Beyond the Tool: Artificial Intelligence and the Transformation of Information Ecosystems

Artificial intelligence (AI) has increasingly embedded in different domains of social life, including work, communication, and cultural production. Many people recognize its potential to improve problem-solving, enhance efficiency, and support human decision-making (Joksimovic et al., 2023; Kennedy et al., 2025). At the same time, both the public and AI experts have expressed serious concerns about its social consequences, including the spread of inaccurate information, the erosion of meaningful social relationships, the displacement of human labor, and other risks generated by automated systems (McClain et al., 2025; Monteith et al., 2024).

These tensions are particularly acute in information ecosystems, where AI is transforming how information is produced, distributed, and governed. Generative AI synthesizes text, images, audio, video, and synthetic personas, lowering the cost of communicative production and expanding content generation at unprecedented scale. Algorithmic recommendation systems organize information circulation by determining which content becomes visible or marginalized. AI is also involved in information governance by supporting content moderation. These developments increase the efficiency, scalability, and personalization of information systems. Meanwhile, they intensify concerns about the authenticity, credibility, and authority of information. The proliferation of synthetic content blurs the distinction between human-generated and machine-generated communication; AI-generated misinformation complicates political governance (Kaplan, 2020), and algorithmically recommended information may reproduce bias against vulnerable social groups (Arora et al., 2023). AI therefore disrupts the established grounds on which people interpret, evaluate, and trust information, while reshaping the conditions under which information is produced, verified, and held accountable.

These developments suggest that AI is more than a technical tool. It increasingly acts as a sociotechnical actor embedded in information ecosystems. Its significance lies at two levels: how it interacts with professionals, users, and communities to change the way information is produced, interpreted, and evaluated; and how it transforms the media and cultural industries through which content is created, distributed, and consumed. This special issue therefore organizes its contributions around two related perspectives. The first adopts a micro-level perspective by examining how different information actors engage with AI. The second adopts a meso-level perspective by examining how AI transforms the industrial structures of media and cultural industries. Together, these perspectives show how AI reshapes information ecosystems at both the level of everyday practice and the level of industry structure.

AI, Information Actors, and Changing Practices

The first set of articles examines how AI shapes the practices of different information actors, including fact-checkers, journalists, platform users, and language communities. Dierickx and van Dalen's (2026) study examines how professional fact-checkers use generative AI to support fact-checking and shows that their adoption of generative AI is driven less by trust than by perceived usefulness. In practice, fact-checkers manage the risks of using generative AI by limiting it to low-risk and auxiliary tasks, while preserving human editorial oversight and manual verification in fact-checking.

Liu and Buente (2026) extend this discussion to TikTok, comparing fact-checking practices among professional and public fact-checkers. The study shows that platform affordances and creator cultures shape how fact-checking is practiced. Professional fact-checkers emphasize authority, evidence, structure, and standardized forms of expression, whereas public fact-checkers rely on accessible language, visual narration, and engagement-oriented strategies. These differences suggest that fact-checking is shaped by the communicative norms and visibility logics that platforms afford.

Hendrickx and Van Coppenolle's (2026) study examines how news organizations frame AI in public discourse. Using Belgium's De Standaard, a Dutch-language daily newspaper, as a case, the study identifies 12 news frames through which AI is represented in news coverage. The findings show that media coverage frames AI through a balanced and diverse set of positive, neutral, and negative frames, including “AI as a handy tool,” “AI threatens human identity,” and “AI can be regulated.” The diversity of these frames suggests that news organizations actively construct the public meanings through which AI is understood and debated.

News organizations not only frame AI for the public but also incorporate AI into their professional routines. Zhang et al. (2026) examine what drives journalists to adopt AI in news work. Drawing on a national survey of 652 Chinese journalists, the study integrates the Technology Acceptance Model and the Theory of Planned Behavior to examine how perceived usefulness, perceived ease of use, subjective norms, attitudes toward journalistic AI, and perceived behavioral control affect AI use in journalism. The findings show that journalists are more likely to adopt AI when they perceive it as useful for their work and when editors, colleagues, and professional communities support its use.

Building on the framework of algorithmic dependence, Liu and Qiao's (2026) study examines how exposure to algorithmic apps can be associated with lower levels of news knowledge through users’ algorithmic dependence. The study further identifies perceived information narrowing, which refers to users’ awareness that algorithmic systems may limit the range of information they receive, as a form of friction or self-regulatory awareness that buffers this negative association. This finding suggests that algorithmic literacy, even in its most basic form, can be a resource for users navigating algorithm-driven information environments.

Aiseng's (2026) study shifts the discussion of algorithmic bias from a technical problem to a structural one. Using algorithmic audits, together with interviews and focus groups, the study finds that marginalized indigenous languages such as Setswana, Tshivenda, and Xitsonga receive limited visibility in algorithmic feeds and have higher error rates in translation and voice recognition. Building on these findings, Aiseng (2026) proposes a decolonial framework for designing and deploying AI technologies that prioritize African linguistic rights, cultural epistemologies, and community-driven innovation.

