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AI Is Not a Matter of “Adoption”: The Question Korean Universities Are Missing

As generative AI rapidly flows into university classrooms, the Korean higher education landscape has entered a period of transition. While the use of AI has become a reality in almost all areas of educational activity—including lecture material preparation, assignment execution, evaluation methods, and research assistance—discussions on how to embrace this change remain fragmented.

Some universities fully permit AI use, others specify restrictive clauses in their syllabi, and some emphasize student responsibility through ethical guidelines. However, these responses are limited in that they commonly view AI only as a “new tool” or an “object of management.” In Korean academic circles, AI discussions mainly converge on two questions: “How far should we allow it?” and “How can we prevent cheating?” While important, these questions fail to address the reality that AI is restructuring the entire educational system. AI is no longer a supplementary aid for specific classes or majors; it has become an environmental factor that transforms the learning process itself. Nevertheless, AI education discussions in Korea remain scattered among individual professors’ discretion, individual universities’ guidelines, and individual students’ ethical issues.

This decentralized response is tied to the structural characteristics of Korean higher education. Universities have long held autonomy as a core value, and state policies have intervened primarily through financial support and performance management. Consequently, national-level design regarding educational content and methods has been relatively weak. The situation has not changed much since the emergence of AI. The government emphasizes nurturing AI talent, digital transformation, and strengthening high-tech industrial competitiveness, but it has failed to present a consistent direction on what kind of civic competency AI education should represent in university settings.

The problem is that AI education is not simply a matter of “technical proficiency.” The ability to utilize AI is directly linked to learning efficiency, which in turn leads to gaps in learning outcomes. In other words, the difference between students who use AI well and those who do not is likely to expand beyond achievement gaps into gaps in educational opportunity. At this point, AI education moves from the realm of individual choice to the realm of public policy.

This is why major countries have begun to treat AI education as a national strategy. The UK, the US, and the EU are discussing AI guidelines and educational strategies in different ways. Particularly in Asia, Singapore and India are integrating AI education into their national systems under vastly different conditions. While these two nations differ starkly in scale and system, both are noteworthy for perceiving AI not as a “technology left to universities” but as an “educational infrastructure that society as a whole must design together.” This article starts with these questions: Where does Korea’s AI education stand now? Are we focusing only on rapid adoption? And is the direction and structure designed as thoroughly as the speed of adoption? The cases of Singapore and India provide benchmarks for answering these questions.

Singapore: Treating AI as a “National Competency” Rather Than a “Technology”

Singapore’s AI education policy is not a collection of individual systems or projects but starts from a clear national vision. This vision, known as “Smart Nation 2.0,” centers on three goals: growth, community, and trust, with AI serving as a key means to realize them. The Singaporean government aims not only to boost industrial competitiveness through AI but also to strengthen the resilience and adaptability of society as a whole.

This approach is clearly reflected in its education policy. In Singapore, AI education is not limited to specific majors or elite talent development programs. While the government aims to nurture 15,000 AI practitioners by 2027, it connects this goal to the entire university education and lifelong learning system. Universities, research institutes, industries, and government departments are bundled into a single ecosystem, with education functioning as the core axis that operates this ecosystem.

Representative examples include the “AI Singapore” and “SkillsFuture” programs. AI Singapore, as a national-level research and education hub, connects university researchers with industrial sites and emphasizes real-world problem-solving using AI. SkillsFuture is a re-education and competency transition program for workers and adult learners, focusing on using AI to design individual learning paths and diagnose competencies. These two programs share a common direction: they do not limit AI education to “student-targeted education” but place it within a lifelong learning framework.

Notably, Singapore treats AI literacy as a matter of civic competency. AI utilization is defined not just as a job skill but as the ability to judge and participate in a changing social environment. Accordingly, in university education, AI appears as a learning element combined with problem-solving, decision-making, and collaboration skills, rather than just a technical major skill. Some policy documents link this to “personal resilience.”

Of course, this model is not perfect. Critics argue that Singapore’s higher education system is still centered on elite universities and that migrant workers or non-formal learners are relatively excluded from lifelong learning programs. There are also concerns that AI education is designed around technical skills and fails to sufficiently incorporate ethical reflection or critical thinking. Nevertheless, Singapore’s case offers important implications by positioning AI education within national purposes and structures rather than leaving it to individual universities. The core lesson is clear: the success of AI education depends not on the level of technology, but on the social purpose under which it is placed. Universities function as implementation entities within this structure, while national policy sets the direction and scope. This shifts the discussion from “permission and control” to “design and responsibility.”

