Conference Agenda

Recent advancements in artificial intelligence (AI), such as ChatGPT, DeepSeek and other generative AI technologies, have significantly transformed the information landscape, bringing both opportunities and challenges in everyday life as in education. These tools, capable of performing complex tasks and creating new content (e.g., generating human-like text, audio, images), have provoked mixed reactions among educators. Some of them see these developments as a chance to innovate and enhance teaching methods, creating a more dynamic, creative, and interactive learning environment Others are concerned about their impact on academic integrity and the possible decline of human creativity and critical thinking. With their improper use they can be used to produce misleading or malicious information, leading to the spread of fake news and harmful information (Trust, 2023).

Given this evolving information landscape, UNESCO (2025, 2022) considers that AI “holds great promise for education, but only if it is deployed in a safe and ethical way”. The organization is focused on providing support and resources to ensure that teachers and students acquire the essential skills needed to navigate this ever-changing information landscape so that AI can benefit everyone, everywhere.

In this way, these necessary skills and knowledge are gradually being integrated into standards and curricula, even though, as Michael Flierl (2024) notes, existing information and media education (IME) does not seem adequately equipped to address the challenges posed by new developments in AI. IME is defined as a pluralistic education that takes into account all forms of information and media, whatever the technology used, and all forms of access to information, including librariesand the Internet (Maury, 2017).

Should the response be to tegulate, promote, or protect? What about in the French school context?

To introduce this conceptual and reflexive paper, we will first review the recent literature on AI, in particular conversational and generative AI in terms of its opportunities and risks (perceived or experienced) in education. We will then put this into perspective with institutional reference texts and identify guidance documents such as programs, accompanying texts, productions of digital thematic groups (#GTnum), and extracts from reports. Such documents inform the content and implementation methods of IME in curricula.

An analytical reading of the data should make it possible to identify how and to what extent the choices made. Specifically, a close reading helps identify the areas and issues to be addressed by teachers and teacher librarians who may contribute to solving the “problems” mentioned above. Such an analysis will help clarify what content is missing, thus opening up perspectives for the future of IME.

References

Flierl, M. (2024). Artificial intelligence and information literacy: Hazards and opportunities. In S. Kurbanoğlu, S., Špiranec, S., Ünal, Y., Boustany, J., Kos, D. (Eds.), Information Experience and Information Literacy, The Eight European Conference on Information Literacy, ECIL 2023, October 9-12, 2023: Revised Selected Papers. Part I. CCIS, vol. 2042. (pp. 52−63). Cham: Springer International Publishing.

Maury, Y. (2017). Éducation à et littératie. Introduction. Les Cahiers de la SFSIC, 14: 143–147. Retrieved 28 August, 2025 from https://cahiers.sfsic.org/sfsic/index.php?id=236

Trust, T., Whalen, J., & Mouza, C. (2023). Editorial: ChatGPT: Challenges, opportunities, and implications for teacher education. Contemporary Issues in Technology and Teacher Education, 23(1): 1–23. Retrieved 28 August, 2025 from https://citejournal.org/wp-content/uploads/2023/02/v23i1editorial1.pdf

UNESCO. (2022). Recommendation on the Ethics of Artificial Intelligence. Retrieved 28 August, 2025 from https://unesdoc.unesco.org/ark:/48223/pf0000381137

UNESCO. (2025). Artificial Intelligence in Education: UNESCO advances key competencies for teachers and learners. Retrieved 28 August, 2025 from https://www.unesco.org/en/articles/artificial-intelligence-education-unesco-advances-key-competencies-teachers-and-learners and https://unesdoc.unesco.org/ark:/48223/pf0000386693.locale=en

This paper investigates educational futures in so far as they constitute more general projections and vision of the general future (Urry, 2016). Data analyses concerning the future have typically been concerned with prediction of financial markets and crime. Not concerned with prediction, the objective of this paper is to investigate sentiments concerning the future of education. Futures have historically been studied in business and policy-making and is currently gaining traction in design.

Educational futures have been concerned with unbundling of services, human enhancement and AI, among other things (Bayne & Gallagher, 2021). In addition to topics that shape different futures, the schism between pessimism and optimism remains entrenched in discussions of technologies and the future (Roderick, 2016). Sentiment analysis is particularly suitable to help us understand this aspect of futures.