These studies show that AI's influence on information practices is shaped by professional norms, platform affordances, user awareness, and structural inequalities. What emerges across these cases is a consistent tension: AI expands the efficiency and reach of information work, yet also reproduces existing hierarchies of visibility and representation.

AI and Changing Media and Cultural Industries

The second set of articles examines how AI shapes the operational logic of different industries. Manovich's (2026) article focuses on the creative industries and conceptualizes generative AI as an artistic medium. AI shapes creative production through a tension between variability and control: it enables creators to generate variations quickly, yet this expanded variability comes at the expense of precise control over details. Moreover, because AI learns from existing artworks, its creativity remains largely bounded by existing aesthetic conventions rather than producing radically new visual languages, a tendency Manovich characterizes as anti-avant-garde. The article also raises a fundamental question about artistic identity: if AI can easily generate multiple styles, artists can no longer rely on stylistic uniqueness to distinguish themselves. Yet the article does not dismiss AI's potential. Instead, it asks what AI can genuinely enable that no previous medium could, pointing to meta-creativity as one possible answer: the acceleration of creative experimentation and the building of custom tools.

Arif et al.'s (2026) article extends this discussion to the entertainment industry by examining AI-generated performers. Through the case of Tilly Norwood, the article shows that AI performers raise questions about authorship, bias, and personality rights. Specifically, authorship becomes blurred when entertainment products are created through a hybrid network of humans, machines, algorithms, and platforms. Bias is also reproduced, as Tilly Norwood's appearance recapitulates Hollywood's dominant aesthetic conventions, including whiteness and blonde femininity, suggesting that AI-generated performers may amplify existing gender and racial biases. Legal exposure also extends beyond copyright to personality rights, as AI can simulate a performer's voice and identity without consent. Beyond these issues, one of the article's most powerful critiques concerns meaningful work. Performance matters not only because it generates income, but also because it provides autonomy, dignity and recognition. The displacement of human performers by AI actors therefore raises fundamental questions about labor, identity, and the conditions under which creative work retains its human significance.

Galily's (2026) article examines how AI-driven technologies and immersive platforms are reshaping content production and audience engagement in sports broadcasting. The article shows how intelligent automation enables scalable, real-time content production that was previously dependent on labor-intensive manual processes, compelling broadcasters to rethink their production workflows, platform partnerships, and revenue strategies. Beyond production, the article argues that AI is transforming the nature of audience participation proposing conversational broadcasting as an emerging model in which generative AI enables fans to interact with live content through natural language, shifting audiences from passive viewers to active participants in a data-driven media experience.

Weber et al. (2026) examine the relationship between publishers and platforms in the German journalism market, combining market analysis with expert interviews. The article shows that publishers and platforms are mutually dependent, and that publishers can strengthen their position through strategies such as directing users to their own services, content withholding, and multi-platform approaches. The article further extends this argument to AI, showing that because AI systems depend on high-quality journalistic content to function effectively, publishers gain new spaces of negotiation through licensing and data-access agreements. Platformization, the article argues, is not a hegemonic process but a dynamic relationship in which publishers retain agency.

These articles show that AI's influence extends beyond individual practice to the structural conditions of creative, entertainment, sports, and journalism industries. Across these sectors, AI expands the speed and variability of creative production, introduces new forms of synthetic performance, reorganizes audience engagement through immersive and conversational media, and reshapes the power relations between platforms and content providers. What connects these cases is a shared dynamic, that is, AI does not simply optimize existing industry arrangements but alters the terms on which authorship, labor, and value are defined and contested.

Conclusion

This special issue examines how AI shapes information ecosystems from both individual and industry perspectives. The contributions show that AI occupies multiple communicative roles in information production and distribution. Echoing Sundar and Lee's (2022) typology of AI roles, AI can generate content as a creator and supports journalists as a co-author in the production of news. At the same time, it acts as a curator that verifies, filters, and organizes information through fact-checking practices. In the sports industry, AI operates as a converser interacting with users in real time to engage audiences. Yet, these communicative roles do not fully capture AI's broad significance. The studies in this issue demonstrate that AI increasingly operates as a sociotechnical actor whose influence extends beyond any single communicative function. It reshapes the conditions under which industries operate, creative identities are formed, and the relationships between producers and consumers are negotiated.

As AI rapidly expands across social domains and industrial sectors, future research should move beyond identifying the factors that drive or hinder AI adoption and examine how people can engage with AI more effectively and responsibly. Sociotechnical perspectives offer a particularly promising direction, as they attend to how AI co-evolves with the organizational structures, professional norms, and power relations within which it is embedded. Future work should also extend political-economic perspectives by analyzing how AI reorganizes labor and production, and reshapes power relations among stakeholders. These changes raise social and ethical concerns about bias, misinformation, surveillance, and inequality, making it necessary to ask what values AI advances and whose interests it serves.