CASRAI Dictionary

Tag: research management and administration

  • AI in Research Administration: Where It’s Actually Deployed

    Most coverage of artificial intelligence in higher education still centres on the classroom — chatbots writing essays, detectors chasing them. Less visible, but arguably more consequential for research offices, is AI in research administration: the back-office layer of proposal budgeting, compliance screening and post-award reporting that keeps federally and privately funded research compliant and auditable. That layer is where AI is quietly moving from pilot to production in 2026, and the evidence — not the marketing copy — shows a narrower, more cautious footprint than headlines suggest.

    This is not a piece about generative AI and authorship integrity, disclosure norms, or research misconduct detection in manuscripts — those questions sit in a separate, already well-documented debate. This is about the administrative machinery: proposal-budget checking, risk-based compliance review, contract redlining and financial reporting inside research offices, sponsored-programmes units and grants-management systems.

    Where AI Is Actually Being Deployed

    The clearest signal comes from a March 2026 Ithaka S+R report, funded through the National Science Foundation’s GRANTED programme (grant #2437518), which convened two workshops — one at Montclair State University (31 participants, 13 institutions) and one at Chapman University (32 participants, 13 institutions) — specifically to catalogue how research administration software and AI tools are being used inside research offices. The findings map closely onto three workflow areas:

    • Pre-award proposal and budget checking. Institutions are using AI to review draft proposals and budgets for items that will trigger downstream review — facilities requirements, human-subjects protocols, or budget lines inconsistent with a sponsor’s rules.
    • Risk-based compliance screening. AI is used as a first-pass filter that flags transactions, contract clauses, or expenditures for human review rather than replacing that review — described by workshop participants as “an extra layer” that directs attention, not a decision-maker.
    • Contract and reporting automation. Redlining of routine contract language, drafting of progress narratives, and identification of funded projects with commercialisation potential are the most cited post-award use cases.

    Two concrete examples illustrate the pattern at very different institutional scales. Southern Utah University, a smaller teaching-focused institution, built a budget-availability report that automatically flags high-risk expenditures for review — a narrow, operationally specific tool rather than a platform. At the University of California San Diego, a large research-intensive institution, the contracts and grants office is running risk-based proposal review to identify projects needing facilities or IRB attention, and has automated non-disclosure-agreement redlining in a way staff estimate cuts drafting time by roughly 70 percent.

    Workflow stage AI use case Maturity in 2026 Reported example
    Pre-award Proposal/budget risk flagging Early production UCSD risk-based proposal review
    Pre-award Funding-opportunity matching Experimental Faculty-to-grant matching pilots
    Compliance Contract clause / NDA redlining Early production UCSD NDA redlining
    Compliance Expenditure anomaly flagging Pilot Southern Utah University budget-availability tool
    Post-award Progress-report drafting Experimental Institution-reported pilots, Ithaka S+R 2026
    Institution-wide Policy Q&A chatbot for staff Early production UCSD TritonGPT; Emory ORAgpt proof-of-concept

    Evidence From the Field: What Institutions Report

    Two enterprise-level projects sit ahead of the field. TritonGPT, developed at UC San Diego and trained on institutional policy documents, has been available to the campus community since 2023 and is now offered as software-as-a-service to other institutions. At the University of Idaho, the NSF GRANTED-funded AI4RA initiative is building open-source tools for research administrators. At the system level, the California State University system ran a 94,000-response AI sentiment survey — described as the largest of its kind — to set baseline metrics before committing to further rollout.

    These are not isolated enthusiasm projects. The Council on Governmental Relations (COGR) has documented that the U.S. federal government issued more than 200 new or revised policies affecting research administration over the preceding ten years — a compliance burden that is the actual driver behind AI adoption, not novelty. Emory University’s sponsored-programmes office built a proof-of-concept generative AI chatbot, reported by SRA International in May 2025, intended to give research administrators instant, policy-grounded answers rather than requiring them to search static guidance documents.

    Answer-First Q&A

    What is AI actually used for in research administration?

    Institutions report using AI mainly for risk-based screening: flagging proposal budgets, contract clauses, or expenditures that need human review, plus drafting routine reports and answering staff policy questions. It is deployed as a triage layer, not as an autonomous decision-maker in compliance-sensitive workflows.

    Is AI reliable enough for research compliance work?

    Not on its own. Workshop participants in the Ithaka S+R study described current tools as error-prone for high-stakes compliance decisions, so institutions keep a human reviewer in the loop and use AI outputs as a prioritisation signal rather than a final determination.

    What is electronic research administration (eRA) software?

