sector":"Doctoral research training and the research workforce","geography":"United States (US STEM PhD graduates 2019-2026)","publishedAt":"NBER Working Paper 35859, issue date October 2026 (day not stated); Nature news coverage 16 October 2026","publicationDate":null,"eventDate":null,"sourceName":"National Bureau of Economic Research","sourceLabel":"Academic working paper (not peer reviewed)","sourceUrl":"https://www.nber.org/papers/w35859","evidenceClass":"academic-research","outcomeClass":"cautionary","topics":["knowledge-work","accessibility-workforce"],"finding":"NBER Working Paper 35859 (Gross, Shvadron and Zhang; issue date October 2026) measures AI-generated writing in a near-census of US STEM PhD dissertations from 2019 to 2026 and links graduates to later employment. AI writing is absent before 2023, present in 29% of dissertations filed in 2026, and rising; use is more common among students from non-English-speaking countries and at lower-ranked programmes. Within the same programme and cohort, graduates whose dissertations contain AI writing are less likely to enter academic research careers and more likely to enter non-tenure-track roles or industry, strongest for US-origin students. The authors argue AI may be displacing part of the work through which scientific expertise is developed. Nature's 16 October report on the paper adds that it covers more than 226,000 graduates, used the Pangram detector on abstracts only without separating partial from full AI generation, found 1.7% in 2023, 18.8% in 2025 and 29.4% in January-May 2026 (36% in mathematical and computer sciences), and that US-undergraduate graduates with AI-written abstracts were 17% less likely to work in academia and 16% more likely to work in industry; an engineer at UNSW warns training matters most where knowledge is incomplete.","sledRelevance":"Graduate schools and research offices set dissertation standards and train the next research workforce. A near-census finding that 29% of 2026 STEM dissertations contain AI writing, with an association to leaving academic research, gives them a measured baseline for policy on AI use in doctoral work and for reviewing whether training still builds expertise.","evidence":"Near-census of US STEM dissertations 2019-2026 (Nature: >226,000 graduates), detector applied to abstracts, linked employment profiles; within program-cohort comparisons; working paper.","architectureImplications":"Limited; institutions considering AI-detection in thesis workflows should treat detector output as a screening signal with known error rates, not proof.","governanceImplications":"Graduate schools may need explicit dissertation AI-use and disclosure policies and advisor guidance; the paper measures prevalence, not misconduct.","securityPrivacyImplications":"Running student work through commercial detectors raises data-handling and consent questions.","caveats":"Not peer reviewed; detector-based; abstracts only; correlational career outcomes; LinkedIn coverage bias.","streamIds":["research"],"roles":{"sales":"The framing point is research training, not cheating. An NBER working paper finds AI writing in 29% of 2026 US STEM dissertations and links it with fewer academic research careers. For graduate deans and vice presidents for research, a bounded engagement is reviewing dissertation AI-use policy, disclosure and advisor guidance. Avoid selling detectors as proof; the study used a detector on abstracts only, is not peer reviewed and shows association, not cause.","engineering":"Fit is limited and mostly about data handling. If an institution wants to measure AI writing in theses, treat detector output as a statistical signal with known error rates, process only with consent and clear retention terms, and avoid individual judgments from scores. Proof of value would be a de-identified, aggregate prevalence estimate validated against a labelled sample before any policy use.","delivery":"Delivery is policy and training: a dissertation AI-use and disclosure policy, advisor guidance and committee practices that check how work was done. The graduate school owns it with faculty governance. Acceptance should track adoption of disclosure and advisor training completion. Risks include penalizing non-native English speakers, false accusations from detectors and policies that ignore legitimate AI use in research."},"roleDetails":{"sales":{"customerProblem":"Graduate schools lack a measured baseline and policy for AI use in dissertations and worry training no longer builds research expertise.","stakeholders":["Graduate school dean","Vice president for research","Faculty advisors and committee chairs","Office of international students","Research integrity office"],"discoveryQuestions":["Does the graduate school have a dissertation AI-use and disclosure policy?","How do committees assess how research and writing were done?","How would policy affect students writing in a second language?"],"valueHypothesis":"Interpretation: clear disclosure and advising practices may protect training quality without banning useful AI assistance.","potentialEngagement":"A policy and practice review for dissertation AI use, with draft disclosure language and advisor guidance.","unsupportedClaims":["That AI use causes students to leave academia","That detector scores identify individual misconduct","Peer-reviewed status or findings outside US STEM"]},"engineering":{"fit":"Limited; relevant only if institutions measure or screen thesis text.","architectureIntegration":"Aggregate, de-identified analysis of thesis abstracts with a validated detector, separated from individual decisions.","prerequisites":["Consent and data-use approval","Labelled validation sample","Detector with published error rates"],"constraints":["Detector error rates and bias","Abstracts may not represent full documents","Legal and privacy limits on student work"],"security":"Minimize data shared with vendors, set retention limits and avoid individual-level scoring.","proofOfValue":["Validate detector on a labelled local sample","Report only aggregate prevalence","Review results for language-background bias"]},"delivery":{"work":"Draft dissertation AI-use and disclosure policy, train advisors, and update committee review practice.","dependencies":["Faculty governance approval","Graduate school systems","Student input"],"ownership":"Graduate school dean with faculty council.","skillsAdoption":"Advisor workshops on supervising AI-assisted research and writing; student guidance on disclosure.","governanceCheckpoints":["Faculty council approval","Equity review for non-native English speakers","Annual review of policy"],"acceptanceCriteria":["Proposal: disclosure statement in all dissertations filed after adoption","Proposal: advisor training completion tracked","Proposal: no individual sanctions based on detector scores alone"],"risks":["Penalizing non-native speakers","False accusations","Policy that ignores legitimate AI use"]}},"retrievedAt":"2026-10-16T22:07:28-05:00","enrichedAt":"2026-10-16T22:12:24-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Higher use among students from non-English-speaking countries suggests AI is also a language aid; policy should avoid penalizing those students while protecting training quality.","procurementImplications":"Any detector procurement should require published error rates and data-retention terms.","operatingModelImplications":"Advising and committee review may need to check how research and writing were done, not only the final document.","sourceVerification":{"openedUrl":"https://www.nber.org/papers/w35859","referenceExcerpt":"AI writing is absent before 2023, present in 29% of dissertations filed in 2026, and rapidly growing.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}