India: An AI Education Experiment Designed for Scale and Gaps

While Singapore is a nation that designed AI education through centralized coordination and policy consistency, India is a case answering the same questions under completely different conditions. India must design its education policy under structural constraints such as vast territory, extreme regional disparities, dozens of official languages, and imbalances in educational accessibility. Under these conditions, India’s AI education policy focuses on “scalability” and “inclusivity” rather than “sophistication.”

India’s AI education strategy took off with the “National Education Policy 2020 (NEP 2020).” NEP 2020 is a large-scale reform plan to restructure the entire education system, presenting the transition of human competency as a key task to respond to the rapidly changing global economic environment. The policy aims to raise the higher education enrollment rate to 50% by 2035 while emphasizing learner-centered flexible degree structures, critical thinking, and creativity. AI is positioned as a key tool to facilitate these reforms.

The practical implementation of NEP 2020 is supported by the “National Digital Education Architecture (NDEAR).” NDEAR is not a single platform but a public digital infrastructure that connects educational content, learning history, assessment, and certification. Within this infrastructure, AI recommends content suited to the learner’s level and needs, enables continuous assessment, and helps teachers track individual learning processes. This is seen as an attempt to gradually replace the culture of one-time exam-oriented evaluation.

The “Four-Year Undergraduate Programme” in Kerala, southern India, demonstrates how these changes work in practice. The program uses AI-based tools to continuously evaluate student progress and measure understanding and reflection through various assignments. This goes beyond simply changing evaluation methods; it is an attempt to shift the purpose of education from the “ability to get the right answer” to the “ability to understand and apply.”

A particularly notable point in the Indian case is the approach to language and accessibility. NEP 2020 specifies regional languages as the primary means of education and assessment, and digital platforms are required to provide multilingual content. AI-based translation technology and adaptive interfaces are used to realize these goals. This is a policy choice to expand educational accessibility for learners who have been excluded from the English-centered higher education structure. AI is also actively used to provide customized learning environments for students with disabilities.

Admittedly, India’s AI education experiment has its limits. Unlike the broad policy vision, gaps in implementation capacity between regions remain large, and policy effects are limited in areas with insufficient digital infrastructure. There are also risks that the digitalization of education could lead to commercialization—where the public education sector relies excessively on private platforms. Nevertheless, India’s approach is significant as it starts from the problem of “expanding educational accessibility” rather than “strengthening the competitiveness of top-tier universities.” India’s case suggests that AI can function as a tool to alleviate educational gaps if the direction of policy design is clear, even under imperfect conditions.

Commonality: AI Education Is a Matter of “Governance,” Not “Technology”

The AI education policies of Singapore and India are starkly different in their starting points, implementation methods, and institutional contexts. One is a small, centralized state; the other is a continental-scale federal state. However, looking at the two cases side-by-side reveals a common perception: AI education is a matter of education governance, not technology adoption.

Neither country leaves AI education to the individual choice of universities or faculty. Instead, they set the purpose and direction of education at the national level, with universities functioning as implementation entities within that framework. This is not a way of denying university autonomy but an approach to creating a policy environment where autonomy can function. By setting minimum standards and public goals for AI education, they aim to prevent university disparities from leading to educational gaps.

Another commonality is that they do not subordinate AI education solely to short-term outcomes or industrial demand. Singapore defines AI as part of social resilience and civic competency, while India combines it with the goals of expanding accessibility and inclusivity. In this process, AI is positioned not as a “skill to use well” but as a “social tool that must be understood and utilized.” This expands the content of AI education from technical proficiency to the realms of judgment, ethics, and responsibility.

Specifically, both countries do not separate lifelong learning systems from higher education. Because AI is a rapidly evolving technology, competencies acquired at a specific point soon become obsolete. Accordingly, both Singapore and India design AI education as a process that is repeatedly updated throughout life. Universities function as the starting point and a waystation in this process, assuming a structure where learning continues after graduation.

This approach clarifies the subject of responsibility. Responsibility for how to teach AI no longer lies solely with individual students or professors. The state presents the direction of education and prepares the institutional foundation, while universities take on the roles of implementation and adjustment. Within this structure, AI education becomes a publicly designed educational infrastructure rather than an object of management. The experiences of Singapore and India ultimately converge on one message: the key task of AI education is not how quickly the technology is adopted, but under what educational purpose it is placed. This message poses an important question to Korean higher education, which is by no means lagging in the speed of AI adoption: Is Korea designing AI education as a single policy system, or is it still leaving it to individual experiments?

