Conference Agenda

This study examines the relationship between Information Literacy (IL) and the use of Artificial Intelligence (AI), specifically ChatGPT, in the teaching-learning processes of university students in Social Sciences, particularly in Education, and Information and Documentation. The study involved a sample of final-year undergraduate students (N=45) from the University of Granada. A qualitative-descriptive methodology was employed, utilizing a focus group with think-aloud techniques and semi-structured interviews, complemented by a quantitative questionnaire based on a Likert scale, developed ad hoc.

Both quantitative and qualitative results were compared to verify the consistency of the information provided by students. The findings reveal the following: first, AI is becoming increasingly widespread in university teaching-learning processes; second, students use ChatGPT primarily as a tool for information retrieval but often without clearly defining their questions or information needs, nor thoroughly verifying the obtained results; third, students find ChatGPT useful for processing information, such as summarizing texts, selecting keywords, categorizing, and relating content, yet they sometimes struggle with managing references, finding it confusing and inaccurate; fourth, there appears to be a correlation between increased use of ChatGPT and a decline in students’ critical attitude and evaluation of information sources, particularly regarding content reliability. Lastly, students use ChatGPT to assist in communicating information, particularly in structuring, writing, and styling class assignments and reports, yet they rarely verify the validity or reliability of bibliographic sources. The use of AI has clear implications for university faculty and librarians, as it necessitates training initiatives that support adaptation to new tools.

In conclusion, students express a strong need for more training in AI tools, proper use of ChatGPT, and faculty familiarization with this technology for its integration into teaching-learning processes, particularly in the development of information literacy competencies.

Introduction

Most research on AI-generated content detection focuses on technological tools (Bellini et al., 2024; Elkhatat et al., 2023; Weber-Wulff et al., 2023), with less attention to users’ ability to distinguish AI from human-created material (Frank et al., 2024). Studies suggest that users struggle with this task, particularly when AI content is high quality (Boutadjine et al., 2024; Nguyen & Barrot, 2024). Developing AI literacy in content recognition is therefore crucial for educators. A key step in this process is self-assessment, which helps users identify gaps in their discernment skills. This study addresses identified research gaps by examining Polish students’ perceptions of their AI literacy, particularly their confidence in identifying AI-generated text, images, and videos.

Method

A survey with a 5-point Likert scale was conducted among undergraduate students (n=350). Participants assessed 32 statements on AI tool usage, exposure to AI-generated content, recognition ability, and online information evaluation. Data were analyzed quantitatively using Google Forms.

Results

Over 80% of respondents reported encountering AI-generated content in the media, and more than 70% felt confident in recognizing AI-generated videos. Despite this, students rarely use verification tools, suggesting a possible overestimation of their abilities. This overconfidence may stem from cognitive bias, as research indicates distinguishing AI-generated from human-created content is highly challenging. Most respondents also support labeling AI-generated content, recognizing the difficulty of distinguishing it from human-created material.

Conclusion

Students express high confidence in detecting AI-generated content, yet their actual ability remains uncertain. Given the difficulty of distinguishing AI from human-created material, this confidence warrants further investigation. Future research will compare self-assessments with empirical tests to assess users’ actual competency and potential causes of high confidence in discernment skills.

References

Bellini, V., Semeraro, F., Montomoli, J., Cascella, M., & Bignami, E. (2024). Between human and AI: assessing the reliability of AI text detection tools. Current Medical Research and Opinion, 40(3): 353–358.

Boutadjine, A., Harrag, F., & Shaalan, K. (2024). Human vs. machine: A comparative study on the detection of AI-generated content. ACM Transactions on Asian and Low-Resource Language Information Processing.

Elkhatat, A. M., Elsaid, K., & Almeer, S. (2023). Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. International Journal for Educational Integrity, 19(1): 17.

Frank, J. et al. (2024). A representative study on human detection of artificially generated media across countries. In Proceedings of the 45th IEEE Symposium on Security and Privacy SP 2024 (pp. 55–73). San Francisco: IEEE.

Nguyen, L., & Barrot, J. S. (2024). Detecting and assessing AI-generated and human-produced texts: The case of second language writing teachers. Assessing Writing, 62, 100899.

Weber-Wulff, D., et al. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), 26.

