AI, libraries, and literacy: revisiting the Journal of the Medical Library Association's policy on the use of generative artificial intelligence in submissions
DOI:
https://doi.org/10.5195/jmla.2026.2516Keywords:
artifical intelligence, information literacy, AI in Libraries, AI literacy, literacy skills, lifelong learning, scholarly communication, scholarly publishing, scholarly writingAbstract
In the fall of 2023, the Journal of the Medical Library Association (JMLA) introduced its policy on generative artificial intelligence (AI) submissions, predicting that these tools would become embedded within the workflows of clinicians, educators, researchers, and students. Those predictions dramatically underestimated AI's impact. On the three-year anniversary of that editorial, we revisit the issue in light of AI's unprecedentedly rapid adoption across knowledge work. We examine the educational challenges AI poses, arguing that excessive cognitive offloading threatens the deep thinking on which learning depends. We then consider the challenges AI creates for researchers, from hallucinated citations to fabricated diseases. We suggest that health sciences librarians, who offer expertise in approaching information systems with discernment, are well suited to be leaders in this space. To help prospective JMLA authors weigh whether and when to use AI, we propose the Compliance, Verifiability, and Disclosure (CVD) Framework. Throughout, we reaffirm our conviction that education and research must remain human-centered endeavors.
References
1. Boruff JT, Kraft M, Carroll AJ. Introducing the Journal of the Medical Library Association’s policy on the use of generative artificial intelligence in submissions. J Med Libr Assoc. 2023 Oct 2;111(4):4. DOI:10.5195/jmla.2023.1826.
2. Deming D. How People Use ChatGPT [Substack newsletter]. Forked Lightning [Internet]. 2025 Sep 15 [cited 2026 Apr 14]. Available from: https://forklightning.substack.com/p/how-people-use-chatgpt.
3. Deming D. ChatGPT really does offer mundane utility [Substack newsletter]. Forked Lightning [Internet]. 2025 Sep 16 [cited 2026 Apr 14]. Available from: https://forklightning.substack.com/p/chatgpt-really-does-offer-mundane.
4. Bick A, Blandin A, Deming DJ. The Rapid Adoption of Generative AI [Working Paper] [Internet]. National Bureau of Economic Research; 2024 [cited 2026 Apr 14]. (Working Paper Series). Available from: https://www.nber.org/papers/w32966. DOI:10.3386/w32966.
5. Roth E. The Verge [Internet]. 2024 [cited 2026 Apr 14]. ChatGPT now has over 300 million weekly users. Available from: https://www.theverge.com/2024/12/4/24313097/chatgpt-300-million-weekly-users.
6. Comscore, Inc. [Internet]. Reston, VA; 2007 [cited 2026 Apr 14]. 61 Billion Searches Conducted Worldwide in August. Available from: https://www.comscore.com/Insights/Press-Releases/2007/10/Worldwide-Searches-Reach-61-Billion?utm_source=chatgpt.com.
7. Roth E. The Verge [Internet]. 2025 [cited 2026 Apr 14]. OpenAI says ChatGPT users send over 2.5 billion prompts every day. Available from: https://www.theverge.com/news/710867/openai-chatgpt-daily-prompts-2-billion.
8. Rogers EM. Diffusion of Innovations. 5th ed. New York: Free Press; 2003. 576 p.
9. Chatterji A, Cunningham T, Deming DJ, Hitzig Z, Ong C, Shan CY, et al. How People Use ChatGPT [Working Paper] [Internet]. National Bureau of Economic Research; 2025 [cited 2025 Sep 29]. (Working Paper Series). Available from: https://www.nber.org/papers/w34255. DOI:10.3386/w34255.
10. Humlum A, Vestergaard E. The unequal adoption of ChatGPT exacerbates existing inequalities among workers. Proc Natl Acad Sci. 2025 Jan 7;122(1):e2414972121. DOI:10.1073/pnas.2414972121.
11. Lund BD, Omame I, Tijani S, Agbaji D. Perceptions toward Artificial Intelligence among Academic Library Employees and Alignment with the Diffusion of Innovations’ Adopter Categories. Coll Res Libr. 2020 Jul 1;81(5):865. DOI:10.5860/crl.81.5.865
12. Rosner-Uddin R. Amazon staff use AI tool for unnecessary tasks to inflate usage scores. Financial Times [Internet]. 2026 May 12 [cited 2026 May 12]. Available from: https://www.ft.com/content/8ee0d3ef-9548-422d-8ff1-ebd48ad4b2ca?syn-25a6b1a6=1.
