Conference Agenda

Background & Objective

In the rapidly evolving landscape of academia, where data-driven research and artificial intelligence (AI) applications are becoming increasingly prevalent, the intersection of AI and data literacy among graduate researchers is a complex and underexplored domain. Despite its significance, existing studies suggest that artificial intelligence (AI) literacy and data literacy (DL) have not been explicitly examined in the literature (Koltay, 2024; Schüller et al., 2023). Also, less is known about the graduate research students’ current state of AI and data literacy competencies. Therefore, there is a pressing need to assess the current state of AI literacy and data literacy (in terms of awareness, knowledge, skills, and attitude), and what are the data-related challenges faced by them. The current study addresses this gap. In the era of data-driven research and artificial intelligence advancements, graduate researchers play a crucial role in shaping the future of academic inquiry and innovation. Understanding and effectively utilizing both AI and data are essential skills for graduate researchers. This research proposal aims to investigate the intersection of AI literacy and data literacy among graduate researchers.

Methodology

Guided by the nature of the research problem the current study will employ mixed-methods techniques to attain research objectives. The study will use a quantitative survey technique to investigate the current state of AI & data literacy among graduate researchers (university students), as well as to identify collaborations and dependencies between AI literacy and data literacy in the research process. The study will use statistical tools to identify quantitative data by measuring AI literacy and data literacy levels among graduate researchers, and furthermore, identify correlations with research quality, innovation, and interdisciplinary collaboration. Furthermore, qualitative interviews will be conducted to undercover the data-related challenges faced by graduate researchers and to propose recommendations for integrated AI and data literacy education tailored to the needs of graduate researchers. The qualitative interview data will be analysed through thematic analysis.

Expected Outcomes

The expected results of the study will provide useful insights into the current state of AI and data literacy competencies of graduate researchers and will identify their existing knowledge and skills-related gaps and challenges. The study will provide recommendations to improve the quality of data literacy education. The findings hold significant implications for academia. By bridging the gap between AI and data literacy, this study will contribute to enhance research quality, foster interdisciplinary collaboration, and inform graduate education.

References

Koltay, T. (2024). From data literacy to artificial intelligence literacy: background and approaches. Central European Library and Information Science Review / Közép-európai Könyvtár-és Információtudományi Szemle, 2(1): 41–48. https://doi.org/10.3311/celisr.38042

Schüller, K., Rampelt, F., Koch, H., & Schleiss, J. (2023). Better ready than just aware: Data and AI Literacy as an enabler for informed decision making in the data age. In M. Klein, D. Krupka, C. Winter & V. Wohlgemuth (Eds.), INFORMATIK 2023 - Designing Futures: Zukünfte gestalten (pp. 425–430). Bonn: Gesellschaft für Informatik e.V. https://doi.org/10.18420/inf2023_49

Introduction

As ChatGPT continues to shape understandings of agency, trust, and emotional intelligence, much of the existing research centres on its role in industry settings. However, fewer studies have explored how individuals develop emotional and relational connections with digital AI tools and the broader implications for trust. This paper adopts a critical posthumanist perspective (Herbrechter et al., 2022) and challenges the framing of AI as a passive instrument, instead positioning it as an agentic presence embedded within sociotechnical networks that actively shape and are shaped by human interactions. These shifts have implications not only for human-AI relationality but also for information literacy, as ChatGPT functions both as a source of knowledge (Acosta-Enriquez et al., 2024) and as an interactive social presence (Kavitha et al., 2024). TikTok’s participatory culture make it a space for examining these entanglements, particularly among younger users who contribute to the co-construction of AI’s social roles (Barta & Andalibi, 2021). This engagement reveals perceptions of AI’s social and epistemic roles. Research on relational agents suggests that forming emotional bonds with AI can lead to increased epistemic trust, as users are more likely to accept and internalize information from systems they perceive as socially and emotionally responsive (Bickmore & Picard, 2005). Within TikTok’s highly interactive and affective environment, these dynamics may be amplified and may reinforce ChatGPT’s role as both a relational and informational authority.