The contemporary information ecosystem is increasingly complex, requiring individuals to navigate vast amounts of information across multiple media platforms while distinguishing reliable sources from misinformation and disinformation. The prevalence of disinformation highlights the need for proactive measures, including public awareness campaigns and comprehensive educational strategies. The literature emphasizes the necessity of enhancing critical evaluation skills, fostering analytical thinking, and reinforcing civic engagement, autonomy, and creativity. Moreover, policymakers stress the importance of integrating validated information literacy strategies into education to cultivate critical thinkers (European Commission, 2016). While academic libraries have undertaken initiatives to counter disinformation (Antunes et al., 2021), further research is required to assess their effectiveness. Addressing this challenge necessitates recognizing the centrality of information literacy in higher education. Students must not only navigate the stages of information retrieval but also critically assess their own competencies in this domain. This study investigated university students’ perceptions of their information literacy skills using the Student Perceptions of Information Literacy Skills (PILS) test (Doyle et al., 2019; Foster et al., 2018).

We translated the instrument into European Portuguese and validated it with students across various academic disciplines and levels. We adopted a mixed-methods approach, combining quantitative statistical analyses of survey responses with qualitative thematic analysis of open-ended reflections. Our study examined students perceived ability to engage with the information and disinformation landscape, drawing on the conceptual knowledge, skills, and dispositions outlined in the Framework for Information Literacy in Higher Education (Association of College and Research Libraries, 2016). Beyond identifying key challenges, we discussed practical strategies for integrating information literacy within curricula, the role of faculty in fostering critical engagement and the potential for collaboration between academic libraries and educators. Additionally, we emphasized the need for long-term evaluation mechanisms to measure the sustained impact of information literacy initiatives. Given the rapidly evolving digital landscape and the increasing sophistication of online misinformation, this study contributed to contemporary debates on digital literacy, advocating for interdisciplinary and adaptive approaches. Based on its findings, we proposed recommendations to strengthen information literacy instruction and the role of academic libraries in fostering students’ critical engagement with information. This research is particularly relevant in higher education, serving as a foundation for enhancing pedagogical practices, especially in the Portuguese context.

References

Antunes, M. L., Lopes, C., & Sanches, T. (2021). Como combater as fake news através da literacia da informação? Desafios e estratégias formativas no ensino superior. BiD, 46(46). https://doi.org/10.1344/BID2020.46.15

Association of College and Research Libraries. (2016). Framework for Information Literacy for Higher Education. Retrieved 28 August, 2025 from http://www.ala.org/acrl/files/issues/infolit/framework.pdf.

Doyle, M., Foster, B., & Yukhymenko-Lescroart, M. A. (2019). Initial development of the perception of information literacy scale (PILS). Communications in Information Literacy, 13(2): 205–227. https://doi.org/10.15760/comminfolit.2019.13.2.5

European Commission. (2016). Research for Cult Committee - Promoting media and information literacy in libraries: In depth analysis. Retrieved 28 August, 2025 from http://www.europarl.europa.eu/supporting-analyses

Foster, B., Doyle, M., & Yukhymenko, M. (2018). Student Perceptions of Information Literacy Skills (PILS) using the ACRL’s Framework for Information Literacy for Higher Education. Retrieved 28 August, 2025 from https://goo.gl/AFYlFv

Introduction

In today’s learning environment, students must employ effective self-regulation strategies when seeking academic sources of help (Karabenick & Newman, 2009; Fong, Gonzales, Hill-Troglin Cox, & Shinn, 2023). Completing academic assignments typically requires both formal and informal sources of support (Karabenick & Knapp, 1988) as well as engagement in the information search process (Kuhlthau, 1991). Both the information search and help-seeking models focus on bridging knowledge gaps and guiding decision-making (Giblin, Stefaniak, Eckhoff, & Luo, 2021).

Objectives

In this study I examined the information searching habits of Czech undergraduate and graduate students in STEM fields. I included an analysis of reported help-seeking behaviors at the point where students started writing their required theses. The main objective of this study was to gather initial data on how students’ information behaviors influenced their decision-making processes when seeking the academic help in 2020, during the COVID crisis.

Methodology

I conducted a quantitative analysis of data gathered in a 2020 questionnaire survey conducted at NTK (Firsova, Millerová, Martinová, Chodounská, & Šátková, 2022). The dataset for this study included responses from 697 undergraduate and graduate students from various public universities in the Czech Republic. I applied several statistical methods, including binomial logistic regression, to estimate the likelihood of seeking specific sources of academic help. I analyzed the differences in the results for different student characteristics such as level of study and affiliation.

Outcomes

The results indicated that students’ information seeking practices directly influenced their help-seeking behaviors. Students accustomed to using high-quality information sources were more likely to seek formal help and less likely to rely on informal peer support. These results indicated that systematically developing the habit of using reliable information sources likely contributes to more effective help-seeking strategies for Czech undergraduate and graduate students.

