AI and Media Transformation: Global Adoption, Emerging Trends, and Industry Impacts

1. Introduction

The relationship between media and technology has never been static. From the printing press to the telegraph, from broadcast television to the World Wide Web, each successive wave of technological innovation has fundamentally altered the conditions under which information is produced, circulated, and consumed (Stöber, 2004). Today, we stand at another such inflection point. Artificial intelligence (AI), and in particular the large-scale generative systems that have proliferated since the early 2020s, is reshaping journalism, public communication, and the broader information ecosystem with a speed and depth that challenges existing frameworks of analysis (de-Lima-Santos & Ceron, 2021). At the same time, AI itself has become one of the most intensely debated subjects in public discourse, attracting commentary that ranges from utopian optimism to existential apprehension (Mohammed et al., 2024). In this dual capacity, as both a tool of media production and a topic of media coverage, AI demands sustained scholarly attention.

This Special Issue was conceived in that spirit. It brings together twelve original articles that examine, from diverse disciplinary and geographic vantage points, how the media shapes narratives about technological innovation and how AI specifically is influencing media practice, public understanding, and social life more broadly. The contributions span computational communication research, journalism studies, media education, environmental communication, political economy, and cultural studies. They draw on studies conducted in Europe, Africa, the Middle East, and across digital platforms that transcend national borders. The result is a comparative and multi-perspectival collection that resists both the techno-optimism that frequently characterises industry commentary and the undifferentiated techno-pessimism that sometimes characterises critical scholarship.

The broader context in which this issue appears is one of profound uncertainty. News organisations worldwide are under structural financial pressure, even as the informational demands placed on them by democratic publics have never been greater. AI offers some newsrooms new tools for efficiency and scale; it simultaneously threatens established journalistic labour, erodes epistemic trust through synthetic media, and introduces new vectors for misinformation. Platforms that once promised to democratise the flow of information have become contested battlegrounds over fact, framing, and the very definition of legitimate knowledge. Governments, civil society organisations, and international bodies are scrambling to develop governance frameworks adequate to these challenges, yet the pace of technological change consistently outstrips the pace of regulatory response.

Against this backdrop, the media remains the primary means through which most citizens form their understanding of AI and its implications. How journalists, editors, producers, educators, and platform operators frame AI, as threat or opportunity, as neutral tool or culturally loaded artefact, as Western invention or globally contested terrain, matters enormously for the kinds of publics that will emerge to deliberate over its governance. This Special Issue contributes to that deliberation by offering empirical evidence, theoretical reflection, and normative argument drawn from scholarship at the forefront of the field.

2. Media, Technology, and the Construction of the Future

Scholarship on the relationship between media and technology has long recognised that media outlets do not merely report on technological change; they actively participate in constituting it (Boczkowski, 2004). The concepts of ‘mediatisation’ and ‘mediation’ have been deployed to capture the ways in which media logic permeates and restructures other social fields, including science, politics, and the economy (Nowak-Teter, 2019). When media organisations cover AI, they do not passively transmit facts about an independently existing technology; they frame that technology in ways that shape public expectations, direct policy attention, and influence the behaviour of developers, investors, and regulators.

Framing theory provides one of the most widely used conceptual tools for this kind of analysis (Goffman, 1974; Entman, 1993). A frame is an organising principle that selects certain aspects of a perceived reality and makes them more salient, in ways that promote a particular problem definition, causal interpretation, moral evaluation, or treatment recommendation. The frames that dominate media coverage of AI, whether ‘automation threat,’ ‘techno-utopian progress,’ ‘existential risk,’ or ‘humanitarian opportunity’, do not reflect pre-given realities about the technology; they are constructed through editorial choices, institutional pressures, and the structural conditions of media production (Nguyen & Hekman, 2024).