Objective

This study draws on critical posthumanist thought and social epistemology (Fricker et al., 2021) to examine how TikTok users construct narratives around ChatGPT’s social roles, framing the platform as a trusted, relational system where AI is engaged with not just as an information source but as a relational and epistemic agent. It further explores the implications of this dynamic for information literacy and explores how emotional trust in AI can shape knowledge construction, critical evaluation, and dependency.

Findings

Findings indicate that ChatGPT is often positioned as a friend, confidant, therapist, and even a superior social presence due to specific “more-than-human” affordances. Many users emphasize its enhanced memory, perceived neutrality, and constant availability further contribute to a trusted social positioning, with some users seeing it as a more reliable emotional and intellectual presence than human counterparts. These perceptions raise critical questions about social and epistemic trust and how AI-mediated interactions shape not only emotional engagement but also information-seeking practices. The blurring of social and epistemic trust may have significant implications for information literacy, as reliance on AI as both a source for relationality and knowledge may discourage verification and reshape how authority and credibility are constructed in digital environments. These affordances may contribute to patterns of overreliance, as some users attribute social and epistemic capacities to ChatGPT that exceed its designed function. The findings suggest that information literacy frameworks should account for both relational and epistemic dynamics with AI.

References

Acosta-Enriquez, B. G. et al. (2024). Knowledge, attitudes, and perceived ethics regarding the use of ChatGPT among generation Z university students. International Journal of Educational Integrity, 20(10).

Barta, K., & Andalibi, N. (2021). Constructing authenticity on TikTok: Social norms and social support on the “fun” platform. Proceedings of the ACM on Human-Computer Interaction, 5(2): 1–29.

Bickmore, T., & Picard, R. (2005). Establishing and maintaining long-term human-computer relationships. ACM Transactions on Computer-Human Interaction, 12(2), 293–327.

Fricker, M., Graham, P. J., & Henderson, D. (Eds.). (2021). The Routledge Handbook of Social Epistemology. Routledge.

Herbrechter, S., Callus, I., de Bruin-Molé, M., Grech, M., Müller, C. J., & Rossini, M. (2022). Critical posthumanism: An overview. In S. Herbrechter, I. Callus, M. Rossini, M. Grech, M. de Bruin-Molé, & C. J. Müller (Eds.), Palgrave Handbook of Critical Posthumanism (pp. 1–24). Palgrave Macmillan.

Kavitha, K., Joshith, V. P., & Sharma, S. (2024). Beyond text: ChatGPT as an emotional resilience support tool for Gen Z – A sequential explanatory design exploration. E-Learning and Digital Media.

Over the past five years as a law librarian, I have observed a shift in how law students engage with information literacy, particularly following the emergence of generative artificial intelligence (AI) tools such as ChatGPT. Prior to late 2022, undergraduate and postgraduate law students regularly sought research support, with queries peaking in January as they prepared for assessments. However, in early 2023, a change emerged: student queries declined in January, shifting instead to February after receiving feedback. Many referenced non-existent or unreliable sources, purportedly found online, raising concerns about AI’s role in research practices. This study investigated the impact of ChatGPT on first year law students’ research behaviour and the broader implications for information literacy within legal education. The research questions considered were: (1) How has generative AI influenced law students’ research methodologies? (2) To what extent do students critically evaluate AI-generated sources? (3) How can legal educators and librarians integrate AI literacy into information literacy instruction? Employing a mixed-methods approach, this research combines qualitative analysis of student queries with quantitative data from academic support meetings. Over one academic year, data were collected from more than 250 student interactions, including individual consultations and email inquiries. Thematic analysis revealed a growing reliance on AI-generated content, often leading to superficial research practices and reduced engagement with authoritative legal sources. To tackle these challenges, I designed a workshop to highlight AI’s limitations in legal research and stress the importance of traditional methods. Over 250 first-year law students took part through their English Legal System module, discussing AI-generated content’s reliability, accuracy, and ethical concerns. Many were surprised by how often AI fabricated legal citations. This workshop paved the way for a wider initiative with academic support services, leading to an integrated AI literacy framework within the law school curriculum. The framework includes targeted interventions such as AI literacy training, critical evaluation exercises, and guided research tasks, helping students assess sources critically and distinguish credible information. This study aligns with a growing body of research exploring the role of AI in higher education, particularly within information literacy instruction (Al-Abdullatif & Alsubaie, 2024). Recent scholarship highlights the need for AI literacy to be embedded within curricula (Meakin, 2024; Ndungu, 2024), encompassing the evaluation of AI-generated content (Wan, 2024), ethical considerations (Černý, 2024), and AI’s role in research workflows (Khan et al., 2024). The findings reflect the importance of such initiatives, demonstrating that proactive AI literacy instruction mitigates misinformation risks (Chaaban et al., 2024) and fosters a more discerning approach to legal research. While this study is situated within a law school context, its implications extend beyond legal education. The challenges posed by generative AI in research are relevant across disciplines, and this framework may serve as a model for broader implementation in higher education.

