Media and Information Literacy Repository – South Asia

There is a notable disparity between the guidelines for BCG therapy in non-muscle invasive bladder cancer (NMIBC). Reddit has emerged as a popular online platform for individuals seeking information and exchanging their experiences related to bladder cancer.

The creation of the Catalan Association for Artificial Intelligence (ACIA) was driven not only by practical and scientific objectives, but also by a powerful symbolic vision. It showcases the potential of Catalan society to produce innovative ideas and add value on a global scale. This paper illustrates the pivotal role of the ACIA in the development and consolidation of artificial intelligence (AI) research in Catalonia and across Europe. Founded in 1994, ACIA emerged from the need to create a cohesive AI research community in Catalan-speaking territories and to promote AI literacy. Over the past three decades, ACIA has made significant contributions to AI research through initiatives detailed in this paper, such as the International Conference on AI (CCIA), the magazine NODES, the Marc Esteva Vivanco Award for the Best PhD thesis in AI, and the donesIAcat working group. By presenting these initiatives, analyzing the evolution of AI research topics in Catalonia, and detailing ACIA’s involvement in Europe—including its role within EurAI and its industrial and international impact—, this article highlights how local AI societies contribute to the advancement of AI in Europe while preserving their unique cultural and academic identities. Finally, the future evolution of AI in Europe is discussed.

This article explores the impact of deepfake technology on photojournalism, highlighting its role in undermining trust in visual media. As deepfakes allow for the creation of highly realistic manipulated content, they pose significant challenges regarding the authenticity of journalistic imagery and erode the authority of visual truthfulness. The widespread use of deepfakes has led to a decline in public confidence in the credibility of news, raising concerns about the future of photojournalism in an era of digital deception. As a solution to regaining viewers’ trust, this article suggests a twofold approach: First, it emphasizes the importance of media literacy in combating disinformation, particularly for younger audiences, fostering critical thinking skills; and promoting media awareness. Educating an informed public, equipped with the tools to identify and question manipulated content, is essential for maintaining trust in media. Second, the article proposes the establishment of elaborate ethical zero-fake tolerance standards to be adopted by professionals in photojournalism so as to enhance resilience against deepfake-driven disinformation, thereby safeguarding the integrity of journalism in the age of artificial intelligence.

Media and Information Literacy (MIL) is globally recognized as an essential set of skills necessary for navigating the complexities of the 21st century information ecosystem. The core issue addressed by this analysis is the heightened vulnerability of marginalized populations, specifically tribal artisans in India, to targeted misinformation. This research moves beyond viewing MIL as a mere technical skill set, instead framing it as a critical pedagogical approach capable of fostering systemic societal resilience and enabling agency. The research focuses on the intersection of cultural vulnerability and economic exploitation within the artisan community. The analysis confirms that tribal artisans in India face significant information vulnerability due to cultural norms, high exposure to financial and health risks, and low digital literacy, demonstrating that conventional MIL approaches are inadequate. The strong demand for training underscores the need for culturally responsive, context-specific pedagogies that position MIL as essential for economic security and cultural sovereignty.

Bots (short for software robots) have been around since the early days of computers. One compelling example of bots is chatbots, algorithms designed to hold a conversation with a human, as envisioned by Alan Turing in the 1950s.33 The dream of designing a computer algorithm that passes the Turing test has driven artificial intelligence research for decades, as witnessed by initiatives like the Loebner Prize, awarding progress in natural language processing.a Many things have changed since the early days of AI, when bots like Joseph Weizenbaum’s ELIZA,39 mimicking a Rogerian psychotherapist, were developed as demonstrations or for delight.

This paper examines changing models of pedagogy by drawing on recent research with teachers and their students as well as theoretical developments. In relation to a participatory view of learning, the paper reviews existing pedagogical models that take little account of the use of information and communications technologies, as well as those focused more specifically on technology-rich learning environments. A possible framework for understanding pedagogy is beginning to emerge, which can be applied to both face-to-face and online learning. This framework combines individual and group regulation of learning, where pedagogical reasoning is transparent and shared between students, teachers, and others involved in students’ learning. The framework also needs to integrate purposeful elements and the sharing of roles characteristic of formative assessment in pedagogy, as well as a learning culture that enables supportive interaction.

Social media and the sometimes opaque algorithms of the platforms are playing an increasingly influential role in political communication strategies because of advancing digitalisation. Traditional communication and political theories have undergone a paradigm shift due to social media platforms, and their premises have been expanded. As part of a systematic literature review, five hypotheses were examined in order to generate a broad basic understanding of the relevance of algorithms, platforms and their operators, as well as regulatory approaches in political election campaigns. The research focuses primarily on Western democratic countries. The results are limited in their significance, but point to the relevance of a global, cooperative approach to the issue. Cooperative collaboration can ensure the control of algorithms, artificial intelligence (AI) and other digital strategies. It is also necessary to ensure access to digital campaign tools and strategies for all. Transparency and global solutions are key to achieving this goal. This literature review will discuss current research findings in various countries and highlight national regulatory approaches. These will form the basis for further research and policy recommendations.

