Conference Agenda

Bibliometrics is the study of bibliographic data to identify trends in publications, often examined within a particular discipline or geographic area. For example, an ongoing study of publication patterns of United States academic librarians spanning the years 1993 to 2017 has set the standard for library and information science (LIS) bibliometric studies of author characteristics and productivity in the United States, including rankings of institutions by productivity (Wiberley, Blecic, De Groote & Schultz, 2023). Another study of trends in LIS publishing across European countries between 2003 and 2012 found that English was the most widely used language followed by German, Spanish and French, with researchers in the United Kingdom, Spain and Germany producing the highest number of papers (Olmeda-Gómez & de Moya-Anegón, 2015). A study of LIS research in the 22 countries of the Arab League from 1951 to 2021 found a trend of increased research productivity among these countries, particularly from 2018 (Siddique, Rehman, Ahmad, Abbas & Khan, 2021). These studies provide insight to authors regarding expectations around individual and institutional productivity across the discipline, help authors identify potential publication venues for future research, and provide opportunities for institutional benchmarking.

The current study seeks to add to the international literature of bibliometric studies by emphasizing information literacy research, rather than the broader category of LIS research, and expanding to include a broader geographic range. This study examines global publication patterns of scholarly articles related to information literacy in the five-year period from 2020 through 2024. Multiple bibliometric indicators are examined for insight into researcher productivity and journal characteristics among those journals consistently publishing information literacy research, as well as identifying information literacy themes among published research. Article metadata was retrieved from the Web of Science platform and supplemented with metadata from the Scopus platform. In order to be included in this study, journals must be peer-reviewed and must have published ten or more articles related to information literacy from 2020 to 2024. The authors retrieved article metadata for individual articles meeting the search criteria and performed title and abstract screening to confirm that each article met the inclusion criteria, namely that the article was an original scholarly article and that the content and topic were relevant to information literacy. More than 1000 articles by close to 2000 unique authors were identified for bibliometric and thematic analysis. Specific variables studied include productivity (by country and institution), journal characteristics (such as language, country, and publishing model), author characteristics (such as country and institutional affiliation), characteristics of author collaborations, and information literacy subthemes. Initial results reinforce the dominance of publications based in the United Kingdom and United States of America in information literacy publishing; however, highly productive authors are producing information literacy research from multiple additional countries. This study will highlight geographic disparities and emerging areas of research, as well as inform authors’ strategic decisions about researching and publishing on information literacy topics.

References

Olmeda-Gómez, C., & de Moya-Anegón, F. (2016). Publishing trends in library and information sciences across European countries and institutions. The Journal of Academic Librarianship, 42(1): 27–37. https://doi.org/10.1016/j.acalib.2015.10.005

Siddique, N., Ur Rehman, S., Ahmad, S., Abbas, A., & Khan, M. A. (2023). Library and information science research in the Arab World: A bibliometric analysis 1951–2021. Global Knowledge, Memory and Communication, 72(1/2): 138–159. https://doi.org/10.1108/GKMC-06-2021-0103

Wiberley, S. E., Blecic, D. D., De Groote, S. L., & Shultz, M. (2023). Publication patterns of US academic librarians and libraries, 2013–2017 with comparison to preceding studies. College & Research Libraries, 84(4): 545. https://doi.org/10.5860/crl.84.4.545

“Learning Nuggets” – compact, thematically focused learning units addressing specific academic needs – serve as a cornerstone of information literacy initiatives within university libraries. As academic environments increasingly depend on digital tools and methodologies, universities face the challenge of equipping students and researchers with essential competencies to thrive in this evolving landscape. Libraries play a pivotal role by providing tailored resources and training, bridging gaps in digital literacy and academic competence.

These nuggets encompass diverse formats, including videos, infographics, quizzes, and short tutorials, enabling learners to efficiently develop key academic and technical skills (Samala et al., 2023). Beyond traditional topics like literature research, citation standards and data management, Learning Nuggets integrate advanced learning experience design and emphasize the toolification of scientific workflows. This includes instruction on reference management systems such as Zotero, collaborative platforms and Artificial Intelligence (AI)-driven tools for research automation. Additionally, they provide insights into digitalization and AI applications in research contexts.

The design of Learning Nuggets draws upon principles of microlearning, gamification, and active learning, promoting engagement and retention. Short, interactive modules cater to varied learning styles, allowing participants to customize their learning journey. Depending on content, Learning Nuggets can stand alone or be integrated within longer learning paths (Kadhem, 2017). By merging technical proficiency and academic skill-building, Learning Nuggets empower users to navigate the increasingly tool-dependent scholarly landscape. Their modular structure promotes independent, self-regulated learning, strengthening the library’s central role in academic teaching and research.

Employing a single case study methodology, this study presents the design and implementation of Learning Nuggets and toolification training at a Technical University’s scientific library. Leveraging educational-design research (McKenney & Reeves, 2014) and principles of learning experience design (Tawfik et al., 2022), the study identifies best practices for integrating modular, tool-focused content into library services. Findings offer actionable insights into resource allocation, user engagement, ongoing support, and iterative improvements, ensuring Learning Nuggets remain accessible, effective, and aligned with diverse user goals and skill levels.

