The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index; none of the 12 sources its claims cite is checked for support yet.
Guidance on the Use of Generative Artificial Intelligence for SPUR Authors, Reviewers, and Readers
Open accessAbstract
Recommended Citation: Mabrouk, Patricia Ann. 2025. Guidance on the Use of Generative Artificial Intelligence for SPUR Authors, Reviewers, and Readers. Scholarship and Practice of Undergraduate Research 8 (2): 4-5. https://doi.org/10.18833/spur/8/2/4
Overview
CoChat Summary
TL;DR
This editorial from the SPUR Editor-in-Chief outlines guidance for authors, reviewers, and readers on using generative AI in submissions to *Scholarship & Practice of Undergraduate Research*. It argues AI offers real productivity benefits but requires mandatory disclosure and full author accountability, since neither humans nor AI can reliably detect AI-generated writing.
Key findings
- AI can benefit academic research and writing across six core domains identified by Kalifa and Albadawy (2024): idea generation and research design; literature research and synthesis; improving manuscript structure and content; data management and analysis; manuscript editing, review, and publishing; and facilitating ethical compliance, communication, and outreach.
- AI poses credibility risks through bias and fabrication (hallucinations); the editorial cites the Jeff Hancock affidavit with fake AI-generated citations, the retracted Guo, Dong, and Hao (2024) study with AI-generated images, and the withdrawn Bader et al. (2024) case report containing an AI self-disclaimer.
- At present, neither humans nor AI can flag AI-generated writing accurately and reliably (Chaka 2023; Fleckenstein et al. 2024), and there is strong evidence of potential bias against nonnative English writers (Giray 2024).
- Because AI cannot be responsible or accountable for its output, the consensus is that AI cannot be an author; SPUR authors must disclose which AI tools were used and how, and accept full accountability for all AI output.
Methods
- This is a non-empirical editorial/guidance piece authored by the SPUR Editor-in-Chief; it presents policy guidance rather than original data or analysis.
- It synthesizes cited literature and illustrative cases (e.g., Hancock, Guo et al., Bader et al.) to justify the journal's AI guidelines.
- The editorial states SPUR currently does not use AI in its peer review and publishing processes, and commits to updating the policy as the technology evolves.
Limitations
- The paper states the policy will be updated as the technology improves and its use expands, and invites reader input—acknowledging the guidance is provisional.
- The paper states that current AI-detection tools are unreliable and that AI use cannot be definitively determined, which limits enforcement of the disclosure requirement.
- Evident from the text: the guidance rests on cited literature and anecdotal cases rather than empirical study of AI use in SPUR submissions; no data or sample are reported.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 Valid · 0 Not valid · 9 Not checked · 4 Own results and interpretations
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unsupervised use of these apps can catch even disciplinary experts unaware.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption not shown here.
In the paper
“There is strong evidence of the potential bias against nonnative English writers (Giray 2024).”
What the sources say
- Not checked yetcites Giray 2024 source
boostedstates a possible cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“the widely publicized image of grossly oversized rat genitalia and donut-bespeckled signaling pathway cartoons produced by Midjourney, which appeared in the now-retracted research study of Guo and colleagues (Guo, Dong, and Hao 2024)”
What the sources say
- Not checked yetcites Guo, Dong, and Hao 2024 source
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A check of a cited source is scheduled, or we don’t hold its full text yet; the claim is checked once it runs.
In the paper
“At present, neither humans nor AI seems to be able to flag AI-generated writing accurately and reliably (Chaka 2023;Fleckenstein et al. 2024).”
What the sources say
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A check of a cited source is scheduled, or we don’t hold its full text yet; the claim is checked once it runs.
In the paper
“Generative artificial intelligence is increasingly being applied in scholarly research, education, and publishing.”
- Also stated in: § Abstract · plain
“Generative artificial intelligence (AI) is everywhere and has significantly impacted higher education and academic publishing (Baek and Wilson 2024;Miller 2024).”
What the sources say
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Abstract · plain
9 references still to check
A free account verifies the references that are still open.
- Probably not a substantial claim. 6
- It may state two separate claims as one. 4
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Abstract
“Kalifa and Albadawy (2024) systematically reviewed the literature”
§ References
“Khalifa, Mohamed, and Mona Albadawy. 2024.”