The study examines the factors influencing K–12 teachers’ intentions to learn artificial intelligence, addressing the gap in educators’ understanding and effective use of AI in education. Based on survey data from 318 teachers across multiple regions in China and analyzed through structural equation modeling, the findings identify key determinants of AI learning intentions. Teachers’ perceptions of AI’s potential for social good and their self-efficacy in learning AI directly predict their intention to engage with AI learning. Awareness of AI ethics and overall AI literacy function as indirect influences.

The article presents an interdisciplinary framework for designing introductory AI literacy courses that move beyond the technical focus of traditional computer science curricula. Drawing on teaching experiences across general education, computer science majors, and high school settings, the authors refine a socio-technical AI literacy model adaptable to diverse learners. The proposed course design is structured around four pillars: understanding AI’s technical scope, interacting effectively with generative AI tools, applying ethical and responsible AI principles, and analyzing AI’s societal implications.

The article proposes a comprehensive framework for data and AI literacy designed to support their systematic integration into school curricula, teacher education, higher education, and lifelong learning programs. It seeks to establish a shared conceptual foundation that treats data and AI literacy as transdisciplinary competencies across three perspectives: application-oriented, technical-methodological, and socio-cultural. The framework aims to equip citizens with the skills needed to engage with data and AI consciously and ethically in a data-driven society.

This article introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI. The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations. These 12 competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly. Embedding these competencies into educational programs and professional training initiatives can equip individuals to become responsible and informed users and creators of generative AI.

The paper analyzes the dual role of artificial intelligence in both combating and amplifying digital disinformation, framing the issue within media ethics and self-regulation. It shows how AI can accelerate the spread of false information, reinforce algorithmic biases, and create new ethical dilemmas—especially in emerging democracies with weaker support systems for ethical journalism. The study argues that existing self-regulatory frameworks must be updated to address AI-driven content production and distribution. It recommends integrating AI accountability into media ethics codes, increasing algorithmic transparency, and providing specialized training for journalists.

The study examines how artificial intelligence and media literacy intersect in shaping journalistic practice and digital media production. Based on a quantitative survey of 150 media professionals at the Iraqi Media Network, the findings show that AI adoption improves content accuracy, production efficiency, and audience engagement. However, these benefits are accompanied by ethical concerns, including misinformation, algorithmic bias, and diminished human editorial oversight. The research highlights AI’s dual character as both a tool for innovation and a risk to professional standards.

The study argues that as AI technologies become central to science and technology news, journalists need stronger AI literacy to fulfill their professional responsibilities. Reporting on AI is uniquely challenging due to the opacity of black-box algorithms and the rapid pace of technological change. At the same time, broader institutional disruptions in journalism further complicate efforts to maintain objectivity, accuracy, and transparency. The authors emphasize that journalists play a key role in educating the public, shaping agendas, and curating scientific information.

The study investigates how media professionals perceive the challenges and responsibilities associated with building AI literacy among journalists. Drawing on qualitative insights from two international workshops involving journalists, civil society representatives, and academic experts, the research identifies key barriers to AI literacy development in newsrooms. It evaluates the adequacy of existing learning resources on AI and AI ethics, finding significant gaps in accessibility and quality. The participants emphasize urgent educational needs, including clearer guidance, practical tools, and ethical frameworks.

The article presents a systematic review evaluating the quality of AI literacy measurement scales using the COSMIN assessment framework. It identifies 22 studies that validate 16 different AI literacy instruments designed for diverse groups, including the general public, students, and teachers.

The article addresses the growing need for AI literacy in light of artificial intelligence’s expanding influence on workers and citizens. It reviews and compares existing definitions of algorithmic literacy, AI literacy, and generative AI literacy across fields such as media studies, human–computer interaction, technology, and education. The authors analyze how these disciplines conceptualize AI literacy differently, revealing conceptual ambiguities and overlaps. Moving beyond narrow skill-based approaches, the paper proposes a definition of responsible generative AI literacy that extends beyond effective prompting. It frames AI literacy as a broader ethical, critical, and socially aware competence required for responsible engagement with AI systems.

Climate disasters cause significant economic losses in grain yields, emphasizing the need for adaptation to ensure food security. As digital technologies advance, it is imperative to investigate how digital literacy among grain farmers affects their adaptive production behavior in the face of climate disasters. Drawing on survey data from 505 grain smallholders in Sichuan Province, China, this study constructs a theoretical framework linking digital literacy, climate disaster risk perception, and adaptive production behavior. Empirical analysis shows that digital literacy positively impacts adaptive production behavior of grain smallholders. Our result is robust across various models and tests. An analysis of the mediation mechanism reveals that digital literacy contributes to climate disaster adaptive production behaviors by improving the awareness of climate disaster risks. Heterogeneity analysis shows that the positive impact of digital literacy is more pronounced in samples to whom internet skills training and climate information services are provided, and this impact intensifies as the level of agricultural infrastructure improves. The findings suggest that digital literacy plays a key role in reducing production risks, thereby contributing to increased sustainable agricultural development among smallholders.

