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

Introduction

The global landscape is experiencing a significant transformation as artificial intelligence (AI) technologies continue to develop rapidly and diffuse across sectors. Although artificial intelligence is not a newly emerging concept, it has recently gained unprecedented attention across industries, largely driven by the initiatives of major technology firms such as OpenAI, Google, and Microsoft. Among these developments, OpenAI's ChatGPT has become particularly influential, serving as a landmark innovation at the center of AI tools powered by deep learning algorithms and applied across diverse domains (Haque and Li, 2025). Building on these advancements, AI has now reached a stage where machines are capable of replicating essential human cognitive abilities such as learning, reasoning, and perception, which are increasingly being applied in sectors including healthcare, education, manufacturing, finance, and criminal justice (Berman et al., 2024).

In the healthcare sector, AI is increasingly functioning as an informal advisory tool, with applications that include medical image analysis and the identification of high-risk patients (Lee et al., 2023). In education, AI is being utilized as a virtual assistant to support personalized learning (Sajja et al., 2024), while in finance, it is used to bridge knowledge gaps and enhance investment decision-making (Dixit et al., 2024). These examples demonstrate how AI is generating concrete changes by improving productivity, reducing operational costs, and supporting more effective decision-making processes. Accordingly, AI is widely regarded as a catalyst for innovation across industries. According to a report by Research and Markets (2024), the global AI market is expected to grow at a compound annual growth rate (CAGR) of 33.81 percent between 2024 and 2030, reaching approximately USD 1,057.47 billion by 2030. This forecast reflects the broad applicability and growth potential of AI technologies, indicating that AI is already playing a central role in shaping industrial and societal transformations worldwide.

The increasing integration of AI into daily life suggests that it has become a fundamental component of modern society. Although AI-driven innovations offer new opportunities, they are also giving rise to various concerns in different sectors (Behera et al., 2025). In particular, the emergence of technical risks, ethical and moral challenges, and legal issues associated with AI use has become a prominent issue in an increasingly AI-dependent world (Cools and Diakopoulos, 2026). These concerns are intensifying as a result of the unprecedented pace of development, wide-ranging applicability, and profound societal impact of AI technologies, all of which contribute to heightened uncertainty about the future.

Public skepticism toward AI has also contributed to increasing calls for ensuring the stability and reliability of AI applications (Coltri, 2024; Tal and Connelly, 2025). In this context, there is a growing societal need not only for a sound understanding of AI and the ability to use it effectively, but also for the capacity to adapt flexibly to AI-induced changes. As AI continues to influence everyday experiences and broader institutional systems, there is increasing recognition of the need for more structured policy discussions and greater emphasis on AI literacy. Such literacy is essential for strengthening individuals’ ability to engage critically and competently with these technologies.

Reflecting this shift, academic discourse in various fields including media and communication studies, law, and public policy has expanded to include not only AI policy but also AI literacy policy (Behera et al., 2025; Chauhan and Gullapalli, 2024; Van Ments et al., 2025; Van Wynsberghe, 2021). However, much of the existing discussion remains centered on regulatory and normative dimensions (Kumar and Choudhury, 2023; Tallberg et al., 2023), while deliberative approaches have received relatively limited attention. This imbalance has restricted the extent to which such policies reflect the diverse needs and expectations of different societal groups, thereby reducing both their effectiveness and public acceptance.

To address these limitations and establish an AI ecosystem that is better aligned with real social demands, there is a pressing need for empirical and practice-oriented approaches. Rather than focusing exclusively on technological advancement, AI-related policies should incorporate deliberative perspectives grounded in the lived experiences and perceptions of individuals. Through participatory policy design that places human values and social contexts at the center, more realistic and widely accepted policy directions can be developed.

In light of these considerations, this study focuses on the case of South Korea, a country that is actively pursuing AI innovation while also working to ensure safety and public trust. Specifically, this research examines perceptions of AI and related policy orientations by conducting written structured expert interviews with media scholars and industry experts who possess a high level of expertise in AI technologies. By doing so, the study not only explores the developmental potential of AI but also investigates its ethical and societal implications. The findings aim to provide insight into more contextually appropriate and socially responsive directions for future AI policy.

Literature review

Age of artificial intelligence

AI was first introduced at the 1956 Dartmouth Conference, where it was defined as “making machines behave in a way that a human being would call intelligent” (McCarthy, 2007; McCarthy et al., 2006). Since then, advancements in computing performance and the development of convergence technologies have led to the emergence of innovative approaches such as large-scale language models, machine learning, and natural language processing (Zawacki-Richter et al., 2019). These developments have laid the groundwork for machines to replicate human intelligence and perform cognitive tasks including learning, reasoning, and problem solving. As a result, AI has become a central driver of innovation while also amplifying conceptual ambiguity surrounding the term (Collins et al., 2021).

Although AI technology is rapidly expanding across industries and society, a clear and universally accepted definition of AI has yet to be established. In this context, Wang et al. (2023) defined AI as “a new technological science that aims to mimic, extend, and advance human intelligence.” Similarly, Chiu et al. (2024) described it as “the ability of machines to perform tasks performed by intelligent beings.” Berente et al. (2021) further characterized AI as “the frontier of computing development that references human intelligence to solve complex decision-making problems.” Despite differences in these definitions, prior studies have identified common characteristics that help distinguish AI technologies from non-AI technologies. Specifically, AI is associated with human-like cognitive functions, while non-AI technologies are not. Drawing on this common ground, the present study defines AI as “machines or technologies that mimic human abilities and skills to the extent that they are perceived as intelligent in solving problems.” This definition not only encompasses the traditional view of AI as mimicking human capabilities, but also acknowledges the potential for AI to evolve beyond human domains, producing broader societal implications.

Based on this definition, AI technology extends beyond a purely technical domain and exerts both direct and indirect influence across various fields, including science, education, and the arts. It contributes to individual and corporate productivity, as well as national-level economic growth. Given its far-reaching impact, AI has emerged as a key area of interest across industry, academia, and government. While private companies such as OpenAI, Google, and Microsoft are at the forefront of technological advancement, governments worldwide are also engaging in strategic efforts to secure leadership in AI technologies and industries. In particular, the convergence of AI with next-generation mobile communication networks, namely 6G, is anticipated to give rise to intelligent communications, which are emerging as a new paradigm in the technological hegemony race, thereby further amplifying the significance of AI (Cui et al., 2025). Accordingly, the United States, the European Union, and China, for example, have identified AI as a core driver of future national competitiveness. These nations are making significant investments not only in research and development but also in education, aiming to cultivate talent aligned with the evolving technological paradigm (Carrozza et al., 2022; Dixon, 2023).