13. Goodhart CAE. Problems of Monetary Management: The UK Experience. In: Goodhart CAE, editor. Monetary Theory and Practice: The UK Experience [Internet]. London: Macmillan Education UK; 1984 [cited 2023 Apr 26]. p. 91–121. Available from: https://doi.org/10.1007/978-1-349-17295-5_4. DOI:10.1007/978-1-349-17295-5_4.
14. Pasiak G. AI Fatigue and Vocational Awe in Academic Libraries. The Scholarly Kitchen [Internet]. 2026 Mar 4 [cited 2026 Mar 30]. Available from: https://scholarlykitchen.sspnet.org/2026/03/04/guest-post-ai-fatigue-and-vocational-awe-in-academic-libraries/.
15. Slater K “Kay.” Against AI: Critical Refusal in the Library. Libr Trends. 2025;73(4):588–608.
16. Estlund K, Hudson Vitale C. Strategic Implications of AI Futures for Research Libraries: Workshop Report [Internet]. Association of Research Libraries and Coalition for Networked Information; 2026 Mar [cited 2026 Mar 30]. Available from: https://www.arl.org/resources/strategic-implications-of-ai-futures-for-research-libraries-workshop-report/. DOI:10.29242/report.futurescape2026.
17. Kung TH, Cheatham M, Medenilla A, Sillos C, Leon LD, Elepaño C, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023 Feb 9;2(2):e0000198. DOI:10.1371/journal.pdig.0000198.
18. Brynjolfsson E, Li D, Raymond L. Generative AI at Work. Q J Econ. 2025 May 1;140(2):889–942. DOI:10.1093/qje/qjae044.
19. Dell’Acqua F, McFowland E, Mollick E, Lifshitz H, Kellogg KC, Rajendran S, et al. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. Organ Sci. 2026 Mar;37(2):403–23. DOI:10.1287/orsc.2025.21838.
20. Wiles E, Krayer L, Abbadi M, Awasthi U, Kennedy R, Mishkin P, et al. GenAI as an Exoskeleton: Experimental Evidence on Knowledge Workers Using GenAI on New Skills. SSRN; 2024. DOI:https://dx.doi.org/10.2139/ssrn.4944588.
21. Bastani H, Bastani O, Sungu A, Ge H, Kabakcı Ö, Mariman R. Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proc Natl Acad Sci. 2025 Jul;122(26):e2422633122. DOI:10.1073/pnas.2422633122.
22. Willingham DT. Why don’t students like school?: a cognitive scientist answers questions about how the mind works and what it means for the classroom. San Francisco, CA: Jossey-Bass; 2009.
23. Ericsson KA, Krampe RT, Tesch-Römer C. The Role of Deliberate Practice in the Acquisition of Expert Performance. Psychol Rev. 1993 Jul;100(3):363–709.
24. Bjork RA. Memory and Meta-memory Considerations in the Training of Human Beings. In: Metcalfe J, Shimamura AP, editors. Metacognition: Knowing about Knowing. MIT Press; 1994. p. 185–205.
25. Bjork EL, Bjork RA. Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In: Psychology and the real world: Essays illustrating fundamental contributions to society. New York, NY, US: Worth Publishers; 2011. p. 56–64.
26. Gerlich M. AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies. 2025 Jan;15(1):6. DOI:10.3390/soc15010006.
27. Carroll AJ, Borycz J. Integrating large language models and generative artificial intelligence tools into information literacy instruction. J Acad Librariansh. 2024 Jul 1;50(4):102899. DOI:10.1016/j.acalib.2024.102899.
28. Pressley L. The Prior Knowledge Problem. Libraries as Epistemic Institutions [Internet]. 2026 Mar 9 [cited 2026 Mar 11]. Available from: https://laurenpressley.com/library/2026/03/09/the-prior-knowledge-problem/.
29. Grinschgl S, Papenmeier F, Meyerhoff HS. Consequences of cognitive offloading: Boosting performance but diminishing memory. Q J Exp Psychol. 2021 Sep 1;74(9):1477–96. DOI:10.1177/17470218211008060.