Similarly, the social construction of technology emphasises that technological artefacts acquire meaning through social processes (Leonardi & Barley, 2010; Pinch & Bijker, 1984). Technologies are not value-neutral; they are shaped by the interests, assumptions, and power relations of those who design, deploy, and regulate them. This insight is particularly pertinent for AI, which is overwhelmingly developed in a small number of wealthy countries and by a small number of large corporations, yet whose effects on labour markets, cultural production, information environments, and planetary resources are global. How the media represents this asymmetry, or fails to represent it, is a question of considerable importance for global communication scholarship.

The articles in this Special Issue collectively engage with these theoretical traditions, though they do so from varied disciplinary standpoints. Several draw explicitly on framing theory to analyse the textual content of media coverage. Others use sociological and ethnographic methods to examine the conditions of media production and the professional identities of media workers navigating AI-inflected newsrooms. Still others adopt experimental or quasi-experimental designs to measure the effects of AI tools on specific communication outcomes. The methodological pluralism of the collection is itself a strength: it reflects the genuine complexity of the phenomena under investigation, and it models the kind of triangulated approach that the field requires if it is to produce knowledge adequate to the challenges ahead.

3. Overview of Contributions

The article by Gradoselskaya and colleagues opens the collection with an analysis of how digital media shapes emerging discourses about the future through AI-driven innovation. Using a multi-method approach to the analysis of social media content in the Russian context, the authors demonstrate that media discourses about the future are not peripheral curiosities but constitute a core dimension of how societies understand their diverse spheres of life, from governance and economy to healthcare and education. The study’s empirical contribution lies in its identification of a consistent disjunction between institutional and everyday voices in AI-related discourse. Institutions (e.g., governments, corporations, research bodies) consistently promote visions of AI-driven futures that are simultaneously practically and theoretically contradictory: they promise both economic efficiency and job creation, both enhanced privacy and expanded surveillance, both democratic empowerment and centralised control. Ordinary users, by contrast, receive these messages with pronounced anxiety and scepticism. This finding raises important questions about the communicative strategies of institutions operating in contested technological terrain, and about the conditions under which public trust in AI narratives might be cultivated or eroded.

Two articles address the role of AI in media production, though they do so from complementary angles and in different geographic contexts, making their juxtaposition particularly productive. The contribution by Fayez and colleagues employs a quasi-experimental design to assess the impact of AI integration on media production training across France, Egypt, and the United Arab Emirates. This three-country comparison is methodologically ambitious and substantively illuminating, capturing variation in media education systems, technological infrastructure, and cultural attitudes toward AI adoption. The study’s finding (that AI integration produced notable improvements in student performance across a range of production tasks including editing, media verification, fact-checking, digital media production, and proofreading) contributes to an emerging evidence base for AI-enhanced pedagogy in communication education. Critically, however, the authors are careful not to read these gains as straightforwardly positive; improvements in technical proficiency do not automatically translate into improvements in journalistic judgement, ethical reasoning, or cultural sensitivity.

Hassouni and Mellor’s article on the UAE takes a qualitatively different approach, drawing on in-depth interviews with nine media professionals working in filmmaking, news production, heritage preservation, and content creation. The UAE provides a particularly interesting case because of its pronounced ambitions in both AI development and cultural heritage conservation, ambitions that frequently exist in tension. The professionals interviewed exhibit what the authors characterise as a form of excited ambivalence: they are genuinely enthusiastic about the productivity gains AI affords, yet they are also acutely concerned about AI’s tendency to misrepresent or flatten local cultural specificity. This tension between technological optimism and cultural protectionism is one of the most generative findings in the collection, and it resonates across multiple other contributions.