In the middle of this global AI race, the South Korean government and private sector have also recognized the strategic value of AI and are actively pursuing initiatives to transition toward an AI-driven society. As part of this effort, the government announced the “AI National Strategy” and the “Data and AI Economy Activation Plan” in 2019, positioning AI as a core engine of future economic growth. In 2020, it launched the “Digital New Deal,” a nationwide initiative promoting digital transformation through advanced technologies such as AI, big data, and cloud computing. Alongside these policy measures, Korean firms are also accelerating the development of AI technologies and services. Major corporations such as Samsung, Naver, and Kakao, together with an expanding ecosystem of startups, are playing pivotal roles in shaping the domestic AI industry.

Alongside government-led initiatives, South Korea's private sector has played a central role in accelerating the development and adoption of AI. Large technology companies have progressively incorporated AI into digital platforms, consumer services, and everyday technologies, contributing to the broader diffusion of AI across Korean society (Yoo et al., 2020; Yoo et al., 2023). More recently, advances in generative and conversational AI have further expanded the scope of AI-based services, with domestic firms increasingly focusing on personalized and user-centered applications (Kakao Corp., 2024). These developments indicate that AI in South Korea is evolving beyond a specialized technological domain and becoming increasingly embedded in the country's broader digital and service ecosystem.

The expansion of South Korea's AI industry is also supported by a growing startup ecosystem. Emerging AI firms are contributing to technological innovation and market diversification while extending their activities beyond the domestic market to the global AI landscape (Nam et al., 2024). This industrial momentum is reflected in the projected growth of the country's generative AI market, which is expected to increase from USD 2.14 billion in 2025 to approximately USD 8.77 billion by 2031 (Statista, 2025). Taken together, the convergence of established technology firms, emerging startups, and a rapidly expanding AI market illustrates the increasing economic and societal significance of AI in South Korea. As AI becomes more deeply integrated into industries, services, and everyday practices, understanding its broader societal implications and developing appropriate policy responses have become increasingly important.

The risks of misunderstanding and unreflective use of AI

Currently in Korea, AI technology is being applied across a range of domains including smartphones, smart home appliances, search services, and personalized AI platforms, exerting a substantial influence on everyday life that extends beyond simple work assistance. However, despite the increasing prominence of AI due to its rapid development and widespread application, the general public is finding it increasingly difficult to fully understand and effectively utilize the technology (Brauner et al., 2023). In other words, as the diffusion of AI accelerates, a lack of user awareness regarding the complexity of the technology may lead to unintended consequences and increase the likelihood of inadequate responses to emerging risks (Carolus et al., 2023).

This phenomenon has been repeatedly observed not only in the development and diffusion of current AI technologies but also throughout the spread of various past innovations. In certain cases, the unintended side effects of these technologies have escalated into broader societal issues. A notable example is the Metaverse. The core feature of Metaverse technology, hyper-realistic connectivity, provides users with a high level of immersion and enhances the sense of realism associated with crimes occurring within the platform. This has resulted in psychological impacts similar to those experienced in real-world incidents. In South Korea, such effects have sparked serious public debates surrounding related events (Shin and Park, 2025). In this regard, new technologies inherently involve not only positive benefits but also potential risks. To effectively understand and respond to such risks, it is essential to clearly identify the fundamental characteristics of the technologies in question (Seo et al., 2015).

In this context, understanding the fundamental properties of AI technology is crucial for comprehensively recognizing not only its potential benefits but also the associated risks. AI is typically characterized by three core attributes: autonomy, learning capability, and inscrutability (Berente et al., 2021). These characteristics offer a fundamental analytical framework for understanding both the utility and the limitations of AI technology. First, autonomy refers to the capacity of AI systems to operate independently, without direct human intervention (Murray et al., 2021). While this autonomy enhances technical efficiency and user convenience, it simultaneously entails the risk of unpredictable behavior and unintended consequences. For instance, navigation services may experimentally direct certain users to suboptimal routes in order to enhance the overall efficiency of the system (Liu et al., 2016). While such an approach can contribute to service improvement at the system level, individual users are often unaware of their participation in these experiments and lack control over their routing. This lack of transparency can lead to significant issues, particularly in emergency situations. Therefore, while the autonomy of AI offers considerable benefits in terms of efficiency and convenience, it also entails significant risks related to uncontrollability and unpredictability.

Next, the learning capability of AI refers to its ability to continuously improve performance based on accumulated data and experience, which is realized through various machine learning techniques, including deep learning (Janiesch et al., 2021). However, if biased or inaccurate data are used during the training process, AI systems are likely to reproduce these flaws, resulting in skewed or erroneous outcomes (Caliskan et al., 2017). For instance, a study by Angwin et al. (2022) found that COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), a risk assessment algorithm used in the United States to predict recidivism, disproportionately assigned higher risk scores to Black defendants compared to White defendants. This example highlights how AI algorithms can perpetuate and even amplify existing social biases by internalizing the distortions embedded in training data.

Finally, inscrutability refers to the difficulty in interpreting how AI algorithms operate or produce outputs, often to the extent that even domain experts may struggle to understand them (Arrieta et al., 2020). As AI technologies are increasingly applied in complex domains, the underlying algorithmic structures have become more intricate. This complexity has led to growing challenges for both general users and experts in comprehending the reasoning behind AI-driven decisions (Brasse et al., 2023). Such concerns about AI are particularly acute in healthcare. Evidence synthesized in a recent systematic review indicates that AI-driven clinical decision support systems are used across many care settings; yet their interpretability often falls short of clinicians’ and patients’ needs, thereby undermining trust, accountability, and the safe adoption of these systems (Xu et al., 2023). This evidence indicates that interpretability must be treated as a core design and evaluation priority if AI tools are to be both reliable and ethical in practice.

In summary, while the rapid proliferation of AI technology and its integration into everyday life have enhanced social efficiency and convenience, they have also amplified the potential for novel types of risk due to AI's inherent characteristics, including autonomy, learning ability, and inscrutability. These risks tend to be exacerbated in contexts where there is insufficient understanding of the systems or a lack of critical engagement with their implications. As a result, the unchecked diffusion of AI has been described as a “race to the bottom” (Coeckelbergh, 2025), suggesting that the stronger and more pervasive AI becomes, the greater and potentially unmanageable the associated risks and problems will be. To ensure the ethical and effective use and diffusion of AI, it is essential to move beyond passive technological acceptance and instead adopt a reflective approach that is grounded in an objective understanding of the operational principles and limitations of AI systems.

