AI and the University as a Service
Integrity signals
Registry
Cites
Statistics
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?
Kirschenbaum and Raley obscure and misconstrue the actual challenges faced by any instructor who teaches writing by focusing critical attention on the technical features and growth patterns of new applications and evolving models.
“In focusing so much critical attention on the technical features and growth patterns of”
From Large language models and the problem of rhetorical debt (2025) · cites this paper as Kirschenbaum and Raley 2024
Cited by
Sorted by citations
Large language models and the problem of rhetorical debt
MacArthur, M. (2025) · AI & SOCIETY · doi:10.1007/s00146-025-02403-w · Cited by 8 · see citing works
“For an inherently fluid technological situation, with new applications and implementations seemingly by the day, and the models themselves continuously evolving, there can be no stable and singular path forward (Kirschenbaum and Raley 2024).”
Encheva, A., van Es, K., & Nguyen, D. (2025) · Learning, Media and Technology · doi:10.1080/17439884.2025.2578625 · Cited by 1 · see citing works
- Authors
- Kirschenbaum, M., & Raley, R.
- Venue
- PMLA/Publications of the Modern Language Association of America
- Publisher
- Modern Language Association (MLA)
- Published
- 2024-05
- Volume
- 139
- Issue
- 3
- Pages
- 504-515
- ISSN
- 0030-8129 (ISSN-L)
- 1938-1530
- OpenAlex
- W4403232382
- Cited by
- 20
- References
- 0
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 2
- FWCI
- 25.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- PMLA/Publications of the Modern Language Association of America (publishedVersion)
- Topics
- Social Sciences › Education › University Challenges and Reforms
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Educational Innovations and Challenges
- Keywords
- artificial intelligence, universities, higher education, academic libraries, artificial intelligence in education
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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