Skip to main content
Have a personal or library account? Click to login
‘GenAI’ Literature Search Tools and Scholarly Diversity: An Algorithmic Ethnographical Analysis Cover

‘GenAI’ Literature Search Tools and Scholarly Diversity: An Algorithmic Ethnographical Analysis

Open Access
|Jun 2026

References

  1. Alonso, J. (2025, April 23). Collection of free education research may lose funding. Retrieved July 18, 2025, from Inside Higher Ed https://www.insidehighered.com/news/quick-takes/2025/04/23/collection-free-education-research-may-lose-funding
  2. Anis, S., & French, J. A. (2023). Efficient, explicatory, and equitable: Why qualitative researchers should embrace AI, but cautiously. Business & Society, 62(6), 11391144. 10.1177/00076503231163286
  3. Bendels, M. H. K., Müller, R., Brueggmann, D., & Groneberg, D. A. (2018). Gender disparities in high-quality research revealed by Nature Index journals. PLOS ONE, 13(1), e0189136. 10.1371/journal.pone.0189136
  4. Bjelobaba, S., Waddington, L., Perkins, M., Foltýnek, T., & Weber-Wulff, D. (2024). Research integrity and GenAI: A systematic analysis of ethical challenges across research phases. arXiv. 10.48550/arxiv.2412.10134
  5. Bol, J. A., Sheffel, A., Zia, N., & Meghani, A. (2023). How to address the geographical bias in academic publishing. BMJ Global Health, 8(12). 10.1136/bmjgh-2023-013111
  6. Bozkurt, A. (2024). GenAI et al.: Cocreation, authorship, ownership, academic ethics and integrity in a time of generative AI. Open Praxis, 16(1), 110. 10.55982/openpraxis.16.1.654
  7. Bozkurt, A. (2025). Algorithmically manufactured minds: Generative and agentic AI in a time of post-truth, reconfiguration of student agency and death of critical pedagogy. Open Praxis, 17(2), 206210. 10.55982/openpraxis.17.2.792
  8. Bozkurt, A., Xiao, J., Farrow, R., Bai, J. Y. H., Nerantzi, C., Moore, S., Dron, J., Stracke, C. M., Singh, L., Crompton, H., Koutropoulos, A., Terentev, E., Pazurek, A., Nichols, M., Sidorkin, A. M., Costello, E., Watson, S., Mulligan, D., Honeychurch, S., … Asino, T. I. (2024). The manifesto for teaching and learning in a time of generative AI: A critical collective stance to better navigate the future. Open Praxis, 16(4), 487513. 10.55982/openpraxis.16.4.777
  9. Byrnes, J., & Spear, A. (2023). Epistemic injustice and algorithmic epistemic injustice in healthcare. International Conference On Computer Ethics. Illinois Institute of Technology in Chicago, IL: International Conference on Computer Ethics, pp. 14. May 16–18, 2023, Chicago, IL, United States. https://journals.library.iit.edu/index.php/Soremoindex.php/CEPE2023/article/view/238
  10. Caliandro, A. (2018). Digital methods for ethnography: Analytical concepts for ethnographers exploring social media environments. Journal of Contemporary Ethnography, 47(5), 551578. 10.1177/0891241617702960
  11. Castelvecchi, D. (2016). Can we open the black box of AI? Nature News, 538(7623), 20. 10.1038/538020a
  12. Castillo-Segura, P., Alario-Hoyos, C., Kloos, C. D., & Fernandez Panadero, C. (2023). Leveraging the potential of generative AI to accelerate systematic literature reviews: An example in the area of educational technology. IEEE IFEES World Engineering Education Forum and Global Engineering Deans Council: Convergence for a Better World: A Call to Action, WEEF-GEDC 2023- Proceedings. October 23–27, 2023. Monterrey, Mexico. 10.1109/WEEF-GEDC59520.2023.10344098