References

Kadhem, H. (2017). Using mobile-based micro-learning to enhance students: Retention of IT concepts and skills. In 2nd International Conference on Knowledge Engineering and Applications, ICKEA 2017, Institute of Electrical and Electronics Engineers Inc. (pp. 128–132). https://doi.org/10.1109/ICKEA.2017.8169915.

McKenney, S., & Reeves, T. C. (2014). Educational design research. In Spector, J., Merrill, M., Elen, J., Bishop, M. (Eds.), Handbook of Research on Educational Communications and Technology. New York, NY: Springer. https://doi.org/10.1007/978-1-4614-3185-5_11.

Samala, A. D., Bojic, L., Bekiroğlu, D., Watrianthos, R., & Hendriyani, Y. (2023). Microlearning: Transforming education with bite-sized learning on the go – insights and applications. International Journal of Interactive Mobile Technologies (iJIM), 17(21): 4–24. https://doi.org/10.3991/ijim.v17i21.42951.

Tawfik, A.A., Gatewood, J., Gish-Lieberman, J.J. et al. (2022). Toward a definition of learning experience design. Tech Know Learn, 27: 309–334. https://doi.org/10.1007/s10758-020-09482-2.

Imagine a university student, stuck on a writing assignment, turning to a Generative Artificial Intelligence (GenAI) tool like ChatGPT or Copilot out of frustration. As the student types: “Write a paper about...” before the student finishes typing the sentence a helpful pop-up on the side of the chat box reads: “Need some ideas? Try the following prompts?” The pop-up then suggests three prompts that correspond to common learning objectives as stated in writing courses like “provide a list of pivotal authors on this topic,” “identify some gaps in the research on this topic,” or “help me plan my research.” The student will then be reminded of a classroom lecture about prompt engineering in which the instructor discussed which kinds of AI prompts are acceptable and which are not. While some institutions of higher education have initiated policies that allow the use of GenAI in some capacity, training instructors and students on its use for educational purposes is still in its infancy. Some instructors of writing intensive courses fear that student use of GenAI tools will undermine the writing process and students’ ability to achieve the course learning outcomes and even lead to the downfall of creative human expression, the redefining of authorship, and the elimination of professions that involve writing (Baron, 2023). These fears are not unfounded as there have been increasing incidents of university students relying on GenAI in ways that do not help them achieve learning outcomes and stretch the boundaries of academic integrity (Goel & Nelson, 2024; Sweeney, 2023). Other authors have argued that aspects of writing pedagogy and assessment should be re-evaluated and updated to circumvent writing tasks that can be easily produced by GenAI software (Lodge, Thompson, & Corrin, 2023; Shah, 2023; Walter, 2024). In this paper, the authors take the position that writing pedagogy and assessment must change to adapt to the current environment in which students use GenAI to assist in their writing tasks. However, they contend that the creation of a tool such as the one described above, as well as instructor training on the usefulness of GenAI, requires the creation of a new learning taxonomy similar to that of Bloom’s Taxonomy. This new taxonomy should be redefined for the current technological age and should incorporate information literacy skills that have been advanced by librarians since the coming of the information age. As information specialists in higher education, the authors will design a framework for a prompt engineering taxonomy that can be used to train instructors and students alike. This presentation will outline their rationale, approach, and share the findings of their preliminary research.

References

Baron, N. S. (2023). Who Wrote This? How AI and the lure of efficiency threaten human writing. Stanford, California: Stanford University Press.

Goel, R. K., & Nelson, M. A. (2024). Do college anti-plagiarism/cheating policies have teeth in the age of AI? Exploratory evidence from the Internet. Managerial and Decision Economics, 45(4): 2336–2347. Retrieved 2 February, 2025 from https://onlinelibrary.wiley.com/doi/abs/10.1002/mde.4139

Lodge, J. M., Thompson, K., & Corrin, L. (2023). Mapping out a research agenda for generative artificial intelligence in tertiary education. Australasian Journal of Educational Technology, 39(1): 1–8. Retrieved 29 January, 2025 from https://ajet.org.au/index.php/AJET/article/view/8695

Shah, P. (2023). AI and the Future of Education. Hoboken, New Jersey: Jossey-Bass/John Wiley & Sons, Inc. Retrieved 16 January, 2025 from https://learning.oreilly.com/library/view/ai-and-the/9781394219247/

Sweeney, S. (2023). Who wrote this? Essay mills and assessment – Considerations regarding contract cheating and AI in higher education. The International Journal of Management Education, 21(2), 100818. Retrieved 2 February, 2025 from https://www.sciencedirect.com/science/article/pii/S1472811723000563

Walter, Y. (2024). Embracing the future of Artificial Intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(1): 15. Retrieved 29 January, 2025 from https://doi.org/10.1186/s41239-024-00448-3