The study, conducted by Sunkyung Shin of Woosuk University and Jooyeun Park of Hankuk University of Foreign Studies, drew on written structured expert interviews with eleven media experts from academia, industry, and national policy research institutes in South Korea. Through qualitative content analysis, the researchers sought to understand how these experts perceive the societal impacts of AI and what policy directions they believe could mitigate associated risks. The findings reveal a landscape of interconnected concerns and opportunities spanning three domains: individual, societal, and industrial.

At the individual level, all eleven participants identified enhanced convenience as a major benefit of AI, largely attributed to recommendation systems, smart home technologies, and automation services that simplify daily decision-making. Experts also pointed to personalized services, improved access to information, and support for creative activities as individual-level advantages. Yet these benefits came with significant caveats. Six participants expressed concern about personal data protection and security, noting that AI systems must continuously learn from users’ daily data, creating persistent vulnerabilities to privacy infringement and information leaks. Others worried about declining cognitive and critical thinking abilities due to excessive reliance on AI, algorithmic bias, and the deepening of the digital divide.

The societal-level analysis revealed even starker tensions. Every single expert identified increased unemployment due to AI-induced job displacement as a major societal risk. Participants described generative AI as likely to reduce demand for human labor, leading to the disappearance of certain jobs or shifts into low-wage roles. As AI moves into professional sectors, overall labor demand may decline, reducing access to quality employment and intensifying industry competition and profit disparities. Four participants also identified the potential for AI to intensify social polarization and inequality, particularly through the concentration of AI technologies and economic benefits within a small number of large firms.

At the industrial level, the experts focused heavily on the media and entertainment sector, where AI is reshaping content production across creation, editing, distribution, and consumption. Ten participants identified increased efficiency in content production as a positive industrial impact, noting that AI-based video production and personalized content delivery contribute to content quality, competitiveness, and accessibility. However, five participants also identified job reduction and role replacement as industrial-level risks, alongside growing polarization between large and small industry actors, intellectual property and copyright disputes, and the spread of fake information and news.

When asked about policy responses to these risks, the experts converged on a striking consensus: AI literacy education was mentioned by all eleven participants as a critical policy response. This unanimous agreement underscores the experts’ belief that expanding public understanding of data practices, misinformation, and human–AI interaction is essential for risk mitigation. Other policy responses included strengthening corporate accountability through self-regulation, mentioned by nine participants, and improving government regulations and legal frameworks, mentioned by seven participants. Experts emphasized that firms developing AI systems should anticipate potential harms and take proactive measures to address them, as neglecting societal impacts could trigger more stringent state intervention.

The study’s most significant contribution lies in its repositioning of AI literacy from a narrow technical skill set to a multidimensional public capability. The analysis identified four core values that should guide AI literacy policy: ethics and responsibility, practical and real-world applicability, social inclusion and accessibility, and autonomy-oriented learning approaches. Experts argued that AI literacy should encompass the capacity to recognize and manage risks such as misinformation, intellectual property conflicts, and potential societal harms, while also providing learning opportunities grounded in practical contexts and ensuring equitable access across different age, income, and social backgrounds.

The emphasis on social inclusion reflects concerns about technological polarization and unequal access to AI-related opportunities. Participants stressed the importance of ensuring that people of all ages and socioeconomic backgrounds have equitable access to AI education and resources, which they view as crucial for narrowing the digital divide and enabling everyone to benefit from AI advancements. The autonomy-oriented dimension highlights the role of user agency, critical thinking, and the capacity for independent evaluation of AI systems, with experts noting that purely regulation-centered models may be limited because coercive or top-down approaches are unlikely to be effective.

South Korea offers a particularly compelling context for this research. The country became the second nation to implement a comprehensive AI legal framework, with the Basic Act on the Promotion of Artificial Intelligence and the Establishment of a Foundation for Trust taking effect in January 2026, modeled after the European Union’s Artificial Intelligence Act. This legislative milestone, combined with vigorous investment by globally competitive firms such as Samsung, Naver, and Kakao, and a technologically literate user base, positions South Korea as a valuable case for examining how policy frameworks and literacy strategies are formulated in a rapidly digitizing environment.

The study’s findings suggest that effective AI governance requires moving beyond the traditional binary of innovation versus control. Instead, the researchers propose a multilayered governance model that simultaneously cultivates user competence, encourages responsible industry behavior, and situates regulatory authority within a broader ecosystem of shared responsibility among government, industry, and civil society. This approach reflects a shift toward co-regulation, in which multiple stakeholders jointly contribute to the development of norms and safeguards for technological advancement and social protection. The researchers acknowledge that their framework is expert-informed and interpretive rather than empirically validated, and they call for future research to engage citizens, digitally marginalized populations, affected communities, and policymakers in its participatory refinement.

