AI Adoption in Research Administration at Emerging Research Institutions
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| Title: | AI Adoption in Research Administration at Emerging Research Institutions |
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| Language: | English |
| Authors: | Dylan Ruediger, Ruby MacDougall, Stefanie Brachfield, Doug Dechow, Jonathan Parker, Jana Remy, Ithaka S+R |
| Source: | ITHAKA S+R. 2026. |
| Availability: | ITHAKA S+R. Available from: ITHAKA. One Liberty Plaza, 165 Broadway 5th Floor, New York, NY 10006. Tel: 212-500-2355; e-mail: ithakasr@ithaka.org; Web site: https://sr.ithaka.org |
| Peer Reviewed: | N |
| Page Count: | 19 |
| Publication Date: | 2026 |
| Sponsoring Agency: | National Science Foundation (NSF) |
| Contract Number: | 2437518 |
| Document Type: | Reports - Evaluative |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Research Administration, Research Universities, Technology Integration, Workshops, Higher Education, Researchers, Capacity Building, Institutional Characteristics, Attitudes, Universities |
| Geographic Terms: | California, New Jersey |
| DOI: | 10.18665/sr.325252 |
| Abstract: | Research administration, an essential component of a university's research enterprise, is growing more complex, costly, and cumbersome each year. According to the Council on Government Relations (COGR), the federal government has issued over 200 new or revised policies related to the administration of research funded by federal agencies in the past 10 years. Funding from private philanthropy brings with it additional compliance requirements that may differ substantively from those of the federal government. Though one effect of these regulations is a web of red tape, at their core are principles that help ensure research is ethical, legal, trustworthy, and makes good use of funders' resources. Research universities of all sizes depend on well-trained staff and well-organized procedures and workflows to manage the pre-award tasks required to effectively compete for research dollars and the post-award tasks associated with expending them. Maintaining the staff and infrastructure to support a robust research enterprise is challenging for all universities, but especially for Emerging Research Institutions (ERIs), where limited staffing resources may restrict opportunities to grow their research portfolios--and along with it the capacity to expand their staffing. As generative AI transitions into an everyday technology, university research offices are exploring its potential to reduce administrative burden and increase operational efficiency. Vendors who provide enterprise level research information management systems are asking similar questions and building AI capabilities into their platforms. While AI seems to present real opportunities to automate aspects of research administrators' workflows, to date the actual return on investment on AI tools is unclear, and widespread adoption presents change and risk management problems. Ithaka S+R, Chapman University, and Montclair State University organized two workshops to help research administrators consider how to leverage AI to build research capacity at ERIs. Working in small groups, participants at both workshops assessed their existing capacities and needs, shared information about experiments with AI, and ideated implementation strategies and collaborative possibilities. Throughout the workshops, a number of important open questions also surfaced about the future of an AI-enabled research enterprise. |
| Abstractor: | ERIC |
| Entry Date: | 2026 |
| Accession Number: | ED681080 |
| Database: | ERIC |
| Abstract: | Research administration, an essential component of a university's research enterprise, is growing more complex, costly, and cumbersome each year. According to the Council on Government Relations (COGR), the federal government has issued over 200 new or revised policies related to the administration of research funded by federal agencies in the past 10 years. Funding from private philanthropy brings with it additional compliance requirements that may differ substantively from those of the federal government. Though one effect of these regulations is a web of red tape, at their core are principles that help ensure research is ethical, legal, trustworthy, and makes good use of funders' resources. Research universities of all sizes depend on well-trained staff and well-organized procedures and workflows to manage the pre-award tasks required to effectively compete for research dollars and the post-award tasks associated with expending them. Maintaining the staff and infrastructure to support a robust research enterprise is challenging for all universities, but especially for Emerging Research Institutions (ERIs), where limited staffing resources may restrict opportunities to grow their research portfolios--and along with it the capacity to expand their staffing. As generative AI transitions into an everyday technology, university research offices are exploring its potential to reduce administrative burden and increase operational efficiency. Vendors who provide enterprise level research information management systems are asking similar questions and building AI capabilities into their platforms. While AI seems to present real opportunities to automate aspects of research administrators' workflows, to date the actual return on investment on AI tools is unclear, and widespread adoption presents change and risk management problems. Ithaka S+R, Chapman University, and Montclair State University organized two workshops to help research administrators consider how to leverage AI to build research capacity at ERIs. Working in small groups, participants at both workshops assessed their existing capacities and needs, shared information about experiments with AI, and ideated implementation strategies and collaborative possibilities. Throughout the workshops, a number of important open questions also surfaced about the future of an AI-enabled research enterprise. |
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| DOI: | 10.18665/sr.325252 |