References

Al-Abdullatif, A. M. & Alsubaie, M. A. (2024). ChatGPT in learning: Assessing students’ use intentions through the lens of perceived value and the influence of AI literacy. Behavioral Sciences, 14(9): 845–868. https://doi.org/10.3390/bs14090845

Černý, M. (2024). University students’ conceptualisation of AI literacy: Theory and empirical evidence. Social Sciences, 13(3): 129–155. https://doi.org/10.3390/socsci13030129

Chaaban, Y., Qadhi, S., Chen, J., & Du, X. (2024). Understanding researchers’ AI readiness in a higher education context: Q methodology research. Education Sciences, 14(7), 709–728. https://doi.org/10.3390/educsci14070709

Khan, R., Gupta, N., Sinhababu, A., & Chakravarty, R. (2024). Impact of conversational and generative AI systems on libraries: A use case Large Language Model (LLM). Science & Technology Libraries, 43(4): 319–333. https://doi.org/10.1080/0194262X.2023.2254814

Meakin, L. (2024). Exploring the impact of generative artificial intelligence on higher education students’ utilization of library resources: A critical examination. Information Technology & Libraries, 43(3): 1–13. https://doi.org/10.5860/ital.v43i3.17246

Ndungu, M. W. (2024). Integrating basic artificial intelligence literacy into media and information literacy programs in higher education: A framework for librarians and educators. Journal of Information Literacy, 18(2): 1–18. https://doi.org/10.11645/18.2.641

Wan, S. (2024). The Jack in the Black Box: Teaching college students to use ChatGPT critically. Information Technology & Libraries, 43(3): 1–3. https://doi.org/10.5860/ital.v43i3.17234

The objectives of the paper are to determine AI literacy and its relations to human information creativity. It is based on a research project on information creativity in digital environment. The main research question is: In which ways can AI tools enhance human information creativity?

Analyses of related literature proved that the ability of GAI (Generative Artificial Intelligence) to engage in creation of content raised attention of many researchers (Vinchon et al., 2023). The question is, which ways of collaboration of AI and humans are most creative. AI systems process information in such a way that they can adapt to their environment, analyse knowledge and generate “new” content (text, images, video), using deep learning. Studies in the academic or workplace environment compared content production with human creativity, writing digitization, and co-creativity (Zhao et al., 2024, Wingström et al., 2024). Participants appreciated speed and quality of the content; limitations included incorrect information, plagiarism, ethics. Human information creativity is marked by inspiration, personal experience, intuition, curiosity. Information creativity framework (Dahlquist, 2023), reviews and studies of information literacy were analysed (Cox, 2021). AI literacy (Ng et al., 2021) means the abilities to select a tool, prompt engineering skills, interpretation, assessment of bias, verification, cognitive, social and ethical understanding of AI impact.

The methodology is based on a conceptual model and design of a qualitative empirical study of 17 doctoral students in humanities and social sciences at Comenius University in Bratislava. In an online focus group and written essays, students expressed their perception and personal experience with AI tools in creative work.