Guided by this problem awareness, the present study seeks to systematically examine both the positive and negative impacts of AI technology on contemporary society. Focusing on South Korean media experts with a high level of technological literacy, the study analyzes their perceptions of key AI-related influence factors across three domains: individual, societal, and industrial. Based on this approach, the following research questions are proposed.

• RQ1: How do media experts in South Korea perceive the major impacts of AI technology?

Responding to risks arising from AI expansion

As previously noted, the rapid diffusion of AI technology in society has been accompanied by various risks and side effects, contributing to a growing sense of public concern. These concerns have sparked increased interest in AI risk management and user protection (Hagendorff and Wezel, 2020), thereby catalyzing the global expansion of AI policy discourse. In particular, discussions on AI governance have evolved into international efforts to establish regulatory and institutional frameworks for the responsible deployment of AI. These efforts are often informed by critical perspectives on technology-centric development that lack a holistic understanding of the technology itself.

Within the broader global discourse on AI policy, the United States has taken a leading role in establishing foundational frameworks. In February 2019, the U.S. government articulated its strategic objective of maintaining leadership in AI through Executive Order 13859, “Maintaining American Leadership in Artificial Intelligence” (U.S. Federal Government, 2019). In December 2020, Executive Order 13960, titled “Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government,” was issued to provide ethical principles and practical guidelines for federal agencies in designing, developing, procuring, and deploying AI technologies (U.S. Federal Government, 2020). Further institutional progress occurred on January 1, 2021, with the enactment of the National Artificial Intelligence Initiative Act of 2020 (U.S. Congress, 2020), which established the National AI Initiative Office and the National AI Advisory Committee to coordinate and advise on national AI strategies.

Subsequently, on October 17, 2022, the Artificial Intelligence Training for the Acquisition Workforce Act was enacted to enhance the competencies of federal employees in utilizing AI technologies (U.S. Congress, 2022a). This legislation emphasized the need for structured educational programs to support responsible AI adoption within the public sector. Later that year, on December 23, the Advancing American AI Act was passed to strengthen national competitiveness in AI and to support the development and implementation of related programs (U.S. Congress, 2022b). Most recently, in November 2023, the Biden administration issued Executive Order 14110: “Safe, Secure, and Trustworthy Artificial Intelligence,” emphasizing the proactive mitigation of potential AI-related risks and the formulation of ethical standards for both public and private sector AI usage (Biden, 2023). Collectively, these measures reflect a comprehensive and multifaceted policy approach to managing the benefits and challenges of AI development in a balanced manner.

In a parallel development, the European Union (EU) officially adopted the Artificial Intelligence Act (AIA) in May 2024, marking the world's first comprehensive legal framework regulating AI (ACT, EU Artificial Intelligence, 2024). The AIA, finalized following a nearly three-year consultation process initiated in 2021, is scheduled to be fully enforced by mid-2026 (European Parliament, 2024). Central to the AIA is a risk-based approach that imposes graduated regulatory obligations depending on the risk levels posed by AI systems (Ebers, 2025). Notably, the Act imposes stringent requirements on high-risk AI systems that could significantly impact health, safety, or fundamental rights, mandating pre-market conformity assessments, ongoing post-market monitoring, and comprehensive risk management protocols (European Commission, 2024). Through these measures, the AIA seeks to mitigate potential harms while safeguarding human-centered values, positioning itself as a model for responsible and ethical AI governance.

Amid this growing momentum of international AI policy development, South Korea is actively working to establish a robust legal and institutional foundation aimed at fostering AI advancement and building societal trust. In December 2019, the South Korean government released its National Strategy for Artificial Intelligence, outlining a vision to become a global leader in the field (Ministry of Science and ICT, 2019). This strategy incorporates comprehensive measures to promote industrial growth, institutional readiness, and proactive responses to potential misuse and social risks associated with AI. Building upon this foundation, in December 2020, the government unveiled the Roadmap for the Improvement of AI Laws, Institutions, and Regulations, which set forth specific policy directions for ethical AI utilization and sustainable industrial development (Ministry of Science and ICT, 2020). Reinforcing this rights-based framework, the National Human Rights Commission of Korea introduced the Human Rights Guidelines on the Development and Use of Artificial Intelligence in May 2022 to prevent discrimination and rights violations across AI life cycles (National Human Rights Commission of Korea, 2022). Subsequently, in August 2023, the Personal Information Protection Commission announced the Policy Direction for the Safe Use of Personal Information in the Age of Artificial Intelligence, which proposed strategies to safely utilize data while minimizing privacy risks and promoting innovation (Personal Information Protection Commission, 2023).

A particularly noteworthy development is that South Korea became one of the early adopters of comprehensive AI legislation. In December 2024, the National Assembly passed the Basic Act on the Promotion of Artificial Intelligence and the Establishment of a Foundation for Trust, modeled after the EU's AIA (Jeong, 2024). Having taken effect in January 2026, this legislation reflects South Korea's evolving policy framework aimed at advancing AI technologies while safeguarding ethical standards and user rights.

As AI technologies continue to proliferate, countries worldwide are intensifying policy efforts to manage associated risks and ensure transparency and accountability. Within this context, South Korea's multifaceted policy strategies are particularly salient. As mentioned earlier, South Korea is emerging as a leader in setting global standards for AI governance, being the second country to implement a comprehensive legal framework. Importantly, South Korea's leadership is not confined to legal and institutional achievements. It is also underpinned by vigorous investment and innovation by globally competitive firms (Nam et al., 2024) and a user base that is both technologically literate and proactive in AI adoption (Im et al., 2011). With this convergence of institutional, industrial, and sociocultural dynamics, South Korea offers a valuable context for examining how policy frameworks and literacy strategies are formulated in a rapidly digitizing environment.

In envisioning future directions for AI policy in South Korea, a critical task is to transition from the predominantly regulation-focused, normative paradigm that has characterized much of the policy discourse to date. As illustrated by developments in the United States, the European Union, and South Korea, regulatory and institutional frameworks have been the focal points of policy formulation. While essential, this focus may inadvertently result in overregulation, potentially stifling innovation. Moreover, in specific sociocultural contexts, rigid regulatory approaches may fall short of achieving desired policy outcomes (Blind, 2012; Sohn et al., 2017). Thus, it is imperative to adopt a more adaptive and integrative approach to formulate AI policies that are both effective and context-sensitive.

In light of these considerations, this study aims to explore the current status and challenges of South Korea's AI policy landscape. Rather than seeking direct international scalability or presenting a universal model, analyzing the perspectives of media experts in South Korea provides context-specific insights into policy transferability, contributing nuanced empirical perspectives to broader global debates on AI governance.