Cited works that were retracted
Works this paper cites that were later retracted, from the retraction notices publishers deposit. Major when the notice came out before this paper was published.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
“Bader, Raneem, Ashraf Imam, Mohammad Alnees, Neta Adler, Joanthan Ilia, Diaa Zugayar, Arbell Dan, and Abed Khalaileh. 2024. REMOVED: "Successful Management of an Iatrogenic Portal Vein and Hepatic Artery Injury in a 4-Month-Old Female Patient: A Case Report and Literature Review." Radiology Case Reports 19(6): 2106-2111. doi: 10.1016/j.radcr.2024.02.037”
Cited work 10.1016/j.radcr.2024.02.037 · retracted 2024-06-01 · notice 10.1016/j.radcr.2024.02.037 · this paper published 2025
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
“Guo, Xinyu, Liang Dong, and Dingjun Hao. 2024. RETRACTED: "Cellular Functions of Spermatogonial Stem Cells in Relation to JAK/STAT Signaling Pathway." Frontiers in Cell and Develop- mental Biology 11: 1339390. doi: 10.3389/fcell.2023.1339390”
Cited work 10.3389/fcell.2023.1339390 · retracted 2024-02-16 · notice 10.3389/fcell.2024.1386861 · this paper published 2025
Citations
Cites
2 retracted
RETRACTED: Cellular functions of spermatogonial stem cells in relation to JAK/STAT signaling pathway
Guo, X., Dong, L., & Hao, D. (2024) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1339390 · Cited by 33
RetractedBader, R., Imam, A., Alnees, M., Adler, N., ilia, J., Zugayar, D., Dan, A., & Khalaileh, A. (2024) · Radiology Case Reports · doi:10.1016/j.radcr.2024.02.037 · Cited by 14
RetractedUsing artificial intelligence in academic writing and research: An essential productivity tool
Khalifa, M., & Albadawy, M. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100145 · Cited by 580
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.2.12 · Cited by 132
Do teachers spot AI? Evaluating the detectability of AI-generated texts among student essays
Fleckenstein, J., Meyer, J., Jansen, T., Keller, S. D., Köller, O., & Möller, J. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100209 · Cited by 126
The Problem with False Positives: AI Detection Unfairly Accuses Scholars of AI Plagiarism
Giray, L. (2024) · The Serials Librarian · doi:10.1080/0361526x.2024.2433256 · Cited by 60
Pandora's Can of Worms: A Year of Generative AI in Higher Education
Miller, R. E. (2024) · portal: Libraries and the Academy · doi:10.1353/pla.2024.a916988 · Cited by 16
An Inquiry Into the Use of Generative AI and Its Implications in Education: Boon or Bane
Baek, E. O., & Wilson, R. V. (2024) · International Journal of Adult Education and Technology · doi:10.4018/ijaet.349233 · Cited by 14
Research works printed but not linked: 1
- Li, Zhi-Qiang, Hui-Lin Xu, Hui-Juan Cao, Zhao-Lan Liu, Yu- Tong Fei, and Jian-Ping Liu. 2024. "Use of Artificial Intel- ligence in Peer Review Among Top 100 Medical Journals." JAMA Network Open 7(12): e2448609. doi: 10.1001/jamanet- workopen.2024.48609 · DOI
Cited by
Still being checked.
Metadata
- Authors
- Mabrouk, P. A.
- Corresponding author
- Mabrouk, Patricia Ann
- Venue
- Scholarship and Practice of Undergraduate Research
- Publisher
- Council on Undergraduate Research
- Published
- 2025
- Volume
- 8
- Issue
- 2
- Pages
- 4-5
- ISSN
- 2476-101X (ISSN-L)
- OpenAlex
- W4408543391
- Cited by
- 0
- References
- 8
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Scholarship and Practice of Undergraduate Research (publishedVersion, cc-by)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Library and Information Sciences › Academic Writing and Publishing
- Social Sciences › Library and Information Sciences › Publishing and Scholarly Communication
- Keywords
- generative AI, spurs, undergraduate research, AI use guidelines, peer review, scholarly publishing
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.
A language model drew the claims from the paper on 2026-11-13, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-13 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