  13. Cellard, L. (2022). Algorithms as figures: Towards a post-digital ethnography of algorithmic contexts. New Media & Society, 24(4), 9821000. 10.1177/14614448221079032
  14. Christin, A. (2020a). Algorithmic ethnography, during and after COVID-19. Communication and the Public, 5(3–4), 108111. 10.1177/2057047320959850
  15. Christin, A. (2020b). The ethnographer and the algorithm: Beyond the black box. Theory and Society, 49(5), 897918. 10.1007/s11186-020-09411-3
  16. Consensus. (n.d.). Less noise. More knowledge. Retrieved July 18, 2025, from https://consensus.app/home/about-us/
  17. Cox, A., & Abbott, P. (2021). Librarians’ perceptions of the challenges for researchers in Rwanda and the potential of open scholarship. Libri, 71(2), 93107. 10.1515/libri-2020-0036
  18. Czerniewicz, L., Goodier, S., & Morrell, R. (2017). Southern knowledge online? Climate change research discoverability and communication practices. Information, Communication & Society, 20(3), 386405. 10.1080/1369118X.2016.1168473
  19. Czerniewicz, L., & Wiens, K. (2013). The online visibility of South African knowledge: Searching for poverty alleviation. The African Journal of Information and Communication (Online), 13. 10.23962/10539/19274
  20. Decker, S. (2025, April 15). Guest post – The open access – AI conundrum: Does free to read mean free to train? The Scholarly Kitchen. Retrieved May 10, 2025, from https://scholarlykitchen.sspnet.org/2025/04/15/guest-post-the-open-access-ai-conundrum-does-free-to-read-mean-free-to-train/
  21. Dhingra, H., Jayashanker, P., Moghe, S., & Strubell, E. (2023). Queer people are people first: Deconstructing sexual identity stereotypes in large language models. arXiv. 10.48550/arXiv.2307.00101
  22. Dotson, K. (2014). Conceptualizing epistemic oppression. Social Epistemology, 28(2), 115138. 10.1080/02691728.2013.782585
  23. Elicit Help Center. (n.d.). Information and advice from the Elicit team. Retrieved July 18, 2025, from https://support.elicit.com/en/articles/552705
  24. Elsevier. (2025). Scopus AI: Explore. Focus. Advance. Retrieved July 18, 2025. from https://www.elsevier.com/en-gb/products/scopus/scopus-ai
  25. Fassinger, R., & Morrow, S. L. (2013). Toward best practices in quantitative, qualitative, and mixed-method research: A social justice perspective. Journal for Social Action in Counseling & Psychology, 5(2), 6983. 10.33043/jsacp.5.2.69-83
  26. Fischer, G., Lundin, J., & Lindberg, J. O. (2020). Rethinking and reinventing learning, education and collaboration in the digital age—from creating technologies to transforming cultures. International Journal of Information and Learning Technology, 37(5), 241252. 10.1108/IJILT-04-2020-0051
  27. Fricker, M. (2008). Forum: Miranda Fricker’s Epistemic injustice: Power and the ethics of knowing. Theoria, An International Journal for Theory, History and Foundations of Science, 23(1), 6971. 10.1387/theoria.7
  28. Gabriel, S. (2024). Generative AI and educational (in)equity. Proceedings of the International Conference on AI Research, ICAIR 2024, 4(1), 133142. December 5–6. 2024. Lisbon, Portugal. 10.34190/icair.4.1.3153
  29. Gerasimov, I., KC, B., Mehrabian, A., Acker, J., & McGuire, M. P. (2024). Comparison of datasets citation coverage in Google Scholar, Web of Science, Scopus, Crossref, and DataCite. Scientometrics, 129(7), 36813704. 10.1007/s11192-024-05073-5
  30. Gillespie, T. (2016). #Trendingistrending: When algorithms become culture. In R. Seyfert & J. Roberge (Eds.), Algorithmic Cultures: Essays on Meaning, Performance and New Technologies (pp. 5275). Routledge.
