Generative AI as a replacement for Bloomberg information literacy instruction
Open accessAbstract
This study reports the results of an experiment conducted to measure the potential for generative AI to serve as an autonomous replacement for Bloomberg terminal information literacy instruction with a business librarian. The author of the study compared the performance and completion time on an assessment between a control group receiving traditional in-person Bloomberg instruction from a librarian against an experimental group taking an assessment with the assistance of Google Gemini in the absence of human instruction. Additional grounded theory thematic analysis was applied by the author to the transcripts of AI prompts and responses generated by the AI cohort for the identification of trends in subject interactions with the AI. Results showed better performance on the assessment by the control group, and a lack of strong statistical evidence supporting the hypothesis that current generative AI could effectively replace traditional human information literacy instruction for Bloomberg.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Carroll, A. J., & Borycz, J. (2024) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2024.102899 · Cited by 78 · see citing works
James, A., & Hampton Filgo, E. (2023) · College & Research Libraries News · doi:10.5860/crln.84.9.334 · Cited by 41 · see citing works
Ndungu, M. W. (2024) · Journal of Information Literacy · doi:10.11645/18.2.641 · Cited by 33 · cited 1× · see citing works
“In library information literacy instruction, the human-in-the-loop approach to AI dominates recent literature (Boyle, 2025; Carroll & Borycz, 2024; Del Castillo & Kelly, 2025; James & Hampton Filgo, 2023; Ndungu, 2024; Schmidt et al., 2025; Zhang, 2024).”
ChatGPT, Gemini, & Copilot: Using generative AI as a tool for information literacy instruction
Boyle, C. (2025) · The Reference Librarian · doi:10.1080/02763877.2025.2465416 · Cited by 12 · see citing works
Exploring Generative AI with ChatGPT for Possible Applications In Information Literacy Instruction
Zhang, L. (2024) · Journal of Electronic Resources Librarianship · doi:10.1080/1941126x.2024.2306058 · Cited by 8 · see citing works
Bloomberg Instruction in Academic Libraries: The Boons and the Challenges
Ottaviano, T. J. (2014) · Public & Access Services Quarterly · doi:10.1080/15228959.2014.904215 · Cited by 4 · see citing works
Becoming a leader in AI literacy instruction by not reinventing the wheel
Schmidt, L., Fruehauf, E., & Beman-Cavallaro, A. (2025) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2025.103117 · Cited by 3 · see citing works
Capstone Course Innovation: Bloomberg Professional Service
Payette, D. L., & Libertella, A. F. (2012) · American Journal of Business Education (AJBE) · doi:10.19030/ajbe.v5i3.7002 · Cited by 2 · see citing works
Teaching the Bloomberg Financial Information System Using Online Videos: An Action Research StudyPreprint
Godfrey, C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3552205 · Cited by 1 · see citing works
How to successfully advocate for a second business librarian
Price, E., & Schubert, C. (2024) · Ticker The Academic Business Librarianship Review · doi:10.3998/ticker.6332 · Cited by 1 · see citing works
The Emotional Intelligence Gap: Can AI Truly Replace Human Interaction in Library Services?
Samuel, S. (2025) · Journal of Information Resource Management (JIRM) · doi:10.61955/ekyqfy · Cited by 1 · see citing works
Eichinger, E., Oberhofer, V., Seifert, C., & Preis, S. J. (2025) · International journal on e-learning · doi:10.70725/516501dchyna · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Del Castillo, M. S., & Kelly, H. Y. (2025). Can AI become an information literacy ally? A survey of library instructor perspectives on ChatGPT. College & Research Libraries, 86(5), 797-816.
- Ross, C. (2020). Making sense of business reference: A guide for librarians and research professionals (2nd ed.). ALA Editions.
- West, T., Malin, M., Kakhkharov, J., Johnson, D., & Wong, V. (2021). Quantifying the use of Bloomberg in finance. Journal of Financial Education, 47(2), 35-50. JSTOR.
Cited by
Still being checked.
- Authors
- Deike, M.
- Affiliations
- University of Notre Dame (US)
- Corresponding author
- Deike, Michael
- Venue
- Journal of Business & Finance Librarianship
- Publisher
- Informa UK Limited
- Published
- 2026-03-02
- Volume
- 31
- Issue
- 4
- Pages
- 350-371
- ISSN
- 0896-3568 (ISSN-L)
- 1547-0644
- OpenAlex
- W7133891234
- Cited by
- 0
- References
- 12
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Business & Finance Librarianship (publishedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Library and Information Sciences › Library Science and Information Literacy
- Physical Sciences › Computer Science › Information Systems › Web and Library Services
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- generative AI, information literacy instruction, Bloomberg Terminal, Google Gemini, business librarians, AI in teaching, assessment performance, completion time
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 ›
Are you an author, or do you see a problem with this page? Request a review