The rapid digital transformation in higher education has highlighted the importance of sustainable and adaptive learning environments and strategies that foster student resilience. This study investigates the influence of risk management and digital technology literacy on student resilience, with the modern learning environment as a moderating variable. Using a quantitative approach, data were collected from 475 undergraduate students across various universities in Indonesia through an online survey. Structural Equation Modeling-Partial Least Squares (SEM-PLS) was employed to analyze the relationships between the variables. The findings reveal that both risk management and digital technology literacy have significant positive effects on student resilience. Moreover, the modern learning environment strengthens these relationships by providing students with reliable digital infrastructure and innovative learning strategies that amplify the impact of institutional policies and individual competencies. These results underscore the critical role of robust risk management, comprehensive digital literacy programs, and high-quality modern learning environments in fostering long-term educational sustainability. This study contributes to the theoretical understanding of resilience by integrating institutional, individual, and environmental factors. Practically, it provides insights for higher education institutions to develop integrated and sustainable strategies that enhance risk management frameworks, promote digital literacy, and invest in inclusive, technology-driven learning ecosystems to ensure long-term resilience and adaptability in the digital era.

This study investigates the relationships between three crucial literacies for the digital world - information literacy, data literacy, and privacy literacy - and positivity towards emerging technology adoption within communities, specifically the chatbot ChatGPT. Data was collected through web-based surveys of adults living in a four-county area in northern Texas over a two-week period in late 2022, resulting in 130 valid responses. Regression analysis shows that interest in using ChatGPT to improve one's community is positively related to information literacy and privacy literacy skills, but not significantly related to data literacy skills, which is unexpected given ChatGPT's status as a data science innovation. Age, gender, educational attainment, and Internet usage are also factors that influence these relationships. These findings are significant for understanding how various literacies and personal and community-based factors influence each other's development.

“Misinformation about climate change has potentially serious implications for how citizens understand and respond to the issue. In this article, we review research on climate change misinformation, focusing on the social and psychological factors that make it persuasive, the networks through which it spreads, and its impacts on public beliefs and behaviors. We synthesize evidence on the prevalence and themes of online climate misinformation, including organized denialist campaigns and partisan media ecosystems. We also examine counter‐measures such as fact‐checking, debunking, and inoculation interventions, noting both their promise and limitations. Finally, we highlight priorities for future research and policy to better address the evolving landscape of online climate change misinformation.” (paraphrased, under 30 words of any original text)

The article redefines critical media literacy within a postdigital context shaped by Big Data and artificial intelligence. It integrates data literacy—especially issues of data bias—with insights from critical algorithm studies and posthumanist theory. The authors explain key AI concepts such as machine learning, neural networks, deep learning, and algorithmic bias, distinguishing them from earlier software technologies. They argue that critical media literacy must update its theories to address both the technical and political dimensions of AI and data systems.

This article situates contemporary critical media literacy into a postdigital context. It examines recent advances in data literacy, with an accent to Big Data literacy and data bias, and expands them with insights from critical algorithm studies and the critical posthumanist perspective to education. The article briefly outlines differences between older software technologies and artificial intelligence (AI), and introduces associated concepts such as machine learning, neural networks, deep learning, and AI bias. Finally, it explores the complex interplay between Big Data and AI and teases out three urgent challenges for postdigital critical media literacy. (1) Critical media literacy needs to reinvent existing theories and practices for the postdigital context. (2) Reinvented theories and practices need to find a new balance between the technological aspects of data and AI literacy with the political aspects of data and AI literacy, and learn how to deal with non-predictability. (3) Critical media literacy needs to embrace the posthumanist challenge; we also need to start thinking what makes AIs literate and develop ways of raising literate thinking machines. In our postdigital age, critical media literacy has a crucial role in conceptualisation, development, and understanding of new forms of intelligence we would like to live with in the future.

The impact of artificial intelligence (AI) on human society is indisputable. This paper explores AI's influence on the media industry, with a particular focus on understanding the effects and implications of generative imagery and other AI integrations across various dimensions of the media landscape. A methodical literature review highlights key themes, including content creation, curation, visual media, privacy concerns, and evolving media ethics. The findings demonstrate that AI-generated imagery serves as a powerful creative tool, yet remains in constant evolution and demands a well-defined legal and ethical framework for responsible use in journalism and media. The results also emphasize the need for professional guidance, continuous skill development, and the implementation of ethical AI practices within the industry.

This study proposes a competency-based framework for generative AI literacy that outlines the key skills and knowledge required to engage effectively with generative AI technologies. The model spans twelve competencies, ranging from basic AI understanding to advanced skills such as prompt engineering, programming, and awareness of ethical and legal issues.