Artificial Intelligence and Academic Professions
Open accessIntegrity signals
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Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The detection tools deployed in response to AI-assisted plagiarism are neither accurate nor reliable.
“Despite widespread faculty concern about AI-assisted plagiarism [58], the detection tools deployed in response are neither accurate nor reliable - further discussed in Appendix C.”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [58]
The July 2025 AAUP report Artificial Intelligence and Academic Professions draws on approximately 500 survey responses from AAUP members regarding their experiences with AI and other educational technologies.
“The report draws on approximately 500 survey responses from AAUP members regarding their experiences with AI and other educational technologies.”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [58]
In the absence of clear, publicly legible policy language, responsibility for interpreting acceptable AI use is often shifted to individual faculty or students, increasing the risk of inconsistent enforcement and inequitable outcomes.
“In the absence of clear, publicly legible policy language, responsibility for interpreting acceptable AI use is often shifted to individual faculty or students, increasing the risk of inconsistent enforcement and inequitable outcomes [58].”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [58]
Faculty are frequently expected to make consequential decisions about AI use, such as whether and how to permit AI in coursework, without adequate institutional support or coordination.
“Faculty are frequently expected to make consequential decisions about AI use - such as whether and how to permit AI in coursework - without adequate institutional support or coordination [47, 58].”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [58]
A central finding of the AAUP report was a widespread concern, reported by 71% of respondents, about the disconnect between administrative decision-making on AI policy and meaningful faculty and student input.
“A central finding was a widespread concern (reported by 71% of respondents) about the disconnect between administrative decision-making on AI policy and meaningful faculty and student input.”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [58]
For at least some faculty, the concern goes beyond the dishonesty question and is rooted in a worry that students will fail to learn.
“it is also the case that for at least some faculty, the concern goes beyond the (dis)honesty question and is rooted in a worry that students will fail to learn”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [58]
Many faculty are concerned with academic dishonesty with the rise of generative AI.
“While it is true that many faculty are concerned with academic dishonesty with the rise of generative AI”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [58]
Cited by
Sorted by citations
The Due Process Deficit: Auditing AI Governance in U.S. Higher Education
Teleki, M., Choi, A. S. G., Duray, A., Liu, H., Zhang, J., Dong, X., Da Silva, D., Koenecke, A., & Caverlee, J. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3812328
- Authors
- Paris, B., Conti-Cook, C., Greene, D., Jones, K., Justie, B., Kirschenbaum, M., Kresge, L., May, E., Nguyen, A., Reynolds, R., Sargent, S., Weinberg, L., Myers West, S., & Widder, D. G.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- University of Maryland, College Park (US)
- Indiana University Indianapolis (US)
- University of California, Los Angeles (US)
- Data & Society Research Institute (US)
- Purdue University West Lafayette (US)
- Cornell University (US)
- Corresponding author
- Widder, David Gray
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2025
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4413192880
- Cited by
- 7
- References
- 0
- Citations by year
- 2026: 6 · 2025: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › University Challenges and Reforms
- Physical Sciences › Computer Science › Information Systems › Digital Education and Society
- Keywords
- artificial intelligence in higher education, academic profession, faculty working conditions, educational technology, intellectual property, faculty governance, technology adoption in teaching, academic work
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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