  31. Goyanes, M., Demeter, M., Bajić, N. S., & de Zúñiga, H. G. (2025). Gender disparities in first authorship: Examining the Matilda effect across communication, political science, and sociology. Scientometrics, 130(5), 29472961. 10.1007/s11192-025-05303-4
  32. Gupta, A., Atef, Y., Mills, A., & Bali, M. (2024). Assistant, parrot, or colonizing loudspeaker? ChatGPT metaphors for developing critical AI literacies. Open Praxis, 16(1), 3753. 10.55982/openpraxis.16.1.631
  33. Heck, T., Keller, C., & Rittberger, M. (2024). Coverage and similarity of bibliographic databases to find most relevant literature for systematic reviews in education. International Journal on Digital Libraries, 25(2), 365376. 10.1007/s00799-023-00364-3
  34. Hengel, E. (2022). Publishing while female: Are women held to higher standards? Evidence from peer review. The Economic Journal, 132(648), 29512991. 10.1093/ej/ueac032
  35. Hernandez, A. (2023). Epistemic oppression and affective exclusion: A pragmatist approach. Passion: Journal of the European Philosophical Society for the Study of Emotions, 1(2), 154168. 10.59123/passion.v1i2.13804
  36. Hill Collins, P. (2000). Black feminist thought: Knowledge, consciousness, and the politics of empowerment (2nd Ed.). Routledge.
  37. Jordan, K., & Tsai, S. P. (2024). Keywords, citations and ‘algorithm magic’: Exploring assumptions about ranking in academic literature searches online. Learning, Media and Technology, 115. 10.1080/17439884.2024.2392108
  38. Kay, J., Kasirzadeh, A., & Mohamed, S. (2025). Epistemic injustice in generative AI. In S. Das, B. P. Green, K. Varshney, M. Ganapini, & A. Renda (Eds.), Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, pp. 684697. October 21–23, 2024. San Jose, California, USA. Available at: https://dl.acm.org/doi/10.5555/3716662.3716722
  39. Khisro, J., & Fenlon, K. (2025). Equity in public access to scientific research results: Insights from federal agency responses to the Nelson Memorandum Policy. Proceedings of the 58th Hawaii International Conference on System Sciences, pp. 21442153. January 7–10. 2025. Big Island, Hawaii. 10.24251/hicss.2025.263
  40. Kulczycki, E. (2023). The evaluation game: How publication metrics shape scholarly communication. Cambridge University Press. 10.1017/9781009351218
  41. Kumar, S., & Gunn, A. (2024). Doctoral students’ reflections on generative artificial intelligence (GenAI) use in the literature review process. Innovations in Education and Teaching International, 62(4), 13951408. 10.1080/14703297.2024.2427049
  42. Larivière, V., Ni, C., Gingras, Y., Cronin, B., & Sugimoto, C. R. (2013). Bibliometrics: Global gender disparities in science. Nature, 504(7479), 211213. 10.1038/504211a
  43. Li, M., Enkhtur, A., Yamamoto, B. A., Cheng, F., & Chen, L. (2025). Potential societal biases of ChatGPT in higher education: A scoping review. Open Praxis, 17(1), 7994. 10.55982/openpraxis.17.1.750
  44. Marshall, G., & Jonker, L. (2010). An introduction to descriptive statistics: A review and practical guide. Radiography, 16(4), e1e7. 10.1016/j.radi.2010.01.001
  45. Mei, K. X., Fereidooni, S., & Caliskan, A. (2023). Bias against 93 stigmatized groups in masked language models and downstream sentiment classification tasks. Proceedings of the 2023 ACM Conference on Fairness Accountability and Transparency, pp. 16991710. June 12–15. 2023. Chicago, USA. 10.1145/3593013.3594109
  46. Mills, D., Kingori, P., Branford, A., Chatio, S., Robinson, N., & Tindana, P. (2023). Who counts? Ghanaian academic publishing and global science. African Minds. 10.47622/9781928502647
  47. Mozelius, P., & Humble, N. (2024). On the use of Generative AI for literature reviews: An exploration of tools and techniques. Proceedings of the European Conference on Research Methodology for Business and Management Studies, pp. 161168. July 4–5. 2024. Porto, Portugal. 10.34190/ecrm.23.1.2528
  48. Onken, J., Chang, L., & Kanwal, F. (2021). Unconscious bias in peer review. Clinical Gastroenterology and Hepatology, 19(3), 419420. 10.1016/j.cgh.2020.12.001