The article by Yaprak, Ercan, Coşan, and Ecevit addresses the question of whether communication and media curricula keep pace with the AI-driven transformation of professional practice. Through a systematic analysis of sixty-six syllabi from six UK communication programmes and one hundred and seven job advertisements spanning graduate to intermediate-level positions across digital media and communication disciplines, the study maps the current state of AI literacy in formal media education and identifies significant gaps between academic preparation and industry expectation. The application of technology-biased skill-change theory to this dataset yields particularly revealing results. The authors find that AI-focused skills, including prompt engineering, algorithmic literacy, synthetic media detection, and the ethical use of AI tools, are systematically underrepresented in existing curricula, even as they are increasingly demanded by employers. This curricular lag is not simply a practical inconvenience; it has normative implications. If graduates enter the profession without adequate AI literacy, they are less equipped to critically evaluate AI-generated content, to identify algorithmic bias in platform-curated information, or to make ethically informed decisions about AI deployment in their own practice. The authors’ recommendation that AI-focused short courses be incorporated to address gaps in studio-based work is a practical intervention, but the study also invites a deeper conversation about the purposes of media education in an AI-saturated information environment.

Three articles address the perceptions of key professional and institutional actors regarding AI innovation, and together they constitute one of the most geographically diverse clusters in the collection. Peña-Alonso, Peña-Fernández, and Meso-Ayerdi’s survey of Spanish journalists examines the perceived relationship between AI and disinformation. Their findings reveal a striking degree of professional consensus: across gender lines and media genres, journalists overwhelmingly believe that AI will further stimulate the production and circulation of disinformation in an information environment already characterised by epistemic instability. This finding is significant for several reasons. It suggests that media professionals are not naïve about the risks of the technologies entering their own workplaces. It also raises questions about the relationship between professional concern and professional practice: if journalists are worried about AI-driven disinformation, are they taking active steps to counter it, and do they have the tools, training, and institutional support to do so effectively?

Gondwe pivots to a different dimension of AI and technological innovation: the question of how Western media represents innovation emanating from the Global South. Through in-depth interviews with twenty-four media personnel, subject matter experts, and technology entrepreneurs, the study examines coverage of four African innovations: Kenya’s M-Pesa and FarmDrive, Nigeria’s LifeBank, and the NigeriaSat-1 satellite. The findings are striking in their consistency: Western media systematically frames these innovations through humanitarian or developmental lenses, presenting them as responses to poverty or crisis rather than as competitive technological achievements in their own right. This framing has concrete consequences, potentially affecting investment flows, partnership opportunities, and the international standing of African technology ecosystems. The study makes a timely and important contribution to the decolonial turn in global communication research.

Mohammed and colleagues extend Gondwe’s concerns by examining how AI innovations are framed by African news outlets themselves. Their content analysis of seventy-three articles published by fifty-two African news organisations over four years reveals that African media coverage of AI is predominantly optimistic but infused with cautionary notes. African journalists and editors are not simply passive recipients of Western framings; they are active constructors of AI narratives shaped by their own institutional contexts, audience expectations, and professional identities. The tension between optimism and caution in African AI coverage mirrors, and yet also differs from, the tensions identified in other contexts, suggesting that global AI discourse is neither monolithic nor simply derivative.

Two articles address the relationship between AI and the management of information quality in public communication, one focusing on AI as a tool for content optimisation, the other on AI’s role in mediating public responses to misinformation. Taha and Abdel-Qader Abdallah’s contribution analyses the use of AI for social media analytics, assessing five prominent digital analytics platforms to determine their accuracy in sentiment analysis for content strategy optimisation. Their findings indicate that AI-assisted analytics significantly improve content performance, suggesting that AI tools can add genuine value to public communication practice. However, the study also highlights important questions about the reliability and consistency of different platforms, and about the extent to which content optimisation serves the interests of audiences rather than merely the commercial or reputational interests of organisations.

Sonni and colleagues adopt a complementary angle, examining media literacy as a mediating variable in the relationship between AI and misinformation. Using a multi-method approach combining survey, content analysis, and in-depth interviews, the authors find that while the media routinely presents AI as an ameliorating tool for misinformation, its effectiveness is highly contingent on individual users’ media literacy levels. This finding underscores a critical point that is sometimes obscured in technologically deterministic accounts: the social impact of AI is not simply a function of the technology’s capabilities but is mediated by the cognitive, cultural, and institutional contexts in which those capabilities are deployed. Media literacy education thus emerges not merely as a complement to AI governance, but as a prerequisite for its effectiveness.