We harvested data from X, which continues to be an active channel for individuals to share their thoughts and opinions (Sancho Ortiz, 2025), by accessing the applicable streaming API using R. Data collection was based on a set of hashtags and search terms that specifically address the future of education and resulted in 429.807 Tweets from the 1st of July 2022 through to the 16th of November, 2023. We applied methods and techniques from educational data science to study conversations about futures on social media. We first applied opinion mining, focusing on time orientation, then conducted sentiment analysis (Liu, 2012), and used social network analyses to identify underlying communication structures (McLevey & Scott, 2023). This approach allowed us to get a better understanding of whether individuals organize in communities to discuss different types of futures and what sentiment they had.

Our preliminary results indicate that while users predominantly talked about the present, they clearly indicated a curiosity about the future. This curiosity seemed to be driven by anxiety (e.g. feeling overwhelmed) and anger about the current situation. Furthermore, additional semantic categories and units, provided even more nuanced insights into how individuals talked about futures. Our SNA then revealed that some communities, particularly the largest ones, seemed to incorporate more words with negative connotations in their communication.

Figure 1.

References

Bayne, S., & Gallagher, M. (2021). Near future teaching: Practice, policy and digital education futures. Policy Futures in Education, 9(5): 607–625. https://doi.org/10.1177/14782103211026446

Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1): 1–167.

McLevey, J., Carrington, P. J., & Scott, J. (2023). The Sage Handbook of Social Network Analysis. SAGE Publications Ltd. Retrieved 22 August, 2025 from http://digital.casalini.it/9781529614688

Roderick, I. (2016). Critical Discourse Studies and Technology: A Multimodal Approach to Analysing Technoculture. Bloomsbury Publishing.

Sancho Ortiz, A. E. (2025). Environmental discourse on social media: Exploring engagement in X/Twitter for environmental purposes. Language and Dialogue, 15(1): 105–133. https://doi.org/10.1075/ld.00189.san

Urry, J. (2016). What is the Future? Polity Press.

Introduction

In an era of pervasive digital connectivity, individuals face increasing exposure to vast amounts of information, often leading to feelings of digital and information overload, stress, and burnout. This phenomenon has given rise to the practice of digital detox—intentional disconnection from digital devices as a means of restoring mental clarity and balance. However, the reliance on temporary disconnection highlights a deeper issue: a lack of effective strategies for managing digital devices access and information consumption. Information literacy, defined as the ability to locate, evaluate, and use information effectively, is crucial in equipping individuals with the skills to navigate the digital landscape responsibly. By fostering critical thinking and selective engagement with digital devices and digital content, information literacy standards offer a potential solution to mitigate digital overload at its source. This proposal examines how current information literacy frameworks address – or neglect – the challenges of digital saturation and explores their potential to serve as preventive mechanisms that enhance digital wellbeing in academic settings, and the relation with everyday-life competencies.

Methodology and Objectives

This proposal is based on an integrative literature review combined with a document analysis of key frameworks and standards related to information literacy. The literature review will critically analyze journal articles, retrieved from Web of Science, on information literacy, on digital literacy and digital detox, to identify potential connections. Simultaneously, key documents – such as The SCONUL seven pillars of information literacy: core model for higher education and The ACRL Framework for Information Literacy – will be examined to assess how current standards address or fail to address the challenges of digital saturation.

This research aims to contribute to the academic discourse on sustainable digital media practices, focusing on digital detox practices, through the lens of information literacy. The specific objectives involve a) a literature review to assess if and how information literacy studies address challenges related to excessive digital media consumption, b) a review of if and how information literacy standards address challenges related to excessive digital media consumption, c) to critically analyze gaps within current information literacy frameworks regarding digital well-being and propose possible updates or integrations, and d) to explore the role of information literacy as a preventive or coping mechanism for digital detox practices.

