Examples
Worked examples
- Is an instance
A National Laboratory researcher uses a generative AI tool to help draft sections of an internal technical report. Under DOE P 2031, that use must be identified and the GenAI tool cited as part of the research methodology -- an internal documentation obligation that applies to DOE and lab staff, not to an outside grant applicant.
- Is an instance
A DOE program office issues internal guidance reminding staff that any GenAI-assisted analysis used in decision-making must be reviewed by a human before it has binding effect, and that AI-generated content with binding effect must be clearly labeled as such -- both are requirements drawn from DOE P 2031's transparency provisions for the department's own use of the technology.
Counter-examples
Looks similar, but isn't
- Not an instance
A university research administrator assumes DOE requires every Office of Science FOA applicant to complete a generative-AI-use disclosure statement in the proposal narrative, the way NSF's December 2023 notice encourages proposers to do. As of this writing, DOE has not published a comparable department-wide proposer-facing disclosure requirement -- DOE P 2031 governs DOE's own workforce, not external applicants -- so this assumption should be checked against the specific FOA's instructions rather than treated as a standing DOE-wide rule.
- Not an instance
Treating DOE's Genesis Mission -- a 2026 funding initiative supporting AI-for-science research -- as the same thing as DOE's AI policy. Genesis Mission is a funding program soliciting AI-related research proposals; DOE P 2031 is a separate internal governance directive about how DOE itself uses generative AI tools. The two are related in subject matter (AI) but are not the same instrument and don't impose the same obligations.
Editorial commentary
DOE AI policy is a term worth unpacking carefully, because it is easy to conflate two genuinely different things: a real Department of Energy directive governing the department’s own use of generative AI, and the applicant-facing generative-AI-disclosure notices that funders like NSF and journal/publisher policies use to govern how external researchers may use AI when preparing a submission. Only the first of these currently exists at DOE.
What DOE P 2031 actually is
DOE P 2031, “Use of Generative Artificial Intelligence,” is a Department of Energy management directive issued December 29, 2025, sitting within DOE’s 200-series of departmental directives. It sets out the framework under which DOE identifies, adopts, deploys, and uses generative AI tools across the department and its National Laboratory system, and it was written to implement, at the departmental level, the October 2023 White House Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence and the March 2024 OMB Memorandum M-24-10 on advancing governance, innovation, and risk management for federal agency AI use. DOE also maintains a companion Generative Artificial Intelligence Reference Guide, developed with input from more than 30 DOE offices, sites, and national laboratories, that operationalizes the policy’s principles.
Who it actually governs
DOE P 2031 is an internal governance instrument. It applies to DOE’s federal workforce and, through the department’s laboratory system, to National Laboratory staff — not to external grant applicants, proposers, journal authors, or award recipients as a class. Two provisions matter most for a research-administration audience: DOE must meet executive-branch requirements on GenAI transparency and disclosure (explaining how GenAI tools operate, their objectives, and their limitations), and any AI-generated content that has a binding effect must be explicitly labeled as AI-generated. Where GenAI contributes to an idea, approach, or invention developed at DOE or a National Laboratory, staff are expected to identify that specific contribution and cite the GenAI tool as part of the research methodology — language that maps onto DOE’s existing research-integrity and record-keeping norms rather than creating a new external disclosure category.
What it is not
DOE P 2031 is not a proposer- or applicant-facing AI-disclosure policy in the way NSF’s AI policy or comparable publisher policies such as the Nature Portfolio AI Policy are. NSF’s December 2023 Notice to the Research Community, for example, encourages (though does not yet mandate) proposers to describe in the project description the extent to which generative AI was used to develop the proposal. DOE has not, as of this writing, published a department-wide equivalent for its own funding opportunity announcements (FOAs). A research administrator preparing a response to a DOE Office of Science, ARPA-E, or applied-energy program FOA should check that specific FOA’s own instructions — and the applicable financial-assistance regulations under 2 CFR Part 910, DOE’s uniform-guidance implementing regulation — rather than assume DOE P 2031 itself imposes a submission-level disclosure obligation. It is also worth not conflating DOE P 2031 with DOE’s funding initiatives in the AI space, such as the Genesis Mission funding opportunity supporting AI-for-science research: that is a grant program soliciting AI-related research, not a governance policy about AI use.
Why the distinction matters for compliance staff
Research administrators tracking funder AI policies across agencies need to know which category a given policy falls into before advising investigators. Confusing an internal-use governance directive with an applicant disclosure requirement risks either over-advising investigators to disclose something no rule currently requires of them, or under-advising them if DOE later does publish a proposer-facing notice, which OMB M-24-10’s broader push toward agency AI governance makes plausible. Until DOE publishes such a notice, the safest practice is to treat DOE P 2031 as departmental policy and to rely on individual FOA instructions for any submission-level AI-disclosure question.
Related terms
Machine-readable encodings
Use in your systems
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