    Electronic research administration (eRA) software centralises pre-award proposal development, post-award financial tracking, IRB/IACUC compliance management, and reporting in one system. Vendors including Cayuse, InfoEd Global and Streamlyne are now embedding AI features into these existing platforms rather than institutions building AI tools separately.

    Will AI replace research administrators?

    Current evidence points the other way. Institutions describe AI as freeing staff time for relationship-building and strategic work, while raising a genuine concern: if entry-level document review and compliance checks are automated away, the profession may lose the training ground that builds administrator judgement over time.

    What Remains Experimental — and Why

    Effort-report anomaly detection — using AI to flag inconsistencies in how research staff certify time charged to federal awards — is frequently proposed as a logical extension of risk-based screening, but publicly documented institutional deployments remain scarce as of mid-2026. This gap matters: effort reporting sits inside some of the most tightly regulated financial-compliance territory in federally sponsored research, and institutions appear to be moving deliberately rather than rushing tools into that specific workflow.

    Three barriers recur across every institution surveyed in the Ithaka S+R workshops:

    • Data governance. Fragmented, inconsistent institutional data undermines AI output quality, and grant proposals routinely contain data covered by HIPAA, export-control rules, or pre-publication intellectual property.
    • Fragmented adoption. Most institutions have not articulated an institution-wide AI strategy for research administration; use is left to individual staff discretion, producing uneven, hard-to-scale experimentation.
    • Trust. Faculty scepticism about whether proposal or compliance data will be used to train external vendor models directly affects whether research administrators can deploy AI tools without damaging working relationships they depend on.

    Implications for Institutions and the Profession

    The practical pattern for institutions considering AI in grants management and compliance workflows is narrower and more disciplined than vendor marketing implies: start with a specific, bounded use case — budget flagging, contract redlining, a policy-guidance chatbot — evaluate it against defined return-on-investment questions, and keep a human reviewer accountable for the final determination. The institutions cited above succeeded by treating AI as an attention-directing layer inside existing research administration workflows, not as a replacement for the judgement that compliance work requires.

    For the broader field of research management and administration, the open question the Ithaka S+R researchers themselves flag is workforce development: if AI absorbs the entry-level document review that has historically trained new research administrators, institutions will need to redesign how professional judgement is built, not just how workloads are reduced. Organisations such as NCURA, SRA International and NORDP are already the venues where this cross-institutional knowledge-sharing is happening, ahead of any formal standard for AI use in the field.

    CASRAI’s own coverage of research administration software categories and standards tracks how eRA platforms are evolving as AI features are absorbed into existing pre-award, post-award and compliance modules — the practical mechanism by which most institutions will encounter AI in this space, rather than through bespoke in-house builds.

  • Pre-Award vs Post-Award Research Administration: Where Compliance Risk Concentrates

    Every sponsored-research office eventually asks the same operational question: where, exactly, does an audit finding get born? Pre-award research administration and post-award research administration are often treated as a single continuous job description, but they carry very different compliance profiles. Under the Office of Management and Budget’s Uniform Guidance (2 CFR 200), the two phases are governed by overlapping but distinct subparts, and institutions that blur the boundary tend to discover the gap only when a federal auditor draws attention to it.

    This guide separates the two functions, maps the specific 2 CFR 200 provisions most associated with audit findings, and flags what changed when OMB’s most recent revision took effect.

    Pre-award vs post-award: where the line falls

    Pre-award activity covers everything that happens before an institution accepts a sponsor’s terms. It is proposal-facing rather than transaction-facing, and its compliance burden is concentrated in representations made to the sponsor rather than in ongoing financial stewardship.

    • Identifying and matching funding opportunities to investigator plans
    • Budget justification and application of institutional/federal indirect cost rates
    • Compliance screening — conflict-of-interest disclosure, human/animal subject clearances, export control review
    • Internal routing, sign-off, and proposal submission
    • Award negotiation and formal acceptance of terms

    Post-award administration begins the moment an award account is set up and runs through closeout. This is where the volume and complexity of federal financial transactions live, which is also why post-award research administration generates a disproportionate share of Single Audit findings.

    • Award and general ledger account setup
    • Ongoing financial compliance monitoring — allowability, allocability, and reasonableness of costs
    • Effort certification and personnel cost justification
    • Subrecipient monitoring on any pass-through funds
    • Interim and final financial and progress reporting
    • Project closeout, equipment disposition, and unused-funds reconciliation

    Bodies such as research administration professional associations — ARMA in the UK, NCURA in the US, and EARMA across Europe — increasingly teach pre-award and post-award as a connected lifecycle rather than two silos, precisely because handoff gaps between the two are where compliance exposure accumulates.