• RQ2: What are the key policy challenges in mitigating AI-related risks, as perceived by media experts in South Korea?

Transitioning from digital to AI literacy

Across the globe, including South Korea, the core themes of AI policy development center on user protection and regulatory oversight. However, a closer examination of current policy frameworks reveals a predominant focus on regulating business practices, primarily in response to the perceived technological instability and potential risks associated with AI systems (Justo-Hanani, 2022; Roberts et al., 2021). As Beck (1992) notes, with technologies, unexpected risks and side effects often arise not from the technology itself, but from how it is used by individuals. In light of this causal dynamic, users must be recognized as essential stakeholders in AI policy discourse; their exclusion is neither appropriate nor tenable.

Among the emerging approaches to managing the risks posed by new technologies, the concept of literacy has garnered growing scholarly attention. Yet literacy is not a novel construct of the digital era. Instead, it has continually evolved alongside societal and technological transformations (Leu et al., 2004). Notably, the rapid advancement of digital technologies, the proliferation of digital environments, and the associated increase in risk have collectively contributed to the ongoing expansion of the literacy concept (Leu et al., 2017).

Traditionally, literacy referred primarily to basic reading and writing skills. However, the advancement of information and communication technologies (ICT) has transformed the concept into a multifaceted and layered construct (Carmi et al., 2020). Closely linked to technological development, the notion of technological literacy has emerged, denoting the comprehensive set of competencies required to effectively engage with digital tools (Deuze and Beckett, 2022). This perspective extends beyond technical proficiency to encompass the critical and ethical use of technology, grounded in both efficiency and accountability.

Technological literacy is thus understood as an expanded competency that transcends traditional decoding skills, incorporating abilities to search for, analyze, evaluate, and produce information (Baro et al., 2019). In response to the demands of the digital era, technological literacy has been further classified into specific subcategories, including digital literacy, media literacy, and data literacy. Digital literacy refers to the fundamental ability to use digital devices and platforms effectively, enabling active participation in the information society (Eshet, 2004). Media literacy involves the capacity to critically assess the credibility and bias of content disseminated through both traditional and digital media. Data literacy extends this framework to include the ability to analyze large-scale datasets and evaluate their implications from an ethical perspective (Kerpedzhiev et al., 2021; Someh et al., 2019).

In this context, the rapid development and diffusion of AI technologies have introduced the emerging notion of AI literacy. However, due to the complexity of AI systems and the diversity of their applications, clearly defining AI literacy and delineating its scope remains a substantial challenge. As AI technologies become more widespread, concerns about their potential risks have intensified, prompting a growing body of research aimed at conceptualizing the term. For instance, Pinski and Benlian (2024) define AI literacy as a “sociotechnical competence” that entails understanding human–AI interaction, recognizing the input-processing-output mechanisms of AI systems, and incorporating user experience with the technology. This view advances beyond seeing AI as a neutral tool, emphasizing the need for users to understand its operational principles to engage more actively and responsibly with the technology

Chiu et al. (2024). broaden the concept of AI literacy by highlighting the importance of critical thinking, the ability to collaborate effectively with AI systems, and an understanding of the social and ethical implications associated with AI technologies. Similarly, Wang et al. (2023) identify four core components of AI literacy, which include understanding the functional and contextual aspects of AI, the ability to apply AI technologies purposefully in problem-solving, critical analysis and evaluation of AI-generated outputs, and attitudinal elements, such as personal responsibility and ethical reflection. Taken together, these perspectives reflect a shift toward viewing AI literacy as a sociotechnical competence grounded in human–AI collaboration. The concept now emphasizes not only technical comprehension but also the ability to recognize, interpret, and respond to the wider social, economic, and ethical consequences of AI. As AI technologies become increasingly embedded in decision-making processes across various sectors, AI literacy is evolving into a multidimensional skill set that encompasses technical understanding, collaborative capability, ethical awareness, and reflective judgment.

Taken together, these studies demonstrate that AI literacy has developed into a multidimensional concept extending well beyond technical proficiency. However, these dimensions have largely been conceptualized as individual competencies for understanding and engaging with AI. As AI-related risks become an increasingly pressing policy concern, greater attention is needed to how AI literacy can be connected to broader governance arrangements involving corporate responsibility, legal and institutional safeguards, social inclusion, and stakeholder coordination. In this sense, AI literacy raises a broader policy question concerning the core values that should guide its development and implementation. While recent literature has increasingly recognized the policy relevance of AI literacy, empirical research examining how individual-level competencies can be systematically incorporated into national AI governance frameworks remains limited. Addressing this gap, the present study draws on the perspectives of South Korean media experts to identify core values considered important for AI literacy policy and to examine how these values can contribute to responsible and inclusive AI governance. Accordingly, the following research question is proposed.

• RQ3: What core values do media experts in South Korea identify in relation to the development of AI literacy policies?

Research method

This study adopted an exploratory qualitative research design to investigate expert perceptions of the societal implications of AI diffusion and to identify possible policy directions. Given the rapidly evolving nature of AI and the absence of stable empirical patterns, a qualitative approach was considered appropriate for examining emerging issues and their contextual meanings. Rather than pursuing statistical generalization, the study sought to obtain expert insights that could inform policy discussions and provide a basis for subsequent empirical research.

The evidence generated in this study should therefore be understood as expert-informed rather than participatory. The interviews capture professional assessments of AI-related risks and policy priorities; however, they do not directly represent the lived experiences, values, or preferences of general citizens, AI end-users, or communities affected by these technologies. Expert perspectives are used here to identify and structure emerging policy concerns and to provide an exploratory basis for future research involving a broader range of social actors.

To select participants with high levels of expertise, a purposive sampling strategy was employed, guided by three principal conditions to ensure the depth of expertise and professional diversity within the sample. First, participants were required to demonstrate substantial knowledge and experience related to AI and intelligent information technologies. This was assessed on the basis of theoretical or applied knowledge of AI, affiliation with organizations engaged in AI-related activities, practical experience with AI-related tasks, and participation in relevant academic or professional activities. Second, participants were selected from positions involving leadership or decision-making responsibilities so that their responses could reflect both professional expertise and organizational authority. Third, institutional diversity was sought by inviting participants from academia, industry, and government-affiliated research institutes to capture varied perspectives across professional settings.