References

Firsova, N., Millerová, K., Martinová, O., Chodounská, A. & Šátková, B. (2022). Vyhodnocení průzkumu Národní technické knihovny “NTK pro příští desetiletí”. Prague: National Library of Technology. Retrieved 21 January, 2025 from https://doi.org/10.48813/ht17-zm78

Fong, C. J., Gonzales, C., Hill-Troglin Cox, C., & Shinn, H. B. (2023). Academic help-seeking and achievement of postsecondary students: A meta-analytic investigation. Journal of Educational Psychology, 115(1): 1–21. https://doi.org/10.1037/edu0000725

Giblin, J., Stefaniak, J., Eckhoff, A., & Luo, T. (2021). An exploration of factors influencing the decision-making process and selection of academic help sources. Journal of Computing in Higher Education, 33(1): 1–18. https://doi-org.ezproxy.techlib.cz/10.1007/s12528-020-09252-0

Karabenick, S. A., & Knapp, J. R. (1988). Help seeking and the need for academic assistance. Journal of Educational Psychology, 80(3): 406-408. US: American Psychological Association.

Karabenick, S. A., & Newman, R. S. (2009). Seeking help: Generalizable self-regulatory process and social-cultural barometer. Contemporary motivation research: From global to local perspectives (pp. 25–48). Hogrefe & Huber Publisher.

Kuhlthau, C. C. (1991). Inside the search process: Information seeking from the user’s perspective. Journal of the American Society for Information Science, 42(5): 361–371.

Emotional labour has gained recognition in librarianship in recent years. Every day, librarians work with angry, stressed or distraught patrons who are striving to meet deadlines, struggling to find the information sources that they need, or battling with writing and publishing. Reseach consultations with library users and encounters with overworked coworkers and leadership can all require us to manage our emotions to achieve a desired result. Arlie Hochschild (1983) defined emotional labour as “the management of feeling to create a publicly observable facial and bodily display” (p. 7). Teaching librarians engage in emotional labour on a regular basis (Julien & Genius, 2009). We encourage, support, persuade, comfort, empower and embolden students and researchers through teaching and tutoring. We also strive to make material that we may have been teaching for years seem fresh and exciting for each new audience. Adding AI resources and methods to our instruction can add another source of emotional labour to our work as both library users and teaching librarians may have emotions to manage around the role these tools play in our practice (Ringnes et al., 2024).

Understanding the emotional labour involved in teaching information literacy can help librarians do this work both more effectively and more sustainably, decreasing risk of burnout.

Purpose

Our purpose is threefold: to increase the awareness of how emotional labour affects academic librarians in their teaching roles; to inspire teaching librarians to think critically about situations that require emotional labour; and to provide evidence-based management strategies.

Methodology

The presentation seeks to present findings from a scoping review about emotional labour for teaching librarians. The review work is in process. The review is based on the framework by Arksey & O’Malley (2005).

Outcome

Emotional labour can negatively impact teaching librarians’ mental and physical health, and several studies have linked emotional labour to burnout and low morale among library workers (Benjes-Small, 2023; Kendrick, 2017; Matteson & Miller, 2013). It is therefore important that teaching librarians understand the concepts of emotional labour, and have strategies to deal with it. The paper addresses the particular experience of emotional labour among teaching librarians and the presentation will give an overview of findings as well as practical tips for doing emotional labour in a more sustainable way.

References

Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1): 19–32. https://doi.org/10.1080/1364557032000119616

Benjes-Small, C. (2023). Beyond self-care: Forging sustainable practices in academic librarianship. Paper presented at the ACRL2023: Forging the future, Pittsburgh, PA. Retrieved 28 August, 2025 from https://scholarworks.wm.edu/librariespubs/98

Hochschild, A. R. (1983). The Managed Heart: Commercialization of human feeling. University of California Press.

Julien, H., & Genuis, S. K. (2009). Emotional labour in librarians’ instructional work. Journal of Documentation, 65(6): 926–937. https://doi.org/10.1108/00220410910998924

Kendrick, K. D. (2017). The low morale experience of academic librarians: A phenomenological study. Journal of Library Administration, 57(8): 846–878. https://doi.org/10.1080/01930826.2017.1368325

Matteson, M. L., & Miller, S. S. (2013). A study of emotional labor in librarianship. Library & Information Science Research, 35(1): 54–62. https://doi.org/10.1016/j.lisr.2012.07.005

Ringnes, H. K., Kannelønning, M. S., & Gasparini, A. A. (2024). AI and emotions: researchers’ emotional reactions when interacting with AI tools. Creating Knowledge. Nordic Network for Information Literacy.