Subject of Research: AI governance

Article Title: Developing an AI literacy policy framework for responsible AI governance: perspectives from South Korean media experts

Article References: Shin, S., & Park, J. (2026). Developing an AI literacy policy framework for responsible AI governance: perspectives from South Korean media experts. Frontiers in Communication, 11, Article 1966278. https://doi.org/10.3389/fcomm.2026.1966278

Image Credits: AI Generated

DOI: 10.3389/fcomm.2026.1966278

Keywords: AI literacy, AI governance, South Korea, AI policy, media experts, responsible AI, digital divide, Developing, literacy, policy, framework, responsible

Professor Chan received the third-class award in the category of Educational Science Research for her paper, A comprehensive AI policy education framework for university teaching and learning. The study stands out for its methodological rigour: it employs a mixed-methods design that combines quantitative and qualitative research to gather data from students, faculty, and staff across a range of disciplines at Hong Kong universities. By triangulating survey data with qualitative insights, the research captures how different members of the university community experience and understand the rapid arrival of generative and other AI tools in teaching, learning, and assessment.

The core contribution of the paper is an AI ecosystem education policy framework that spans three interconnected domains: teaching, governance, and operations. Rather than treating artificial intelligence as a purely technical challenge to be managed by information technology departments, the framework positions it as an institutional ecosystem in which pedagogy, rules, and day-to-day administrative processes must evolve together. This systems-level view offers universities a structured way to think about how AI integration reshapes academic environments, and it provides stakeholders with actionable insights into both the opportunities and the risks that accompany adoption.

The timing of the research is significant. Universities worldwide are grappling with how to regulate AI use in coursework, how to prepare graduates for an AI-saturated labour market, and how to maintain academic integrity without stifling innovation. Chan’s framework speaks directly to these tensions, proposing that institutions can move forward with confidence only when innovation is paired with trust, accountability, and meaningful learning. In her response to the award, she said: “I am honoured to receive this award and grateful to MoE for recognising this research. At a time when AI brings both excitement and uncertainty, education must help shape its future. This research offers institutions a framework to move forward with confidence, ensuring that innovation goes hand in hand with trust, accountability and meaningful learning.”

Professor Ng’s award, by contrast, reaches back more than a century and a half. He received the Popular Science Publication Award in the Law category for his book, Political Censorship in British Hong Kong: Freedom of Expression and the Law (1842–1997). The work is the product of an ambitious archival effort involving the decryption and interpretation of confidential documents, including classified communications exchanged between London and Hong Kong across the colonial period. By reading these records against the published legal and political history of the territory, Ng reconstructs how censorship actually operated beneath the surface of official pronouncements.

The book’s central finding is striking and likely to provoke debate among historians and legal scholars. Ng argues that Hong Kong’s freedom of speech and judicial independence were not rooted in British legal traditions, as is often assumed, but were instead products of specific historical circumstances shaped by British policies towards China during the colonial era. In other words, the civil liberties that came to define the territory did not arrive as a straightforward transplant of Westminster-style law; they emerged contingently, as colonial authorities balanced commercial interests, geopolitical calculation, and local pressures over more than 150 years.

Ng reflected on the significance of the award for public engagement with legal history: “I am truly delighted that this legal history research, grounded in declassified records, has been embraced by the public and honoured with this award. Law and history share a core commitment to factual evidence; uncovering the past is essential for us to understand today and guide our future.” The Popular Science Publication Award category itself is notable, as it rewards research output written for a general audience rather than specialists, signalling the Ministry’s interest in scholarship that travels beyond the academy.

The Higher Education Outstanding Scientific Research Output Awards (Humanities and Social Sciences) were established by the Ministry of Education in 1995 to recognise outstanding research projects in the humanities and social sciences at tertiary institutions in the Chinese Mainland. Hong Kong’s tertiary institutions were subsequently included in the scheme, making it a genuinely cross-border accolade for the region’s universities. The awards are regarded as the most reputable and influential honour in the field of social sciences in the nation, and receiving one signifies an awardee’s remarkable contribution to advancing research and development in higher education.