Candidate selection and recruitment were facilitated through professional networks and recommendations from members of the Korea Media Management Association. A total of 14 eligible experts matching the selection criteria were initially contacted via email, of whom 11 agreed to participate (a response rate of 78.6%), while three declined or were unable to participate due to scheduling constraints. Although six of the eleven participants were drawn from academia, reflecting the prominent role of academic scholars in national AI policy governance and media research in South Korea, the final panel also included seasoned experts from major ICT corporations, broadcasting institutions, and national policy research centers. Given the specialized nature of the study topic and the depth of data collected, a sample size of eleven was deemed sufficient to achieve informational adequacy and thematic saturation, as no substantially new core themes emerged during the final stages of data coding. Nevertheless, sampling through professional association networks introduces a potential degree of sectoral concentration and professional homogeneity, a characteristic inherent to targeted expert panels that should be considered when interpreting the findings.

As this research was conducted with adult professionals in a non-medical and non-interventional social science setting, participant autonomy and confidentiality were carefully considered throughout the study. Potential participants were approached individually by email and received an explanation of the study's purpose and how their responses would be used for academic research before making a decision about participation. Participation was entirely voluntary, and written responses were collected only from individuals who chose to participate. The research involved no clinical procedures or experimental interventions and did not include vulnerable populations or the collection of sensitive health-related information. To safeguard participants’ privacy, information that could reveal individual identities was omitted from the manuscript. Interview responses and illustrative quotations were also presented without identifying participants. For the same reason, Table 1 provides only broad information about participants’ professional backgrounds.

Table 1. Characteristics of expert interview participants.

The interview questionnaire consisted of two sections (see Table 2). The first addressed perceptions of AI technologies and service environments and assessments of existing policy measures. Seven questions were used to examine the positive and negative impacts of AI, perceived risks and side effects, evaluations of existing policies, and possible policy responses. The second section contained five questions concerning the development of AI literacy policy, including policy priorities, key actors involved in implementation, and social and academic challenges associated with AI literacy.

Table 2. Structure of the expert interview questionnaire.

The interviews were conducted in written form using a structured questionnaire, with responses collected via email in March 2025. The resulting textual data were analyzed using qualitative content analysis. This approach was used to systematically identify recurring patterns in the expert accounts while preserving the contextual meaning of individual responses. Drawing on established procedures for qualitative thematic reduction (Galea et al., 2025), the analysis proceeded in three stages. First, the responses were read repeatedly, and statements relevant to each research question were identified and assigned initial codes. Recurring expressions, concerns, and policy-related ideas were coded according to their substantive meaning. Second, codes addressing similar issues were compared across responses and grouped into broader categories. This process focused on identifying recurring patterns as well as differences in how participants understood AI-related impacts and policy issues. Third, the categories were reviewed and consolidated into higher-level thematic domains corresponding to the three research questions: the societal impacts of AI, policy responses to AI-related risks, and priorities for AI literacy policy. These thematic domains provided the analytical structure for organizing and reporting the findings. The number of participants referring to each theme was used descriptively to indicate patterns within the interview data, rather than as a measure of prevalence or generalizability. Accordingly, thematic relevance was assessed in relation to both recurrence across responses and the substantive significance of participants’ accounts.

Several procedures were employed to strengthen the credibility and consistency of the analysis. Member checking allowed participants to assess whether the researchers’ preliminary interpretations adequately reflected their views. Investigator triangulation involved independent examination of the dataset by multiple researchers, followed by comparison and discussion of the interpretations. In addition, peer debriefing was conducted with a doctoral-level researcher who was not directly involved in the initial analysis and who reviewed the coding process, category development, and interpretive decisions. Throughout the analysis, particular attention was paid to preserving the meaning of participants’ accounts. Illustrative excerpts are therefore presented in the Results section to demonstrate how the reported categories were grounded in the interview material.

Results

Expert perspectives on the multidimensional impacts of AI

This study addresses Research Question 1 by examining how South Korean media experts perceive the multidimensional impacts of AI technologies and services across individual, societal, and industrial domains. All participating experts emphasized that AI is already exerting significant influence across various sectors of society.

The individual-level impacts of AI

South Korean media experts identified five major individual-level impacts of AI, including enhanced convenience, increased utility through personalized services, support for creative activities, improved access to information, and the potential for emotional interaction (see Table 3). Enhanced convenience was mentioned by all participants (A, B, C, D, E, F, G, H, I, J, K) as an individual-level benefit of AI. This was largely attributed to AI-driven recommendation systems, smart home technologies, and automation services that simplify decision-making and reduce time and effort in daily life. Participant E elaborated on this point.

Table 3. Perceived positive impacts of AI at the individual-level.

Generative AI is expected to enhance quality of life by providing personalized services in areas such as learning, entertainment, and everyday convenience. For instance, by learning a user's preferences and tastes through daily interactions via devices like AI speakers, it is likely to recommend customized content and assist with personal schedule and health management. (Participant E)

These insights suggest that AI advancements are not limited to functional improvements but may also contribute to improvements in quality of life. As AI technologies integrate into daily routines, they are reshaping user decision-making and behavior. Participants also identified personalized services (C, E, F, H, I, J, K) and improved access to information (B, F, H, I, J) as individual-level benefits of AI. These assessments are consistent with recent developments, such as AI-based digital textbooks in South Korea's education system (Lee and Bang, 2025) and the widespread use of platforms such as ChatGPT and Perplexity in academic research (Aithal and Aithal, 2023).

Despite these advantages, experts also acknowledged significant risks. Consistent with previous studies (Berliner, 2024; Cheng et al., 2022), they emphasized that AI's development entails inherent limitations and unintended consequences.

Experts identified several individual-level risks (see Table 4), including concerns about personal data protection and security (B, C, E, G, I, J), the reliability and quality of AI-generated information (B, F, H, I, K), the potential decline in cognitive and critical thinking abilities due to excessive reliance on AI (A, H, I), algorithmic bias and homogenized outputs (G, I, J), the deepening of the digital divide (D, I), and unclear accountability and the shifting of responsibility to users (I, J).

Table 4. Perceived negative impacts of AI at the individual-level.

Concerns about personal data protection and security were mentioned by six participants (B, C, E, G, I, J). These concerns reflect risks associated with how AI systems collect and process personal data, including potential privacy violations and information leaks (Guzmán-Velásquez and Lalinde-Pulido, 2022; Martin and Zimmermann, 2024). Participant E emphasized this point.

Given that AI technologies must continuously learn from users’ daily data, there is a high likelihood that risks related to personal information security and privacy infringement will increase. Although advancements are being made in security technologies, hacking methods are also evolving, making it difficult to completely eliminate the risk of data breaches. (Participant E)

These findings suggest that concerns about data security and privacy extend beyond technical or incidental issues and may reflect broader risks associated with the design and functioning of AI systems.