  49. Oza, A. (2023, December 22). Citations show gender bias—And the reasons are surprising. Nature. 10.1038/d41586-023-03474-9
  50. Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G., & Granka, L. (2007). In google we trust: Users’ decisions on rank, position, and relevance. Journal of Computer-Mediated Communication, 12(3), 801823. 10.1111/j.1083-6101.2007.00351.x
  51. Pollock, D., & Michael, A. (2024, December 10). News and Views: How much content can AI legally exploit? Delta Think. Retrieved May 10, 2025, from https://www.deltathink.com/news-and-views-how-much-content-can-ai-legally-exploit
  52. Ray, K. S., Zurn, P., Dworkin, J. D., Bassett, D. S., & Resnik, D. B. (2024). Citation bias, diversity, and ethics. Accountability in Research, 31(2), 158172. 10.1080/08989621.2022.2111257
  53. ResearchRabbit. (2025). Reimagine research. Retrieved July 18, 2025, from https://www.researchrabbit.ai
  54. Rycroft-Smith, L., & Macey, D. (2025). All sizzle, no steak: AI tools are not able to act as credible knowledge brokers by summarising evidence in mathematics education. In T. Fujita (Ed.), Proceedings of the British Society for Research into Learning Mathematics, pp. 16. March 1, 2025. Online. https://bsrlm.org.uk/wp-content/uploads/2025/05/BSRLM-CP-45-1-10.pdf
  55. Schoeb, D., Suarez-Ibarrola, R., Hein, S., Dressler, F. F., Adams, F., Schlager, D., & Miernik, A. (2020). Use of artificial intelligence for medical literature search: Randomized controlled trial using the hackathon format. Interactive Journal of Medical Research, 9(1), e16606. 10.2196/16606
  56. Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 205395171773810. 10.1177/2053951717738104
  57. Selwyn, N. (2022). The future of AI and education: Some cautionary notes, European Journal of Education, 57(4), 620631. 10.1111/ejed.12532
  58. Semantic Scholar. (n.d.). Our product. Retrieved July 18, 2025. from https://www.semanticscholar.org/product
  59. Spillias, S., Tuohy, P., Andreotta, M., Annand-Jones, R., Boschetti, F., Cvitanovic, C., Duggan, J., Fulton, E. A., Karcher, D. B., Paris, C., Shellock, R., & Trebilco, R. (2024). Human-AI collaboration to identify literature for evidence synthesis. Cell Reports Sustainability, 1(7), 100132. 10.1016/j.crsus.2024.100132
  60. Tlili, A., Bond, M., Bozkurt, A., Arar, K., Chiu, T. K. F., & Rospigliosi, P. ‘Asher’. (2025). Academic integrity in the generative AI (GenAI) era: A collective editorial response. Interactive Learning Environments, 33(3), 18191822. 10.1080/10494820.2025.2471198
  61. von Eschenbach, W. J. (2021). Transparency and the black box problem: Why we do not trust AI. Philosophy & Technology, 34(4), 16071622. 10.1007/s13347-021-00477-0
  62. Wanyama, S. B., McQuaid, R. W., & Kittler, M. (2022). Where you search determines what you find: The effects of bibliographic databases on systematic reviews. International Journal of Social Research Methodology, 25(3), 409422. 10.1080/13645579.2021.1892378
  63. Wellmon, C., & Piper, A. (2017). Publication, power, and patronage: On inequality and academic publishing. Critical Inquiry. 10.6084/M9.FIGSHARE.4558072.V1
  64. White, D. (2025, March 11). Agency vs Efficiency (The AI learning gambit). David White: Digital and Education. Retrieved March 13, 2025, from https://daveowhite.com/aigambit/
  65. Whitfield, S., & Hofmann, M. A. (2023). Elicit: AI literature review research assistant. Public Services Quarterly, 19(3), 201207. 10.1080/15228959.2023.2224125
  66. Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8(10), 18391850. 10.1038/s41562-024-02004-5
  67. Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90112. 10.1111/bjet.13370
Language: English
Page range: 291 - 309
Submitted on: Jul 31, 2025
Accepted on: Mar 5, 2026
Published on: Jun 2, 2026
Published by: International Council for Open and Distance Education (ICDE)
In partnership with: Paradigm Publishing Services

© 2026 Kathy M. Chandler, Katy Jordan, Ishaq Al-Naabi, Panagiota Tzanni, Leone Gately, published by International Council for Open and Distance Education (ICDE)
This work is licensed under the Creative Commons Attribution 4.0 License.