The tenth round of the awards, in which Chan and Ng were honoured, illustrates the scheme’s dual character. On one side sits Chan’s forward-looking study of AI policy, a piece of educational science designed to help institutions navigate a technology that is transforming classrooms in real time. On the other sits Ng’s historical excavation of censorship and civil liberties, a work of legal history that uses declassified evidence to complicate comfortable narratives about the origins of rights. That both projects were recognised under the same programme speaks to the awards’ remit: to reward rigorous, evidence-based research across the full span of the humanities and social sciences, whether it addresses the newest technologies or the deepest archives.

For the University of Hong Kong, the double recognition highlights the institution’s research strength in education and law, two faculties whose work reaches from policy frameworks for emerging technologies to the documentary record of empire. Chan’s mixed-methods framework gives university leaders a template for building AI governance that earns the trust of students and staff, while Ng’s decrypted archives remind readers that the freedoms enjoyed today have contingent, traceable histories. Together, the two awards show how evidence, whether statistical or archival, remains the foundation on which both future policy and historical understanding are built.

Subject of Research: AI policy education and colonial censorship research recognised by the Ministry of Education

Article Title: HKU Professors receive the 10th Higher Education Outstanding Scientific Research Output Award from the Ministry of Education

Article References: HKU Professors receive the 10th Higher Education Outstanding Scientific Research Output Award from the Ministry of Education. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: University of Hong Kong, Ministry of Education, Higher Education Awards, AI policy, legal history, social sciences, Professors, receive, Higher, Education, Outstanding, Scientific

The market dilution argument is deceptively simple. It proposes that even when AI systems do not directly copy or harm any individual copyrighted work, the sheer volume of machine-generated output could crowd human authors out of the marketplace, weakening the economic incentives that copyright law was designed to protect. If a publisher can generate endless novels, illustrations, or articles at negligible cost, the theory goes, human creators may find their works displaced and their livelihoods eroded. This reasoning has begun to appear in courtrooms as a way to extend copyright liability beyond traditional infringement, reaching the training process itself and the outputs of foundation models trained on vast collections of copyrighted material.

Samuelson and her colleagues question whether this theory holds up under scrutiny. In their view, the argument oversimplifies the relationship between human and machine creativity, treating AI as a uniform threat to all creative markets rather than a technology whose effects vary enormously depending on context. The authors contend that any serious assessment of market dilution must account for how AI is actually being used, the extent of human contribution to the works in question, and the specific creators and markets affected. A graphic designer using AI to accelerate routine tasks faces a very different situation from a novelist competing against fully automated fiction, and a stock photography market operates under dynamics distinct from those of fine art or scientific publishing.

This insistence on specificity carries a direct challenge for the courts. The two federal rulings so far have found that training AI models on copyrighted material can qualify as fair use, the legal doctrine that permits certain uses of copyrighted works without permission from rights holders. But those decisions have not settled the broader dispute, and the market dilution theory represents one of the most consequential arguments still in play. If courts accept the idea that generative AI inherently dilutes markets for human-authored works, the consequences could extend far beyond the parties in any single case. As Samuelson and her colleagues write, rulings against fair use on dilution grounds may substantially raise liability risks for all developers and users of generative AI in ways that are difficult to predict.

The authors are particularly concerned about the ripple effects on the scientific community. Generative AI holds considerable promise for advancing research, from literature synthesis to hypothesis generation to domain-specific modeling, but that promise depends in part on the legal permissibility of training models on large collections of existing material. Court rulings that restrict the use of copyrighted works for training could create widespread legal uncertainty, affecting scientist users and builders of generative AI systems, including open-source and domain-specific models that underpin much of contemporary computational research. The warning is not abstract: researchers who fine-tune models on scientific corpora, developers who release tools for public use, and institutions that deploy AI in laboratories all stand to inherit liability risks whose contours no one can currently map.

What the authors propose instead is a discipline of evidence. Claims of harm to creative markets, they argue, should be evaluated through rigorous empirical research focused on particular AI systems and particular creative markets, rather than through broad assumptions about the impact of AI on human authorship. This means longitudinal data tracking how creative industries actually evolve as AI tools spread, alongside surveys of creative practices that reveal how human authors are integrating, resisting, or adapting to machine assistance. The relationship between human and AI creativity is not a zero-sum contest in their account; it is a set of interactions that differ across genres, media, price points, and audiences, and only sustained observation can distinguish genuine displacement from speculative fear.

To make that observation possible, Samuelson and her colleagues suggest collaboration between AI developers and researchers. Access to data about how models are trained, what outputs they produce, and how those outputs circulate in commercial markets would allow scholars to test dilution claims against reality rather than intuition. Such partnerships are not without complications, since proprietary interests and privacy concerns complicate data sharing, but the authors frame them as essential if policy is to rest on evidence rather than anecdote. The alternative, they imply, is a legal regime shaped by contested narratives in litigation, where the loudest claims of harm or benefit substitute for careful measurement.