The societal and environmental-level impacts of AI

This section synthesizes expert perspectives on the societal impacts of AI technologies. Consistent with the individual-level analysis, both positive and negative implications were identified, and the discussion below focuses on the positive dimensions. Four recurring themes emerged across the interviews. Increased efficiency across society was mentioned by all participants (A, B, C, D, E, F, G, H, I, J, K), with participants noting that AI can enhance resource allocation and operational processes in areas such as public administration, industry, and education. Participants also identified the creation of new jobs and opportunities (E, F, H, I), the expansion of opportunities for creative thinking (E, G, I, J), and the enhancement of learning and application capabilities (C, I, K) as societal-level benefits of AI. The creation of new jobs and opportunities was associated with emerging roles in areas such as data analytics, algorithm auditing, and AI governance. Opportunities for creative thinking were linked to the potential for automation of routine tasks to allow greater engagement in higher-order and cognitively demanding activities, while enhanced learning and application capabilities were associated with adaptive and personalized forms of knowledge acquisition. A summary of these perceived societal-level benefits is presented in Table 5.

Table 5. Perceived positive impacts of AI at the societal level.

Increased societal efficiency, mentioned by all participants, was closely linked to benefits such as personalized services, improved access to information, and support for creative work. Participants described AI as transforming operational models and role structures across public, industrial, and educational sectors and as influencing broader areas such as the economy, culture, and labor. Participant J summarized this view.

AI is widely applied in socio-cultural fields such as media and content (e.g., YouTube), as well as economic domains including credit ratings, refugee classification, and diplomacy. It has also played a key role in healthcare policymaking, particularly during the COVID-19 pandemic. AI has become pervasive across private, public, and market sectors, illustrating its high utility and efficiency. As a result, public awareness of AI's benefits is spreading with minimal resistance. (Participant J)

Despite these benefits, experts also identified substantial societal risks. Increased unemployment due to AI-induced job displacement was mentioned by all participants (A, B, C, D, E, F, G, H, I, J, K) as a societal-level risk. Participants also identified the potential for AI to intensify social polarization and inequality (D, E, I, J), erode tasks traditionally viewed as uniquely human (A, G), and generate complex legal, ethical, and moral challenges (F, J). Although similar concerns have accompanied past technological transitions, the advanced automation and algorithmic integration associated with AI have increased attention to these issues (Masakowski, 2020). A summary of these perceived risks is provided in Table 6.

Table 6. Perceived negative impacts of AI at the societal level.

Participants described AI as having structural implications for labor markets, particularly in relation to job displacement and changes in employment patterns (Chiarini et al., 2024).

The spread of generative AI is 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. (Participant I)

Several participants (D, E, I, J) also identified concerns about the concentration of AI technologies and related economic benefits within a small number of large firms and the unequal distribution of resulting gains. Participants associated this concentration not only with potential job losses but also with broader structural inequality. Additionally, participants identified concerns about the erosion of human creativity (A, G) and the growing legal and ethical complexities surrounding originality, authorship, and copyright in AI-generated content (F, J).

The industrial-level impacts of AI

An analysis of expert perspectives on the industrial impacts of AI shows that AI adoption is expanding across various sectors, with participants identifying substantial changes in the media and entertainment industry. Experts described AI as reshaping existing structures and practices within this sector. Participants described AI as playing an increasingly important role across different stages of the content production process, including creation, editing, distribution, and consumption. According to participants, AI can improve operational efficiency, support the development of new business models, and generate new forms of value. Table 7 summarizes the positive industrial-level impacts identified by the experts.

Table 7. Perceived positive impacts of AI at the industrial level.

Increased efficiency in content production was mentioned by ten participants (A, B, C, D, E, F, G, H, I, K) as a positive industrial-level impact of AI. Participants associated this increased efficiency with AI-based video production and personalized content delivery, which they viewed as contributing to content quality, competitiveness, and accessibility, as well as changes in competition, creativity, and business models across the industry.

AI enables rapid production of more creative and diverse media content and expands personalized content delivery. This increases content diversity and accessibility, allowing creators who previously lacked technical capacity to realize their ideas. Ultimately, this enhances the overall user experience. (Participant E)

As Participant E's statement illustrates, AI is influencing competition, creativity, and the broader content ecosystem. However, participants also identified several challenges associated with these changes. Table 8 summarizes the negative industrial-level impacts identified by the participants.

Table 8. Perceived negative impacts of AI at the industrial level.

Job reduction and role replacement were identified by five participants (A, B, F, J, K) as industrial-level risks associated with AI. Other identified concerns included growing polarization between large and small industry actors (C, I, J), intellectual property and copyright disputes (E, I), the spread of misinformation (H, F), and the homogenization of content quality (G). One participant also raised concerns about the long-term sustainability of one-person media (I).

AI technology is also expanding into creative fields, such as music and writing, accelerating changes in creative practices. (Participant F)

Taken together, the findings for Research Question 1 demonstrate that participants perceived AI as having diverse impacts across South Korea's individual, societal, and industrial spheres. The analysis also identified a range of concerns regarding the potential negative consequences of AI across these domains.

Expert perspectives on policy responses for mitigating AI risks

In response to Research Question 2, which explored the social and policy measures necessary to mitigate the side effects of AI technology and service utilization, six policy responses were identified through a synthesis of expert opinions. AI literacy education was mentioned by all participating experts (A, B, C, D, E, F, G, H, I, J, K). Experts also identified other policy responses, including strengthening corporate accountability through self-regulation (A, B, C, D, E, G, H, I, J), improving government regulations and legal frameworks (B, C, D, E, F, H, I), establishing technological safeguards and safety systems (E, H, I), expanding public discourse and inclusive governance (B, F, H), and strengthening data and personal information protection (B, F). These expert-informed policy responses are presented in Table 9.

Table 9. Policy responses for mitigating AI risks.

These findings, consistent with Participant J's remarks, highlight that technological advancement does not automatically produce societal benefit. Experts emphasized that AI can intensify existing inequalities and introduce new forms of harm, underscoring the need to move beyond techno-deterministic views that frame AI primarily in terms of efficiency or economic gain.

As AI technologies and services continue to expand, their side effects are expected to intensify. By rapidly reproducing existing inequalities and injustices, AI may not only exacerbate current issues but also generate new ones. To prevent this, it is necessary to move beyond a techno-deterministic perspective that views AI solely in terms of economic and industrial benefits. (Participant J)

Against this backdrop, AI literacy was identified by all eleven experts as a policy response to AI-related risks. They argued that expanding public understanding of data practices, misinformation, and human–AI interaction is critical for risk mitigation.

