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. To investigate the current state of AI & data literacy among graduate researchers (university students). And to identify collaborations and dependencies between AI literacy and data literacy in the research process, a quantitative survey technique will be used. Quantitative data will be data using statistical tools to measure AI literacy and data literacy levels among graduate researchers and 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.

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.

Introduction

ChatGPT continues to shape understandings of agency, trust, and emotional intelligence, yet 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. The present paper adopts a critical post humanist perspective that highlights the agentic potential embedded within sociotechnical networks that actively shape 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 and as an interactive social presence. 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. This engagement reveals perceptions of AI’s social and epistemic roles, as users are more likely to accept and internalize information from systems they perceive as socially and emotionally responsive.

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., Arbulú Ballesteros, M.A., Arbulu Perez Vargas, C.G. et al. Knowledge, attitudes, and perceived Ethics regarding the use of ChatGPT among generation Z university students. Int J Educ Integr 20, 10 (2024).

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), Article 430, 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.

Keywords: ChatGPT, Emotional Trust, Epistemic Trust, Social Media, TikTok

The advent of generative AI (GenAI), particularly tools such as ChatGPT, has profoundly disrupted higher education, with legal education facing unique challenges.[1] This paper examines the implications of GenAI for legal pedagogy and referencing practices, focusing on the Oxford University Standard for the Citation of Legal Authorities (OSCOLA). Drawing on insights from a 2025 workshop at a major UK university, the exploration of student engagement with GenAI in legal research is observed. The findings reveals declining demand for traditional referencing support alongside a rise in fictitious citations, highlighting limited critical awareness of AI-generated content. In response, this paper recommends increased collaboration between librarians and lecturers [2-3], particularly in developing teaching approaches that emphasise ethical AI use and critical evaluation. Although legal education provides the case context, the recommendation is relevant across higher education, where structured, interdisciplinary support will be essential for navigating the uncertainties of the AI era.

References

1. EBSCO (2024) AI in library research and AI in academic research platforms - findings from EBSCO’s recent beta release. https://tinyurl.com/4vafcf5w

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

3. Zhou X, Schofield L (2024) A model to enhance students’ AI literacy. https://tinyurl.com/bdzfvexz

The objectives of the paper are to determine AI literacy and its relations to human information creativity. It is based on a research project focused 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 existing knowledge and generate “new” content (text, images, video), using deep learning (LLM). Studies in the academic or workplace environment compared content production with human creativity, writing digitization, and co-creativity (Zhao, Cox & Cai, 2024, Wingström et al., 2024). Participants appreciated speed and quality of the content, limitations of GAI were incorrect information, plagiarism, fake references, ethics (responsibility). Human information creativity is marked by inspiration, personal experience, intuition, motivation, curiosity. Information creativity framework (Dahlquist, 2023), reviews and studies of information literacy of students were analysed (Cox, 2021). Collaboration of GAI and human creativity determined AI literacy (Ng et al., 2021) as the abilities to select a tool, prompt engineering skills, interpretation of outputs, assessment of bias, verification, cognitive, social and ethical understanding of AI impact.

The methodology is based on conceptual analyses, a 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 of AI tools in creative work and personal experience with AI tools.

Results confirmed that most of the participants used AI tools for inspiration (orientation, structuring), combinations (analyses, syntheses), transformations (translation) and presentation (visualization) of information. Participants mentioned several ethical concerns (verification, revisions, plagiarism). We developed a conceptual model which can be used for support of AI literacy related to information creativity of doctoral students in the AI enhanced academic writing; composed of inspiration, exploration, combinations (structuring), transformations, assessment, support and final presentation. Further experiments of academic writing with AI tools will follow. In conclusion, we determined relationships of AI literacy and information creativity covering knowledge, skills and values of the use of AI tools for inspiration, exploration, transformations, discovery of topics, concept maps, visualization, collaboration. Co-creativity of humans and AI can enhance development of products using analyses and syntheses, constraints and 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. Computers 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., Hautala, J. & Lundman, R. (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., Cox, A. & L. Cai (2024). ChatGPT and the Digitisation of Writing. Humanities & Social Sciences Communications. 11, 482. https://doi.org/10.1057/s41599-024-02904-x.tps://doi.org/10.1057/s41599-7.

AI is everywhere, it influences almost every part of our life, do we want it or not. 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 (words, text, handwritten manuscripts or images), 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. AI is often hidden within other applications or within infrastructures, so it is hard to know where and how AI is in use.

It must be evidence that without information literacy one cannot cope with artificial intelligence. The newest subcategory of information literacy is artificial intelligence literacy. Usually in the literature it is stated that artificial intelligence literacy contains all those competencies which are needed to understand how AI works. Without this knowledge one cannot use this new technology, but at the same time we have to be sure how basic information literacy skills relate to AI literacy.

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.

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. When using artificial intelligence’s broad opportunities, these requirements are still valid. 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 tot he ethical norms?

The research is mainly theoretical, reflecting on the scientific literature. Based on the answers we can create a new concept and definition of AI literacy.

References:

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

Mills, Kelly, Ruiz, Pati and Lee, Keun-woo (2024): AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology. Digital Promise. https://digitalpromise.org/2024/06/18/ai-literacy-a-framework-to-understand-evaluate-and-use-emerging-technology/#:~:text=Our%20expanded%20AI%20Literacy%20Framework%20depicts%20the%20relationship,educational%20leaders%20in%20adapting%20it%20to%20their%20contexts.