Even if evidence eventually establishes that AI harms particular creative markets, the authors do not believe courts are the right institution to respond. In their view, legislatures are better equipped than judges resolving individual copyright disputes to develop targeted policies such as licensing schemes or public funding for creators. A legislature can weigh competing interests across an entire sector, design compensation mechanisms, and adjust rules as technology evolves, while a court ruling in a single case must apply legal doctrines to facts presented by the parties before it. The distinction matters because copyright doctrine was built around copying and distribution, not around the diffuse economic effects of a new production technology, and stretching it to cover market dilution risks distorting both the doctrine and the technology.

The Policy Forum lands at a hinge point in the relationship between copyright law and artificial intelligence. The fair use question remains unsettled despite two favorable federal rulings for AI developers, the volume of litigation continues to grow, and the market dilution theory offers plaintiffs a route to liability that does not require proving copying at all. Samuelson and her colleagues have staked out a position that resists both extremes: they do not dismiss the possibility that AI could harm creative markets, but they insist that such harm must be demonstrated, measured, and addressed by institutions capable of crafting proportionate responses. For scientists, developers, and creators alike, the message is that the next few years of litigation and legislation will determine whether generative AI develops within a predictable legal framework or under a cloud of unpredictable liability.

What happens next will shape more than courtroom outcomes. If empirical research confirms that specific markets face genuine displacement, targeted legislative remedies could protect creators without freezing AI development. If research instead shows that human and machine creativity coexist and complement each other in most domains, the dilution theory may fade as a legal strategy. Either way, the authors have reframed the debate around a question that can actually be answered: not whether AI is good or bad for creativity in the abstract, but which systems, which markets, and which creators are affected, and by how much. That reframing may prove to be the most consequential contribution of all.

Subject of Research: The legal and economic debate over whether generative AI dilutes markets for human-created works and the role of copyright law, empirical research, and legislation in resolving it.

Article Title: Does generative AI dilute markets for human-created work?

Article References: Does generative AI dilute markets for human-created work?. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: generative AI, market dilution, copyright law, fair use, Pamela Samuelson, Science journal, AI litigation, creative industries, human authorship, AI policy, licensing schemes, scientific research

The analytical engine behind the argument is the concept of sociotechnical imaginaries, developed by science and technology studies scholars Sheila Jasanoff and Sang-Hyun Kim. These imaginaries are collectively held visions of desirable futures that a society pursues through science and technology, and they do more than reflect values; they actively shape which questions get asked and which get deferred. In East Asia, Son argues, the dominant imaginary has been one of catch-up and national ascent, a narrative in which technology functions primarily as the vehicle for escaping historical subordination and achieving prosperity. Within such an imaginary, ethical scrutiny of technology itself can appear not merely unnecessary but almost disloyal, a brake on the national project at precisely the moment acceleration matters most.

The first structural tendency Son names is importer’s pragmatism. For decades, East Asian economies consumed technologies designed, standardized, and philosophically pre-digested elsewhere. When the artifacts arrive ready-made, the ethical debates that accompanied their creation arrive, if at all, as afterthoughts or foreign imports. The philosophical labor of asking what a technology should be, rather than merely how well it works, was performed in other languages, other institutions, and other societies. Importing nations became extraordinarily skilled at adoption, refinement, and deployment, but the muscle of first-principles normative inquiry atrophied. The result is a policy culture fluent in technical specifications and market strategy but comparatively silent on the deeper questions of what these systems do to human relationships, labor, and dignity.

The second tendency, pursuer’s competitiveness, compounds the first. Nations that perceive themselves as chasing the technological frontier organize their entire innovation ecosystems around speed. Son points to South Korea’s recent drive to be among the world’s top three AI powers, including the selection of elite teams to build a sovereign AI model, as a contemporary expression of this logic. In a race, the metric is velocity, and ethical deliberation is structurally coded as friction. Frameworks such as the country’s new comprehensive AI law, enacted as the first of its kind, attempt to attach safety and trust requirements to development, but Son’s analysis suggests that legislation alone cannot counteract an imaginary in which national prestige is staked on being first. The competition of speed, he argues, must become a competition of direction.