Findings

As this is a work in progress, we cannot outline the results. Based on the research design and the research already accomplished, we expect this paper proposal will provide a structured synthesis of existing knowledge on the relationship between information literacy and digital detox. Based on the preliminary results, gaps within current information literacy frameworks regarding the prevention of digital overload were found, and we expect to be able to formulate recommendations for enhancing information literacy standards to incorporate digital well-being and mindful media consumption strategies. This literature-based work will highlight how information literacy could be positioned to include skills for managing digital habits effectively.

This paper examines the implications for Age Friendly Media and Information Literacy (AFMIL) of society’s increased reliance on Artificial Intelligence (AI) and identifies how AI can be put into the hands of older people so that they can shape its use as a gerontechnology. We view AI as an information tool that can be operationalised as part of AFMIL. The WHO (2022) briefing on ageism in AI positions AI within the concept of gerontechnology described as “technological software and devices that meet the needs of older people” (p. 4). This approach contrasts with corporate and government ambitions to exploit AI for economic advantage (e.g. the UK Government’s plan for AI, https://tinyurl.com/yvv88p4b) that places the locus of control with investors and politicians, thereby positioning older people as consumers and recipients of AI enabled services with limited agency in design and deployment.

Ageism is negative stereotyping, bias, and discrimination on grounds of age leading to older people’s rights and interests being marginalised in politics and media representation (Johnston & Dalziel 2021). We developed a framework for the AFMIL city (Webber & Johnston, 2019), drawing on key international policy documents, identifying three roles for older people (see below). We will use this theoretical framework, in combination with Birkland’s (2019) typology of older users of technology, to critique the current situation and identify ways forward.

Role 1: Older people as portrayed by media, government agencies, and experts: avoiding stereotyping and disinformation. As AIs are trained using existing works, they are prone to repeating and amplifying systemic biases. including ageism, from the design stage of the development cycle. This has not been given as much attention as stereotyping of those with other protected characteristics (Stypińska, 2023; Johnston, 2025). As noted above, AI narratives focusing on commerce and growth do not attend to these ethical issues. Role 2: Older people as consumers of information and media: taking account of their preferences, practices and life experiences. Older people are often positioned as passive and unskilled in technology use with their variety of skills and needs not addressed. (Birkland, 2019; Webber & Johnston, 2019). Ryan & Gutman (2023) give the example of using an AI agent to interact with an older person to make them feel engaged with the community, thus potentially removing the older person’s agency in genuinely engaging with and shaping their community. Role 3: Older people as MIL creators, innovators, and critics. This is the most neglected role, both in terms of MIL and in relation to AI: WHO (2022) stress the right of older people to challenge AI-generated information. WHO (2022) and Compagna & Kohlbacher (2015) advocate participative involvement of older people in technological design They criticise the usual, more marginal, involvement.

We conclude by proposing how older people can be involved in all stages of AI development, utilising mechanisms such as citizens’ assemblies, thus forming a gerontechnological approach that employs deliberative democracy.

References

Birkland, J. (2019). Gerontechnology: Understanding older adult information and communication technology use. Emerald.

Compagna, D., & Kohlbacher, F. (2015). The limits of participatory technology development: The case of service robots in care facilities for older people. Technological Forecasting & Social Change, 93: 19–31. https://doi.org/10.1016/j.techfore.2014.07.012

Johnston, B. (2025). AI and Ageing: Towards gerontechnology. Commonweal. Retrieved 28 August, 2025 from https://tinyurl.com/4vvpn3md

Johnston, B., & Dalziel, C. (2021). All of our Futures: Scotland’s ageing population and what to do about it in 2021-2045. Commonweal.

Ryan, Y., & Gutman, G. (2023). Aging, artificial intelligence, and the built environment in smart cities: Ethical considerations. Gerontechnology, 22(2): 1–5. https://doi.org/10.4017/gt.2023.22.2.rya.08

Stypińska, J. (2023). AI Ageism: A critical roadmap for studying age discrimination and exclusion in digitalized societies. AI & Society, 38: 665–677. https://doi.org/10.1007/s00146-022-01553-.

Webber, S., & Johnston, B. (2019). The Age-Friendly Media and Information Literate (#AFMIL) City. Journal of Information Literacy, 13(2), 276–291. https://doi.org/10.11645/13.2.2672

World Health Organization (WHO). (2022). Ageism in Artificial Intelligence for Health. WHO.