Media and Information Literacy Repository – South Asia

The rapid advancement of artificial intelligence, particularly generative AI, has triggered profound transformations within the global journalism industry, fundamentally challenging traditional news production models, professional norms, and educational paradigms. This comprehensive study examines the multidimensional impact of AI technologies on contemporary journalism through theoretical analysis, industry observation, and educational case studies. The paper first explores the emergence of "Journalistic Taylorism]"(Hindy Lauer Schachter,2020). A phenomenon characterized by the standardization of news production processes through algorithmic intervention, resulting in journalist deskilling and professional alienation. It then analyzes how news organizations are navigating the tension between platform dependency and professional autonomy, creating what scholars term "functional infotainment" on social media platforms. The educational dimension receives particular attention, with examination of innovative "three-stage" training models that balance foundational skills with AI literacy development. By synthesizing international research perspectives with empirical data from China's growing digital publishing sector (which reached ¥1.7485 trillion in revenue in 2024), this paper proposes a holistic framework for understanding journalism's evolution in the AI era. The findings suggest that successful adaptation requires neither wholesale rejection nor uncritical embrace of technology, but rather the cultivation of journalists who possess both critical thinking capabilities and technological fluency – professionals capable of leveraging AI's efficiencies while preserving journalism's essential democratic functions. The study concludes with recommendations for institutional, educational, and ethical reforms that might enable journalism to maintain its public service mission amidst technological disruption.

This research examines the potential integration of digital literacy (DL) and artificial intelligence (AI) in the classroom, emphasizing how these tools might enhance learning results and prevent cyberbullying. Digital literacy—which encompasses technological proficiency, critical thinking, ethical awareness, and appropriate online conduct—has become more important for equitable socioeconomic development in India, especially in the post-pandemic age. National and state-led initiatives like Digital India, PMGDISHA, and Kerala's Akshaya project demonstrate efforts to bridge the urban–rural digital divide, even if barriers still persist due to limited access, especially for underprivileged communities. When applied appropriately, artificial intelligence has the potential to create transformative educational opportunities through administrative automation, adaptive learning platforms, and intelligent tutoring—all of which are consistent with Sustainable Development Goal 4 and India's National Education Policy (NEP) 2020. AI might benefit teachers by removing tedious tasks, increasing accessibility for a range of students, and customizing instruction. However, to address ethical and infrastructural challenges, including algorithmic bias, data privacy, unequal access, and an over-reliance on technology, robust regulatory frameworks, teacher preparation, and inclusive infrastructure development are needed. Teenagers' wellbeing is still frequently at risk from cyberbullying due to the anonymity and reach of digital platforms. It is linked to severe mental and physical health issues, including as depression, anxiety, substance abuse, and social isolation. Victims, perpetrators, and "bully-victims" all experience overlapping and worsening harms. By empowering students to recognize, reject, and report dangerous behaviors, digital literacy acts as a preventive tool. This encourages moral conduct and empathy on the internet. However, DL is not enough on its own; AIpowered moderation that makes use of machine learning and natural language processing can identify harmful content. Identifying complicated language, comprehending context and meaning, and preventing undetected misuse or false positives are some of the difficulties. In order to address cultural, psychological, and environmental issues, the article promotes a multidisciplinary approach to AI design for online safety by fusing technological know-how with social scientific perspectives. Training objective: Context-aware AI systems require high-quality, diversified annotated datasets. Among the policy recommendations are ensuring inclusive design for India's diverse population, integrating AI into middle school curricula, expanding public-private partnerships for sustainable implementation, and developing a National AI-in-Education Framework that prioritizes ethical governance, infrastructure development, and AI literacy. By successfully integrating digital literacy and artificial intelligence into its educational system, India can provide safe, hospitable, and personalized learning environments and prepare its citizens to participate fully in the global digital economy. This partnership has the potential to reduce cyberbullying, reduce educational inequalities, and create a new generation of informed, resilient, and ethical online citizens.