Results confirmed that most participants used AI tools for inspiration (orientation), combinations (syntheses), transformations (translation), presentation; alongside ethical concerns (verification). A final conceptual model can be used for support of AI literacy related to information creativity of doctoral students and the AI enhanced academic writing. It is based mainly on creative information processing, exploration, assessment, synthesis. In conclusion, we determined relationships of AI literacy and information creativity covering knowledge, skills and values of AI tools (concept maps, collaboration). Co-creativity of humans and AI can enhance products with the use of control, metaphors, creative ecologies. Results can be applied to information design and information literacy theory.

References

Cox, A. M. (2021). Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions. Intern. J. of Educ. Technol. in Higher Education, 18(1): 3. https://doi.org/10.1186/s41239-020-00237-8.

Dahlquist, M. (2023). Toward a Framework for information creativity. College & Research Libraries, 84(3): 441. https://doi.org/10.5860/crl.84.3.441.

Ng, D. T. K. et al. (2021). Conceptualizing AI literacy: an exploratory review. Comp. and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

Vinchon, F. et al. (2023). Artificial intelligence & creativity: A manifesto for collaboration. The Journal of Creative Behavior, 57(4): 472–484. https://doi.org/10.1002/jocb.597.

Wingström, R, et al. (2024). Redefining creativity in the era of AI? Perspectives of computer scientists and new media artists. Creativity Research Journal, 36(2): 177–193. https://doi.org/10.1080/10400419.2022.2107850.

Zhao, X. et al. (2024). ChatGPT and the digitisation of writing. Humanities & Social Sciences Communications, 11: 482. https://doi.org/10.1057/s41599-024-02904-x

AI is everywhere, it influences almost every part of our life, especially in the library and information field. According to Cox (2024) artificial intelligence as a general-purpose technology appears in many contexts but looks different in each one. Sometimes it is about turning ‘stuff’ to data, sometimes finding patterns in such data, sometimes it offers adaptivity and sometimes it seems to be about predicting future behaviour. As a result, it is hard to define AI except at the abstract level in terms of computers doing things we think of humans doing. Without information literacy one cannot cope with artificial intelligence. Information literacy contains those competencies that make the person able to understand the information need, to locate and collect the relevant information, to select, evaluate and organize this information, to make use of it keeping the social and academical rules and ethics. The newest subcategory of information literacy is artificial intelligence literacy. In the literature it is stated that artificial intelligence literacy contains all those competences which are needed to understand how AI works. Without this knowledge one cannot use this new technology.

AI literacy framework (Mills et al., 2024) defines three interconnected modes of engagement:

• Understand: Acquiring basic knowledge of what AI can do and how it works in order to make informed decisions about evaluating and using AI systems and tools.

• Evaluate: Centering human judgment and justice to critically consider the benefits and/or costs of AI to individuals, society, and the environment.

• Use: Interacting, creating, and problem-solving with AI as a progression of use for distinct contexts and purposes.

My research questions are the following, aiming to understand the capacities of AI related to information literacy:

• Can it really understand the information need?

• Can it select the relevant information for us?

• Can it evaluate the information, if it is true or not?

• Can it fulfil the social and ethical rules related to information?

• Can it use the information always according to the ethical norms?

My hypothesis is that AI literacy competences combined with information literacy competences can give the necessary skills for information professionals to apply new technology efficiently.

The research is mainly theoretical, reflecting on the scientific literature. I would like to compare information literacy competences to AI literacy competences, and based on this comparison create a more detailed AI literacy competency standard. AI literacy should be defined as a subset of information literacy. I would like to focus on those competences which are new and important to cope with AI during information related tasks (eg. Algorithmic literacy, prompt engineering, self reflective mindset etc.) (Chiu et al, 2024).

References

Chiu, T. K., Zubair A., Murod I., & Temitayo Sanusi, I. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6. https://doi.org/10.1016/j.caeo.2024.100171

Cox, A. (2024). Developing a library strategic response to Artificial Intelligence. The University of Sheffield. https://doi.org/10.15131/shef.data.24631293.v1

Mills, K., Ruiz, P. & Lee, K. (2024): AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology. Digital Promise. Retrieved 22 August, 2025 from https://digitalpromise.org/2024/06/18/ai-literacy-a-framework-to-understand-evaluate-and-use-emerging-technology