Santini and colleagues’ investigation into how environmental issues are discussed within conservative groups on social media platforms constitutes one of the most politically engaged contributions in the collection. Guided by framing theory and deploying a multi-method approach that combines quantitative and qualitative analysis, the study reveals that within these online communities, environmental issues are rarely discussed on their own terms. Instead, they are consistently subsumed within larger cultural and political narratives involving war, economic competition, and agribusiness interests. The study’s identification of social media platforms as active agents of anti-environmental framing, rather than merely passive conduits for such frames, is a theoretically important finding with significant practical implications. Platform algorithms, recommendation systems, and community structures shape the conditions of possibility for environmental communication in ways that systematically disadvantage pro-sustainability narratives. The weaponisation of environmental discourse for political ends is not, the authors suggest, an accidental by-product of open online platforms; it is a structurally facilitated outcome. This finding speaks directly to debates about platform governance, algorithmic accountability, and the conditions for meaningful public deliberation on environmental and climate challenges.

Arikewuyo’s investigation into the psychological dimensions of AI-generated fake news exposure among young adults fills an important gap in the literature. While much existing scholarship on disinformation focuses on its production, distribution, or political effects, comparatively less attention has been paid to its psychological consequences for media consumers. Through a survey conducted among young adults, the study demonstrates that exposure to AI-generated fake news negatively affects perceptions of media credibility and is associated with the stimulation of anti-social behaviours. These findings carry significant implications for media psychology, public health communication, and the governance of information platforms. Young adults are among the heaviest users of digital and social media, and they are therefore disproportionately exposed to AI-generated content, including content that is deliberately fabricated. The erosion of trust in media institutions that the author documents is not merely an epistemic problem; it has downstream effects on civic participation, social cohesion, and democratic resilience. The article points toward the need for intervention strategies that are not solely focused on content moderation but also address the psychological and social conditions that make individuals vulnerable to manipulation.

The collection closes with Adjin-Tettey and colleagues’ ethnographic study of AI adoption in the newsrooms of South Africa and Ghana. Through in-depth interviews with eighteen journalists across both countries, the study maps the informal and often unauthorised ways in which AI tools have entered routine journalistic practice in African newsrooms, despite the absence of formal institutional policies or editorial frameworks governing their use. The journalists interviewed are candid about both their enthusiasm for AI-assisted workflows and their sincere ethical concerns. Copyright, attribution, accuracy, and cultural representation emerge as persistent concerns. The finding that generative AI is being adopted in the absence of institutional guidance reflects a pattern familiar from other technological transitions in journalism: practitioners often lead, and institutions follow; sometimes too slowly. The study’s comparative dimension, spanning two countries with different media systems and political economies, adds depth to these observations and invites further comparative work across the African continent.

4. Cross-Cutting Themes and Debates

4.1. The Paradox of Excited Ambivalence

One of the most common findings to emerge across these contributions is what we might term the paradox of ‘excited ambivalence’ of AI. Whether examining Russian social media users, UAE media professionals, Spanish journalists, African newsroom practitioners, or young adults exposed to AI-generated content, the contributors consistently find that AI elicits simultaneous enthusiasm and anxiety from those who encounter it. This is not simply a cognitive inconsistency; it reflects the genuinely ambivalent character of AI as a social phenomenon. AI offers real productivity gains, creative possibilities, and communicative efficiencies, and these benefits are experienced and valued by practitioners across diverse contexts (Marconi, 2020; Munoriyarwa & de-Lima-Santos, 2025). At the same time, AI threatens established livelihoods, complicates epistemic authority, distorts cultural representation, and enables new forms of manipulation and harm (Munoriyarwa, 2024).

This ambivalence should not be resolved in favour of either pole. The challenge for media scholars, educators, and practitioners is to cultivate a form of critical engagement with AI that takes both its possibilities and its dangers seriously, one that resists both uncritical enthusiasm and reflexive rejection. The articles in this Special Issue model such an engagement, and we hope they contribute to a wider scholarly and public conversation attuned to the full complexity of the AI moment.