The changes driven by generative AI remain difficult to predict, and prematurely enforcing restrictive measures may hinder its positive potential. Policy efforts should focus on enabling people to benefit from generative AI safely and meaningfully. (Participant A)

It is important to raise public awareness around personal data and prevent indiscriminate privacy violations or data misuse. There is also a growing need for ethics and literacy education to help users identify misinformation and respond to various forms of digital crime. (Participant B)

Participants also identified strengthening corporate responsibility through self-regulation as a policy response. 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.

Companies should anticipate potential harms from technological development and take proactive steps to minimize them. Ignoring societal impacts will likely lead to stricter government intervention, which may in turn hinder innovation. A self-regulatory environment that promotes responsible innovation is essential. (Participant C)

Participants also raised concerns about the limitations of self-regulation alone.

Current laws and regulations are inadequate given the rapid pace of AI development. To address misinformation, copyright violations, and privacy concerns, legal frameworks must be strengthened and updated. This includes clarifying accountability, regulating AI-generated content, and enforcing penalties for digital crimes. (Participant E)

Such views reflect a practical stance that emphasizes not only education but also institutional reform and social protection. Regarding the regulatory design process, social consensus was also emphasized, particularly the importance of community-based consultation structures.

A key policy direction is to establish clear national-level rules that reflect public consensus. For example, copyright protections should be extended to training data, and unfair use must be prohibited. If legal or technical challenges prevent fair compensation, mechanisms should be introduced to mediate agreement among stakeholders. (Participant D)

Taken together, these perspectives suggest the potential value of an inclusive, multi-stakeholder governance framework involving government, industry, and civil society in addressing data sovereignty, algorithmic accountability, and the coordination of diverse policy responses.

Expert perceptions on foundational values for AI literacy development

In response to Research Question 3, the analysis of expert perceptions identified four core values related to the development of AI literacy policy. Ethics and Responsibility Education (E, F, J), Practicality and Real-World Applicability (C, E, F), and Social Inclusion and Accessibility (E, F, G) were each mentioned by three participants, while Autonomy-Oriented Learning Approaches were mentioned by two participants (D, H). These patterns of participant mentions are presented descriptively and highlight distinct dimensions of AI literacy policy rather than establishing a hierarchy of policy priorities (see Table 10).

Table 10. Core values for AI literacy policy development.

The theme of Ethics and Responsibility Education reflects concerns among participating experts that AI literacy should include the capacity to recognize and manage risks such as misinformation, intellectual property conflicts, and potential societal harms.

As AI increasingly influences many aspects of our lives, it is essential for individuals living in the digital era to acquire appropriate knowledge, skills, attitudes, and values. Beyond understanding technological systems and AI-driven changes, it is especially important to critically examine their implications for human life and society. Core competencies should include knowledge of AI concepts, digital and data literacy, online safety, basic AI programming, AI ethics, effective collaboration with AI, and the development of responsible attitudes toward AI. (Participant J)

The theme of Practicality and Real-World Applicability reflects participants’ emphasis on connecting AI education with skills that users can apply in daily life and work. This aligns with Participant C's observation that the current gap is no longer access to devices, but rather “users’ ability to comprehend, manage, and communicate around digital content.”

Educational content should not only provide theoretical knowledge but also emphasize practical skills that enable individuals to effectively understand and apply AI in everyday life and work settings. These practical skills should be integrated with a solid theoretical foundation. (Participant E)

The theme of Social Inclusion and Accessibility reflects concerns about technological polarization and unequal access to AI-related opportunities. Participants emphasized the importance of equitable access to AI education across different age, income, and social backgrounds.

It is essential to ensure that people of all ages and socioeconomic backgrounds have equitable access to AI education and resources. This is crucial for narrowing the digital divide and enabling everyone to benefit from advancements in AI technology. (Participant E)

The direction of AI literacy policy should focus on enabling all citizens to understand and effectively use AI. From an educational standpoint, this requires moving beyond discipline-specific approaches. Instead, education should be designed through an integrated framework that encompasses fields such as computer science, sociology, and law, as the impact of AI is not confined to any single domain. (Participant F)

Autonomy-Oriented Learning Approaches were also identified as a core value (D, H). Participants associated this dimension with user agency, critical thinking, and the capacity for independent evaluation of AI systems. Participant D noted that purely regulation-centered models may be limited because “coercive or top-down approaches are unlikely to be effective,” highlighting the role of user autonomy in AI literacy.

Collectively, these findings suggest that AI literacy policy can be understood as a multidimensional construct encompassing moral responsibility, practical skill acquisition, equitable access, and user autonomy.

By synthesizing the empirical insights across the three research questions, ranging from perceived benefits and risks to multi-layered policy responses and core literacy values, the analysis extends the analytical focus beyond isolated user skills to broader considerations of structural policy design. These integrated perspectives provide the empirical basis from which the broader policy implications presented in the following section are developed through the authors’ interpretation.

Conclusion

This study adopted an exploratory qualitative approach and conducted written structured expert interviews with eleven media experts from academia, industry, and national research institutes in South Korea. The study aimed to examine how the accelerating diffusion of AI is reshaping society and to identify possible directions for responsible AI governance.

The research findings can be summarized as follows. First, the findings of RQ1, consistent with prior research (Coeckelbergh, 2025), suggest that participants perceive AI as having wide-ranging impacts across individual, societal, and industrial domains. These impacts involve both benefits and risks. Specifically, media experts in South Korea acknowledged the advantages of AI in enhancing information accessibility, improving work efficiency, and fostering creativity. At the same time, they expressed concerns regarding personal data breaches, rising unemployment driven by automation, and industrial restructuring, particularly within the media sector. To navigate the emerging AI era, however, it is necessary to move beyond a dichotomous perspective of “benefits versus risks.” Instead, the impacts of AI should be understood as a continuum in which values and risks are intricately interconnected. This interpretation underscores the need for an integrative analytical framework and alternative approaches that address both the promises and the risks of AI in a holistic manner.

