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 & 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.

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.

Abstract

Although artificial intelligence (AI) is still perceived as a novelty issue across numerous sciences, there already exist a plethora of studies dedicated to AI-generated content detection. Most of them apply a technology-oriented approach, focused on specific AI detection tools (Bellini et al. 2024; Elkhatat et al. 2023; Weber-Wulff et al. 2023). However, when it comes to information users, there is still a significant gap, concerning people’s ability to assess whether given content is created by AI technology or not (Frank et al. 2024). As observed by Boutadjine et al. (2024) and Frank et al. (2024), AI-content detection seems to be a challenge for information users, especially when a fake material is of good quality (Nguyen & Barrot 2024). Therefore, developing users’ artificial intelligence literacy in the context of recognizing AI-generated content is an urgent need for all types of educators. For these educational activities to be effective it is particularly important to make users realize the need to acquire that set of skills. Self-assessment of discernment abilities in relation to AI- generated content could be a first step towards deepening users’ understanding of a need for AI literacy education. Our study attempts to fill identified gaps by delivering baseline data on Polish students’ perception of their own artificial intelligence literacy, with special emphasis on identifying AI-generated content. The main objective of the paper is to assess to what extent students feel confident about their ability to identify AI-generated material (text, image, video). A survey method based on Likert’s scale’s questionnaire was applied to collect data among a group of undergraduate students (n=350). The respondents were asked to assess 32 statements regarding: i) general usage of AI tools, ii) encountering AI-generated content in media, iii) ability to recognize AI-generated content and iv) evaluation of the quality of online information. In the next step, Google Forms tool was used to conduct quantitative analysis of the gathered data. The obtained results show that the majority of respondents declare to encounter AI-generated content in media (over 80%), and most of them perceive their competency of detecting AI-generated material as being quite high (e.g. over 70% of students declare that they can recognize AI-generated videos). At the same time, respondents report that they do not use any tools to verify AI-generated content regularly and they do not always evaluate the quality of online information. Based on the results obtained we conclude that respondents rate their ability to detect AI-generated content highly, indicating a strong sense of confidence in these skills. However, in order to reveal an actual level of students’ AI-detection ability it is important to compare these results with findings derived from empirical research on AI-generated content recognition, which is a plan of the authors’ future study.

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, Just Accepted (December 2024).

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., Herbert, F., Ricker, J., Schönherr, L., Eisenhofer, T., Fischer, A., ... & Holz, T. (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., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., ... & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), 26.

Today’s information ecosystem is increasingly demanding. It involves navigating information across multiple media, produced at an overwhelming rate, with a pressing need to distinguish accurate information from misinformation and disinformation. The increasing prevalence of disinformation requires proactive measures, including public awareness campaigns and widespread education. The literature emphasizes the need to improve the ability to evaluate information sources, foster critical thinking to combat disinformation and strengthen citizenship, individual autonomy, and creative potential. It also calls on policymakers to prioritize these objectives in their agendas, advocating the training of proactive critical thinkers through validated information literacy strategies (European Commission, 2016).

While studies highlight the commitment of academic libraries to implementing policies and projects to combat disinformation (Antunes et al., 2021), there is still a need for in-depth research. Addressing this issue requires recognizing the centrality of these skills for academic students. Therefore, students need to be aware of the stages of information retrieval and, at the same time, of their skills in this field. The present study explores the perceptions of academic students about their information literacy. The Student Perceptions of Information Literacy Skills test [PILS] (Doyle et al., 2019; Foster et al., 2018) has proven to be an appropriate way to learn about students’ perceptions on this topic. The translation of the instrument into European Portuguese was validated with academic students from different learning levels and subject areas. The aim was to analyze students' perceptions of their responses to the information/disinformation ecosystem, also using skills, conceptual knowledge and dispositions, regarding the six frameworks of the Framework for Information Literacy in Higher Education (Association of College and Research Libraries, 2016). Therefore, this study could be essential in higher education, acting as a lever to improve pedagogical practices, especially in the Portuguese context. From the diagnosis developed in this research, some recommendations for teaching activity in IL as well as implications for academic libraries are provided.

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. 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. 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. https://goo.gl/AFYlFv

Introduction

In today's learning environment, students must employ effective self-regulation strategies when seeking academic source of help (Karabenick & Newman, 2009). 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 processes (Giblin, Stefaniak, Eckhoff, & Luo, 2021).

Objectives

The present study examines the information searching habits of undergraduate and graduate students, specifically their use of information sources for academic assignments and their help-seeking behavior at the beginning of the thesis-writing process. The main objective is to analyze how students’ information behavior influences their decision-making process when seeking the source of academic help. The study focuses primarily on the Czech undergraduate and graduate students in STEM fields.

Methodology

This study applies a quantitative research approach, utilizing data from a questionnaire survey conducted at NTK between June and July 2020 (Firsova, Millerová, Martinová, Chodounská, & Šátková, 2022). One of the primary objectives of the survey was to examine students’ study habits and preferences regarding sources of academic help. The dataset for this study comprises responses from 697 undergraduate and graduate students. To address the study’s objectives, several statistical methods were applied, including binomial logistic regression to estimate the likelihood of seeking specific sources of academic help. Additionally, the study discusses differences in the results based on students’ characteristics, such as level of study and affiliation.

Outcomes

The results indicate that students’ information practices directly influence their help-seeking behavior. Although academic libraries are not always the most obvious source of assistance, students accustomed to using high-quality information sources are more likely to seek formal help and less likely to rely on informal peer support. Thus, systematically developing habits of using reliable information sources contributes to more effective help-seeking strategies.

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 January 21, 2025, from https://doi.org/10.48813/ht17-zm78

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. Retrieved January 29, 2025, from 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 been gaining 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. Lately AI tools have changed how we work in higher education, and information literacy instruction now often includes various AI tools related to literature searching as well as prompt engineering. Working with AI has evoked a variety of emotions (Ringnes et al., 2024). The patron-librarian meetings, as well as encounters with overworked coworkers and leadership, can all require us to manage our emotions to achieve the desired results. 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. 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

To increase the awareness of how emotional labour affects academic librarians in their teaching roles, and to inspire teaching librarians to think critically about situations that require emotional labour, as well as providing 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 important that teaching librarians understand the concepts of emotional labour, and that they have strategies to deal with this. The paper addresses the experience of emotional labour among teaching librarians and the presentation will give an overview of findings as well as practical tips for sustainable emotional labour.

References

Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International journal of social research methodology, 8(1), 19-32. doi: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. 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. doi: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. doi: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. doi: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.