4.2. Global Asymmetries in AI Discourse

A second cross-cutting theme concerns the profound asymmetries that characterise global AI discourse (de-Lima-Santos & Jamil, 2024). The development of AI is concentrated in a small number of wealthy countries and corporations; the discourse about AI in mainstream international media largely reflects the perspectives, concerns, and frames of those actors; and the governance frameworks being developed for AI largely reflect the interests of powerful states and industries. The contributions to this Special Issue from African, Middle Eastern, and Global South contexts represent an important corrective to this tendency. They demonstrate that AI looks different, and raises different hopes, different fears, and different ethical questions, when viewed from Nairobi, Lagos, Abu Dhabi, or Accra compared to when viewed from Silicon Valley, London, or Beijing.

This insight has methodological as well as substantive implications. It suggests that the field of AI and media studies needs to actively diversify its geographic imagination, its language capacities, its institutional partnerships, and its theoretical frameworks. Western-centric assumptions embedded in dominant frameworks about what counts as ‘innovation,’ what constitutes ‘professional journalism,’ and what makes communication ‘effective’ may be inadequate or actively misleading when applied to non-Western contexts. Building a genuinely global field of media and AI scholarship requires not merely the addition of non-Western case studies to existing frameworks, but a deeper engagement with non-Western theoretical traditions and a willingness to revise foundational assumptions (Mitchelstein & Boczkowski, 2021).

4.3. Media Literacy as a Democratic Imperative

Several contributions converge on the conclusion that media literacy is not merely a desirable individual competency but a democratic imperative in the age of AI (Matusevych et al., 2024; Stamboliev, 2023). If AI enables the production of synthetic media at scale, and if the effectiveness of AI disinformation tools depends on the media literacy levels of individual users, then unequal distributions of media literacy become a form of democratic vulnerability (Romanishyn et al., 2025). Communities with lower average levels of media literacy, whether because of educational disadvantage, limited access to quality journalism, or structural marginalisation, are more exposed to AI-driven manipulation and less equipped to evaluate the provenance and reliability of information (Jacob & Vajjhala, 2025).

This framing connects media literacy education to broader concerns about social justice, digital equity, and democratic resilience. It also has implications for the governance of AI in media contexts. Regulatory frameworks focused exclusively on the supply side (on content moderation, platform liability, or transparency requirements for AI-generated content) will be insufficient if they are not complemented by demand-side investments in media literacy education that are accessible to all citizens, not merely those with the cultural and economic capital to pursue formal education.

4.4. The Ethics of AI in Journalism

The ethical dimensions of AI in journalism are addressed, explicitly or implicitly, in almost every contribution to this Special Issue. From the Spanish journalists’ concerns about AI-driven disinformation, to the UAE professionals’ worries about cultural misrepresentation, to the African newsroom practitioners’ concerns about copyright and attribution, a consistent pattern emerges: AI creates genuine ethical challenges for journalism that existing professional frameworks are ill-equipped to address (Al-Zoubi et al., 2024).

The field requires new ethical frameworks suitable for the AI moment: frameworks that can address questions of source attribution in AI-assisted reporting, responsibilities for AI-generated errors and harms, standards for disclosure of AI use to audiences, and the cultural and political implications of training data composition (de-Lima-Santos et al., 2025). The development of such frameworks cannot be left to technology companies or platform operators alone; it requires sustained engagement from journalism scholars, media ethicists, professional associations, and the practitioners who will be required to implement ethical standards in their daily work.

5. Geographic Scope, Methodological Diversity, and Limitations

This Special Issue is notable for its geographic diversity, spanning Europe (Spain, UK, France), the Middle East (UAE, Egypt), Africa (Nigeria, Kenya, South Africa, Ghana, broadly the African continent), and transnational digital spaces. This breadth reflects the global character of the phenomena under investigation and the editors’ deliberate commitment to assembling a genuinely international collection. At the same time, we acknowledge several important limitations.