The third tendency is the collectivist narrative of progress. Where technological advancement is framed as a collective national achievement, individual harms can be reframed as acceptable sacrifices on the road to shared glory. Son traces this pattern through a sobering catalogue of Korean disasters: the Seongsu Bridge collapse, the Sampoong Department Store collapse, and the Wenzhou train collision, alongside the Hwang Woo-suk stem cell fraud scandal. Each episode, in his reading, illustrates how the imperative of progress can suppress dissenting voices, marginalize whistleblowers, and delay accountability until catastrophe forces reckoning. Applied to AI, the same collectivist logic surfaces in policies like the rapid rollout of AI textbooks in schools, where documented confusion among students and teachers and concerns over digital overuse were subordinated to the national narrative of educational transformation.

The fourth tendency is perhaps the most culturally distinctive: the limited centrality of what Son calls the Frankenstein complex. In Western societies, Mary Shelley’s creature and its descendants have seeded a deep cultural anxiety about creations that turn on their creators, an anxiety that fuels public resistance, precautionary regulation, and a rich literary and philosophical tradition of technological self-doubt. Thinkers from Jacques Ellul to Hans Jonas built ethics on this wariness. East Asian societies, by contrast, have largely absorbed technology without an equivalent mythic register of rebellion and retribution. The absence of this cultural brake has eased acceptance and accelerated diffusion, but it has also removed a natural trigger for public deliberation. When nothing in the collective imagination insists that new powers demand new humility, normative debate must be built deliberately rather than inherited.

Son’s case study of Korean AI adoption shows how these four tendencies converge in the present. The country has positioned itself as an attractive test bed for global AI companies, a role that brings investment and visibility but also imports the industry’s well-documented externalities, from the exploitation of low-paid data workers abroad to embedded gender bias in generative systems. Domestic civil society groups, such as the Institute for Digital Rights, have produced reports on AI’s human rights implications, yet the policy mainstream remains oriented toward development targets. Even labor conflict carries the pattern: when Hyundai’s union battled the rollout of Atlas robots, the dispute was framed primarily as an industrial relations problem rather than an occasion for society-wide reflection on automation’s direction. The structural tendencies do not prevent ethical debate, but they consistently narrow its scope and blunt its force.

The paper’s constructive proposal is deliberately not a call for culturally distinctive principles. Son explicitly declines to argue that Confucian benevolence or Buddhist non-attachment should be grafted onto AI governance as a regional signature. Such an approach, he suggests, misidentifies the problem as one of content when it is actually one of structure. Instead, he outlines a fourfold redirection of East Asian techno-modernity: from passive importer to responsible developer, meaning that nations must now own the full normative consequences of the systems they build and deploy; from competition of speed to competition of direction, rewarding societies that choose better trajectories rather than merely faster ones; from national collectivism to expanded responsibility, extending the circle of moral concern beyond the nation to workers, users, and communities elsewhere who bear the costs of AI supply chains; and from faith in progress to strategies of survival and human flourishing, a shift in the underlying imaginary itself.

The implications reach well beyond Korea. As AI development globalizes, the assumption that ethical frameworks can simply be exported from the societies that built the technology is increasingly untenable, and the temptation to answer with a menu of regional principles grows. Son’s analysis offers a sharper diagnostic: the question is not which tradition supplies the right maxims, but which structures allow any maxims to matter. A society whose imaginaries celebrate acceleration, whose institutions reward catching up, and whose culture lacks inherited scripts of technological suspicion will neutralize even the most elegant ethical principles. Conversely, restructuring those imaginaries, through democratized technology governance of the kind theorists like Langdon Winner and Andrew Feenberg have long advocated, could make deliberation constitutive rather than ornamental.

What makes the argument resonate now is the timing. East Asia is no longer merely importing AI; Korea has enacted the world’s first comprehensive AI safety law, launched national committees to claim a place among the top three AI powers, and embedded AI into classrooms. The region has crossed the threshold from pursuer to potential leader, and with that transition comes the loss of the importer’s alibi. If Son is right, the next chapter of East Asian AI will be judged not by how faithfully it applies ancient wisdom to modern machines, but by whether societies forged in the crucible of catch-up development can invent a new imaginary, one in which asking where technology should go is as prestigious as asking how fast it can get there.

Subject of Research: Structural analysis of East Asian AI ethics and techno-modernity using South Korea as a case study

Article Title: Do we need an East Asian AI ethics?: South Korea as a paradigmatic case

Article References: Son, W.-C. (2026). Do we need an East Asian AI ethics?: South Korea as a paradigmatic case. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03336-8

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03336-8

Keywords: AI ethics, East Asia, South Korea, techno-modernity, sociotechnical imaginaries, Confucianism, Frankenstein complex, AI governance, AI policy, philosophy of technology, national competitiveness, responsible innovation