30. Cowan N. What are the differences between long-term, short-term, and working memory? In: Progress in Brain Research [Internet]. Elsevier; 2008 [cited 2026 Apr 15]. p. 323–38. Available from: https://www.sciencedirect.com/science/chapter/bookseries/pii/S0079612307000209. DOI:10.1016/S0079-6123(07)00020-9.
31. Halford GS, Cowan N, Andrews G. Separating cognitive capacity from knowledge: a new hypothesis. Trends Cogn Sci. 2007 Jun 1;11(6):236–42. DOI:10.1016/j.tics.2007.04.001
32. Strevens M. The knowledge machine: how irrationality created modern science. First edition. New York: Liveright Publishing Corporation; 2020. 350 p.
33. Naddaf M, Quill E. Hallucinated citations are polluting the scientific literature. What can be done? Nature. 2026 Apr 1;652(8108):26–9. DOI:10.1038/d41586-026-00969-z.
34. Naddaf M. AI linked to explosion of low-quality biomedical research papers. Nature. 2025 May 21;641(8065):1080–1. DOI:10.1038/d41586-025-01592-0.
35. Jones N. ArXiv preprint server clamps down on AI slop. Science. 2026 Jan 23. DOI:10.1126/science.z66g9k7.
36. Stokel-Walker C. Scientists invented a fake disease. AI told people it was real. Nature. 2026 Apr 7;652(8110):559–61. DOI:10.1038/d41586-026-01100-y.
37. Hao Q, Xu F, Li Y, Evans J. Artificial intelligence tools expand scientists’ impact but contract science’s focus. Nature. 2026 Jan;649(8099):1237–43. DOI:10.1038/s41586-025-09922-y.
38. Hahnel M. What parts of the academic knowledge creation & dissemination pipeline can we automate with AI? OpenResearch.wtf [Internet]. 2026 Mar 23 [cited 2026 Apr 17]. Available from: https://www.openresearch.wtf/what-parts-of-the-academic-knowledge-creation-dissemination-pipeline-can-we-automate-with-ai/.
39. Shao E, Wang Y, Qian Y, Pan Z, Liu H, Wang D. SciSciGPT: advancing human–AI collaboration in the science of science. Nat Comput Sci. 2026 Mar;6(3):301–15. DOI:10.1038/s43588-025-00906-6.
40. Cornelio C, Ito T, Cory-Wright R, Dash S, Horesh L. The need for verification in artificial intelligence-driven scientific discovery. Philos Trans R Soc Math Phys Eng Sci. 2026 Apr 9;384(2317):20240591. DOI:10.1098/rsta.2024.0591.
41. Oatley K, Djikic M. Writing as Thinking. Rev Gen Psychol. 2008 Mar 1;12(1):9–27. DOI:10.1037/1089-2680.12.1.9.
42. Carroll AJ, Borycz J. Generative Artificial Intelligence. In: Essentials of STEM librarianship. Chicago: ALA Neal-Schuman; 2026. p. 143–60.
43. Placani A. Anthropomorphism in AI: hype and fallacy. AI Ethics. 2024 Aug 1;4(3):691–8. DOI:10.1007/s43681-024-00419-4.
44. Warner J. Writers Write [Substack newsletter]. The Biblioracle Recommends [Internet]. 2026 Apr 5 [cited 2026 Apr 21]. Available from: https://biblioracle.substack.com/p/writers-write.
45. Foster J. Language Models. Ingeniously Simple [Internet]. 2021 Oct 14 [cited 2023 Oct 6]. Available from: https://medium.com/ingeniouslysimple/language-models-15e45dce0805.
46. Shakespeare W. 3.3.97-98. In: Hamlet [Internet]. Washington, DC: Folger Shakespeare Library; [cited 2026 Apr 21]. Available from: https://www.folger.edu/explore/shakespeares-works/hamlet/read/3/3/.
47. Mollick E. Choosing to Stay Human [Internet]. 2026 Feb 18 [cited 2026 May 28]. Available from: https://www.oneusefulthing.org/p/choosing-to-stay-human.
48. Anderson H. BBC [Internet]. 2023 [cited 2026 May 28]. First Folio at 400: The Shakespeare words you don’t know you know. Available from: https://www.bbc.com/culture/article/20140527-say-what-shakespeares-words.
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Copyright (c) 2026 Alexander J. Carroll, AHIP, Jill T. Boruff, AHIP, Michelle Kraft, AHIP, FMLA

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