    The compliance risk heatmap

    Not every task carries equal audit exposure. Mapping common research-administration tasks against the Uniform Guidance provisions auditors cite most often produces a practical heatmap for prioritising internal review effort.

    Phase Task Governing 2 CFR 200 provision Typical audit-finding risk
    Pre-award Budget development / indirect cost application Subpart E — Cost Principles Low–Medium
    Pre-award Conflict-of-interest and subject-protection clearance §200.112, institutional policy Medium
    Post-award Procurement of goods/services on federal funds §§200.317–200.327 High
    Post-award Subrecipient monitoring §§200.331–200.333 High
    Post-award Internal controls over federal expenditure §200.303 High
    Post-award Effort certification / salary charging Subpart E, Compensation Medium–High
    Post-award Financial and progress reporting timeliness §§200.328–200.329 Medium
    Post-award Closeout and equipment disposition §§200.344–200.345 Low–Medium

    The pattern is consistent across institutional Single Audits: pre-award weaknesses tend to surface as proposal-accuracy or disclosure gaps, while post-award weaknesses — inadequate subrecipient monitoring, undocumented internal controls, and procurement shortcuts — account for the majority of significant deficiencies reported to cognizant agencies. That imbalance is exactly why post-award teams typically carry larger headcount relative to transaction volume, even though pre-award work is more visible to investigators.

    The Uniform Guidance is changing

    OMB’s most recent revision to 2 CFR 200 took effect for federal awards issued on or after 1 October 2024, and it directly reshapes several of the risk areas above. Institutions still operating on pre-2024 assumptions are the ones most likely to generate findings against the revised text.

    • The Single Audit expenditure threshold rose from $750,000 to $1,000,000, removing some smaller institutions from mandatory audit scope but concentrating audit attention on larger, more complex programmes.
    • The de minimis indirect cost rate available to entities without a negotiated rate agreement rose from 10% to 15% of modified total direct costs.
    • The equipment and capital-asset capitalisation threshold rose from $5,000 to $10,000, changing what must be separately tracked and reported at closeout.

    Further clarifying guidance and agency-specific implementation notes continue to be issued as sponsors align their own policy manuals with the revised text, which means the compliance target for both pre-award and post-award teams is still moving. Research offices that update proposal templates and account-setup checklists only once, at the point of the original 2024 change, risk drifting out of alignment as agencies finish rolling out their own interpretations.

    Common questions on pre-award and post-award risk

    What is pre-award research administration?

    Pre-award research administration is the set of institutional functions that support a project from funding search through award acceptance — matching opportunities, building compliant budgets, screening for conflicts of interest, and routing proposals for internal sign-off before submission to a sponsor.

    What is the pre-award process?

    The pre-award process runs from identifying a funding opportunity through formal award acceptance. It typically includes proposal development, budget justification, internal institutional review, submission to the sponsor, and negotiation of final award terms before the account is established.

    What is a pre-award?

    A pre-award refers to the preparatory documentation and approvals — intent-to-apply forms, budget justifications, compliance certifications — completed before a sponsor formally commits funding. These records establish the institutional and regulatory basis the eventual award will be managed against.

    What skills do you need to be a research administrator?

    Research administrators need working knowledge of sponsor and federal regulations (including the Uniform Guidance), budget and financial analysis skills, attention to procedural detail, and the ability to translate technical compliance requirements into plain guidance for investigators.

    Implications for research offices

    The practical takeaway is not that pre-award compliance is unimportant — a flawed conflict-of-interest disclosure or an unallowable cost baked into a budget justification can still trigger scrutiny. The takeaway is that sponsored research administration teams should weight their internal review and training investment toward where findings actually concentrate: procurement, subrecipient monitoring, and documented internal controls in the post-award phase.

    Institutions that separate “grant administration” from “grant management” organisationally sometimes reproduce the same handoff risk internally — pre-award teams hand a fully compliant proposal to post-award teams who inherit responsibility for terms they did not negotiate. A shared risk register, reviewed jointly across both functions at account setup, closes that gap more reliably than siloed checklists. Institutional glossaries and shared reference material — see CASRAI’s research administration glossary — help standardise the terminology both teams use when escalating a compliance question.

    Looking ahead

    As OMB continues to refine implementation guidance around the 2024 Uniform Guidance revision, the boundary between pre-award and post-award compliance work will keep shifting rather than settling. Research offices that treat the two phases as a connected risk chain — rather than a handoff between departments — will be better positioned to absorb the next round of regulatory change without a corresponding spike in audit findings.