First, some world regions are entirely absent from the collection. Neither Latin America, nor South and Southeast Asia, nor East Asia features significantly in the contributions, and the absence of perspectives from China, the world’s second-largest AI economy, is a particularly notable gap. Future Special Issues would benefit from active solicitation of work from these regions.

Second, the collection is predominantly focused on the production and distribution dimensions of media and AI, with comparatively less attention to questions of platform infrastructure, data governance, and the material and environmental impacts of AI on media systems. The environmental costs of AI, the energy consumption of large language model training and inference, the carbon footprint of data centres, and the extractive pressures of rare-earth mineral mining are not extensively addressed in the present collection, though they represent an increasingly urgent domain for media and communication scholarship.

Methodologically, the collection is diverse in ways that are mostly productive but that also create some challenges for synthesis. Articles employ survey research, quasi-experiments, in-depth interviews, content analysis, syllabus analysis, and multi-method approaches. This diversity reflects genuine methodological pluralism in the field rather than disciplinary incoherence, and we believe it enriches the collection. Readers should nonetheless be attentive to the different epistemological commitments and validity criteria associated with different methodological traditions.

6. Conclusions and Future Research Directions

The twelve articles assembled in this Special Issue offer a rich and variegated portrait of the media–AI nexus in a period of rapid and consequential change. They reveal media systems that are simultaneously adaptive and brittle, hopeful and anxious, globally connected and locally differentiated. They document professional communities that are navigating profound uncertainty with a combination of pragmatic experimentation and principled concern. And they point, collectively, toward a set of urgent research agendas that the field of media and communication studies must address if it is to contribute meaningfully to the governance of AI in the public interest.

First, comparative research on AI framing across a wider range of national and linguistic contexts is urgently needed. The articles in this collection have taken significant steps in this direction, but the vast majority of AI framing research to date has focused on English-language, Western media. The development of multilingual, multi-country frameworks and datasets, drawing on the kinds of international research collaborations modelled by contributors to this issue, should be a priority for the field.

Second, longitudinal research on the dynamics of AI adoption in media organisations is needed to complement the largely cross-sectional evidence currently available. How do journalists’ attitudes toward AI change as they accumulate experience with AI tools? How do newsroom cultures evolve as AI becomes more thoroughly integrated into production workflows? How does the audiences’ trust in media shift as they become more aware of AI’s role in content creation? These questions can only be answered through sustained, longitudinal investigation.

Third, the field needs theoretical frameworks that can account for the distinctiveness of AI as a communication technology. AI is not merely faster or more efficient than previous media technologies; it is, in important respects, qualitatively different—capable of generating content that is indistinguishable from human-produced content, capable of personalising communication at massive scale, and capable of operating with a degree of autonomy that challenges established notions of authorship, agency, and accountability. Theoretical frameworks developed for the analysis of earlier media technologies may need to be substantially revised, or new frameworks developed, to capture these distinctive features.

Fourth, the normative dimensions of AI in media deserve sustained scholarly attention. Questions about what constitutes responsible AI in journalism, what the requirements are for media literacy in a democratic public, how global asymmetries in AI development can be addressed, and how the material and environmental costs of AI can be equitably distributed are not merely technical or empirical questions, they are normative questions that require explicit ethical and political reasoning. Media scholars are well-positioned to contribute to these debates, and the present collection models the kind of normatively engaged scholarship that the moment demands.

Finally, and perhaps most fundamentally, the field needs to cultivate genuine intellectual partnerships with scholars, practitioners, and communities in the Global South, not as research subjects but as co-investigators and co-theorists (Mitchelstein & Boczkowski, 2021). The insights offered by contributions from Nigeria, Ghana, South Africa, Kenya, and the UAE in this Special Issue are not merely regional case studies to be incorporated into existing frameworks; they are challenges to those frameworks, invitations to rethink foundational assumptions, and contributions to a genuinely global theory of media and AI. We hope that this Special Issue contributes, however modestly, to the project of building such a theory.