Building on the insights obtained from RQ1, RQ2 explored policy responses that balance the values and risks of AI. AI literacy education was identified by all participants as a policy response for mitigating AI-related risks while enhancing the value of AI in society. In addition, participants identified the reinforcement of corporate responsibility through self-regulation as a complementary strategy. Participants’ responses also indicated that self-regulation and institutional regulation may play complementary roles in addressing AI-related risks. Ultimately, these discussions point to the limitations of long-standing value-driven dichotomies in regulatory debates within the media sector (Wilczek et al., 2025) and underscore the need for inclusive and collaborative policy alternatives. Taken together, these findings suggest that effective AI governance requires moving beyond the traditional binary of innovation versus control. Instead, a multilayered governance model is needed, one that simultaneously cultivates user competence, encourages responsible industry behavior, and situates regulatory authority within a broader ecosystem of shared responsibility. Such an approach reflects a shift toward co-regulation, in which government, industry, and civil society jointly contribute to the development of norms and safeguards for technological advancement and social protection.

Third, RQ3 examined expert perceptions of the foundational values that should guide AI literacy policy. The analysis identified four core values: ethics and responsibility, practical and real-world applicability, social inclusion and accessibility, and autonomy-oriented learning. These dimensions suggest that AI literacy should be approached not merely as a technical skill set but as a multidimensional public capability. In particular, the participants emphasized the importance of cultivating ethical judgment and responsibility, providing learning opportunities grounded in practical contexts, ensuring inclusive access for digitally marginalized populations, and fostering users’ capacity to critically and independently evaluate AI systems. These findings suggest that future AI literacy policy should integrate value-based, practice-oriented, inclusion-focused, and autonomy-supportive approaches within a coherent policy framework.

In sum, the theoretical value of these findings lies in shifting the analytical role of AI literacy from a set of individual attributes to a policy-oriented framework. Rather than focusing solely on what competencies individuals should acquire, the analysis suggests that dimensions including ethics and responsibility, practical applicability, social inclusion, and user autonomy can inform the design and implementation of AI literacy policy. Furthermore, by situating AI literacy within the broader framework of responsible AI governance, this study connects competence-oriented approaches to AI literacy with institutional approaches to governance. AI literacy policy can therefore be conceptualized not as an isolated educational effort, but as one component of a multilayered system of shared responsibility among state, industry, and civil actors. Importantly, strengthening individual AI literacy should not be understood as a substitute for institutional responsibility. While literacy can enhance users’ capacity to understand, critically evaluate, and engage with AI systems, addressing structural risks also requires complementary action by governments, industry actors, and other institutions responsible for AI development and governance. Accordingly, the proposed framework should be understood as an expert-informed and interpretive framework rather than an empirically validated governance model. It is derived from the perspectives of the eleven experts included in this study and has not yet been validated with citizens, affected communities, policymakers, or other stakeholder groups. The framework therefore provides a conceptual and policy-oriented basis for future empirical validation and participatory refinement across broader and more diverse stakeholder contexts.

Building on these empirical findings, the following policy recommendations represent an interpretive synthesis by the authors rather than additional themes directly elicited from the interviews. Specifically, the recommendations translate the AI-related concerns, policy directions, and foundational values identified across RQ1–RQ3 into broader governance implications. This distinction clarifies the relationship between the participant-derived findings and the authors’ policy-level interpretation of those findings. Five policy recommendations are proposed on this basis.

First, given participants’ concerns regarding legal, ethical, privacy, and accountability risks, one policy implication is the need to strengthen ethical and institutional safeguards surrounding AI development and use. These principles should be embedded within a strengthened legal and institutional framework that clearly delineates accountability, ensures algorithmic transparency, and provides mechanisms for redress in cases of harm. Such institutional safeguards are important not only for protecting citizens but also for reinforcing public trust in emerging AI systems.

Second, participants’ emphasis on corporate responsibility and self-regulation suggests a policy implication concerning organizational risk management. This could include implementing ethical review procedures, conducting periodic algorithmic impact assessments, and adhering to responsible data governance standards. Through such measures, companies can contribute to a sustainable innovation ecosystem in which technological advancement is aligned with social responsibility. Strengthening corporate accountability is particularly relevant in contexts where rapid AI adoption may intensify industrial disparities and concentrate benefits among a limited set of market actors.

Third, the findings on ethics and responsibility, practical applicability, social inclusion, and user autonomy provide an empirical basis for a multidimensional approach to AI literacy policy. From a policy perspective, this could involve developing multidisciplinary curricula that encompass technical understanding, critical reasoning, ethics, and risk awareness, enabling learners to meaningfully interpret and evaluate AI-generated outputs. Inclusive education strategies should also be considered to reduce the risk that digitally vulnerable populations, such as older adults, low-income groups, and individuals with disabilities, are marginalized in an increasingly AI-driven society. At the same time, literacy initiatives should support user autonomy by strengthening individuals’ capacity to critically assess AI systems rather than relying solely on prescriptive or top-down forms of instruction.

Fourth, participants’ calls for public discourse and inclusive governance provide the basis for a broader policy implication concerning sustained coordination among government, industry, academia, and civil society. From a policy perspective, this could involve institutional arrangements that facilitate continued stakeholder consultation, feedback, and adaptation as AI technologies evolve. Such structures may contribute to greater policy coherence, reduce fragmentation, and support the long-term sustainability of AI governance.

Finally, the findings also point to the importance of continuous monitoring and evaluation as AI technologies and associated risks evolve. From a policy perspective, this could involve institutional mechanisms for systematically assessing emerging technological, social, and ethical risks and feeding such evidence back into policy development.

Although this study focuses on the South Korean context, its findings may offer conceptual and policy relevance for broader international discussions on responsible AI governance. South Korea provides a distinct case for examining how rapid AI adoption intersects with questions of literacy, institutional responsibility, and social inclusion. Rather than serving as an internationally scalable model or offering directly generalizable policy prescriptions, the findings provide a context-sensitive reference point that can inform comparative research and policy dialogue in other jurisdictions confronting similar challenges associated with AI diffusion.

These findings, however, should be interpreted within the specific methodological boundaries of the study. Based on the professional judgments of eleven media experts, the analysis does not provide direct empirical evidence regarding how general citizens or specific social groups experience AI-related risks or evaluate proposed policy responses. This distinction is particularly relevant given the emphasis placed here on social inclusion and collaborative governance. While expert knowledge can help identify critical policy concerns and structural directions, it cannot substitute for the lived experiences of communities directly affected by AI systems. Future research should therefore empirically examine the proposed framework across broader stakeholder groups and actively engage citizens, digitally marginalized populations, affected communities, policymakers, and other relevant stakeholders in its participatory refinement. Such research could assess the applicability of the framework across diverse social contexts and provide a more robust empirical basis for its further development.

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Footnotes

Edited by: Kevin El Haddad, University of Mons, Belgium

Reviewed by: Rengganis Siwi Amumpuni, University of PGRI Madiun, Indonesia

Gabriele Biagini, University of Florence, Italy