This study explores the influence of digital media literacy (DML) and political ideology on the perceived credibility of fact-checking messages. A 2
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2 factorial online experiment was conducted in South Korea and the U.S. to examine the effects of factchecking source (AI vs. human), DML (high vs. low), and political ideology (conservative vs. liberal). While AI-sourced fact-checking messages exhibited a stronger influence among individuals with high DML, human-sourced messages demonstrated greater efficacy among those with low DML. Contrary to expectations, politically liberal individuals with high DML assigned lower credibility ratings to news articles when the fact-checking source was AI, compared to human experts. Furthermore, the interaction between fact-checking source and DML was mediated by source likeability. These findings contribute to our understanding of how individual differences influence responses to fact-checked content and inform targeted fact-checking strategies.

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.

The study explores possible methods of assessing the level of advertising literacy by examining it in juxtaposition with artificial intelligence (AI) and human judgement. The ability to understand advertisements is an important part of being able to correctly interpret information in the media, which helps people recognize techniques in advertising and make decisions based on recognition. Traditional approaches to testing advertising literacy, such as surveys, tests, and qualitative techniques, are important, but have limited scope and granularity of analysis. New technologies that incorporate artificial intelligence allow for deeper exploration of behavior and emotion using hybrid models that combine the accuracy of artificial intelligence with human understanding. The present exploration assesses the possibilities and includes attributes such as effectiveness, benefits and barriers of AI-driven tools and combined technologies. The results suggest the necessity to develop tools for specific target groups and propose methods for combining technological advances with human aspects in order to improve the assessment of advertising literacy. The study highlights the potential of using flexible and easily scalable methods to cope with the increasing complexity of advertising in the current digital age.

This exploratory study examines the growing role of artificial intelligence (AI) in the Philippine news media industry, highlighting both its benefits and challenges. Using qualitative methods such as interviews, desk reviews, and focus groups, the study finds that AI adoption in newsrooms began mainly in the early 2020s and is used to improve efficiency, speed of content production, and audience engagement. AI is generally viewed as a tool to support journalists rather than replace them, with human oversight remaining essential. However, concerns include AI inaccuracies, misinformation, copyright issues, job displacement, and reduced revenue due to AI-generated news summaries. The study recommends stronger AI governance, platform accountability, better media literacy, and collaboration among stakeholders to ensure ethical and sustainable use of AI in journalism.

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.

Information literacy is a critical topic in contemporary pedagogy and information science. It is ranked among the essential competencies for the 21st century, and in recent years, it has received increasing research interest. The problem, however, is that research has focused mainly on primary and university (college) contexts and only rarely analyzed secondary school settings. This paper, therefore, focuses on a group of high school students and examines whether the literature’s idea of their needs corresponds to their actual needs. Based on the analysis of 32 documents indexed in the Scopus and Web of Science databases, the paper identifies seven significant discursive areas addressed in the literature, both theoretically and empirically. These are: the relationship of libraries and librarians to the development of information literacy, information evaluation, the relationship of information literacy and learning competencies, connection with other competencies, emphasis on the constructivist approach, the social dimension of information literacy, and its possible use for self-actualization. These topics form a specific research discourse. In the second phase of the research, focus groups (8 groups in 4 schools, 41 students) on information literacy were studied through the seven essential discourses mentioned. Although our sample lacked reflections on the relationship between the library and high school students, the remaining six fundamental discourses appeared in the testimonies of high school students (libraries and librarians, evaluation of information, learning competencies, connection with other literacy, constructivist approach, the social dimension of information literacy, and information literacy as a means of self-actualization). The findings show that the main difference between literary discourse and student responses lies in the perception of libraries as centers of information literacy development, with students preferring the school or their teachers instead.

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.

The article examines how widespread algorithmic decision-making systems shape digital media while remaining difficult for the public to critically evaluate. Drawing on the theory of attitude–behavior consistency from political communication, the authors develop a framework to foster algorithmic literacy and encourage more informed public attitudes toward algorithms. They design and test an intervention that combines algorithmic education with personalized user experiences to assess its impact. The findings show that both components together shape attitudes toward algorithms, but the effectiveness of the intervention varies depending on individuals’ patterns of technology use.

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.

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.

This study develops and validates the Scale for the Assessment of Non-experts’ AI Literacy (SNAIL) to measure AI literacy among individuals without formal AI or computer science training. The study support a three-factor model covering technical understanding, critical appraisal, and practical application of AI.