Korea’s Current Position: Fast but Uncoordinated AI Education

The speed of AI adoption in Korean higher education is not slow. Since the spread of generative AI, many universities have prepared guidelines, and some are attempting to reorganize class designs and evaluation methods based on AI use. Workshops and training programs centered on teaching and learning centers have increased rapidly, and students routinely use AI as an assignment and learning aid.

On the surface, it is hard to say that Korean universities are falling behind. However, a closer look reveals a lack of common direction or structure. Standards for AI use differ by university, and even within the same university, approaches vary greatly by college or individual class. Some actively encourage AI use, while others remain cautious, focusing on the possibility of cheating. As a result, instead of being provided with a consistent educational experience, students face completely different rules depending on the atmosphere of each class and the judgment of the professor.

This situation is linked to the inertia of Korean higher education policy, which has left the field to its own autonomy and judgment rather than institutionally designing AI education. The government has presented AI talent nurturing and digital transformation as key national tasks, but these have been discussed primarily from the perspective of nurturing professional personnel to meet industrial demand. There has been a relative lack of discussion on how to define AI as a public competency across university education and what minimum learning experience should be guaranteed for all students.

In particular, in discussions surrounding lifelong learning, adult learners, and the role of regional universities, AI education remains on the periphery. While some universities provide AI-related education in the form of online courses or micro-degrees, these often remain at the level of individual projects. AI-driven learning path design or customized education are still tried only on a limited basis, and it is hard to say they have been institutionalized into a learning system that continues after university graduation.

In this process, AI education risks functioning as another resource for competition. Students who can use AI effectively gain an advantage in learning efficiency and achievement, while those who cannot find themselves at a disadvantage. These differences are easily reduced to matters of individual effort or choice, but they are actually matters of educational environment and institutional design. Unless AI education is systematically designed, technical gaps are likely to solidify into learning gaps. Korea’s AI education reality can be summarized as “fast but uncoordinated.” While technology adoption and individual experiments are active, the governance to weave them into a single educational policy system has not yet been formed.

Four Questions for a Korean-Style AI Education Strategy

First, for whom is Korea’s AI education being designed? Current discussions focus mainly on top-tier universities and nurturing high-tech industrial personnel. However, higher education is not a system only for an elite group. We need to consider what AI means for students at regional universities, adult learners, and the majority of students regardless of their majors. The policy direction changes significantly depending on whether AI education is viewed as an optional additional skill or a basic competency that all students must experience.

Second, is the university an implementation entity or a policy object in AI education? So far, AI education in Korea has relied heavily on the autonomous judgment of universities. This has the advantage of promoting innovation, but it also acts as a factor expanding gaps between universities. If only autonomy is emphasized without national-level minimum standards and direction, AI education will struggle to secure institutional publicness. Singapore and India show that university autonomy and national design do not necessarily clash.

Third, is AI a tool to reduce educational gaps or a new screening device? AI has the potential to enable education tailored to the learner’s level and needs, but it operates on the premise of the competency to utilize it. If AI use is expanded without sufficient support and institutional mechanisms, educational gaps may actually intensify. AI education policy must be designed with this duality in mind.

Fourth, to what extent is common national design possible? It is unrealistic to force the same educational model on all universities. However, at a minimum, common standards are needed for the purpose of AI education, basic principles, and learner protection mechanisms. This is not regulation but provides a common foundation upon which universities can autonomously innovate. Leaving AI education only to the field without such design is akin to neglecting responsibility.

These four questions are matters of policy choice and, simultaneously, fundamental questions about what public role Korean higher education will perform in the AI era. Answers are hard to determine in the short term, but accelerating technology adoption while avoiding these questions could be the most dangerous choice.

Singapore and India started from different conditions and contexts but share a common view of AI education: that AI is not a tool brought in from outside education, but a public infrastructure that must be newly integrated into the education system. This perception shifts AI education discussions from matters of technical use to matters of policy design and governance. Korean higher education is sensitive to technological change and has the capacity to quickly embrace new tools. However, current AI education discussions have focused so much on speed and response that they have failed to sufficiently raise questions about purpose and structure. Discussions on why and for whom AI should be used have been pushed to the background.

AI education eventually boils down to the question of what kind of human beings universities aim to nurture. If the role of higher education is to cultivate citizens who can go beyond simply handling AI well to understanding its possibilities and limits and judging its impact on society, AI education can no longer remain an individual choice or a supplementary program. It becomes part of education that must be designed publicly. The coordinated national strategy of Singapore and the inclusive experiment of India do not force a single conclusion on Korea. Instead, they present benchmarks for choice. Depending on whether Korea treats AI education as a sub-sector of competitiveness policy or uses it as an opportunity to redefine the public nature of higher education, the future educational landscape will look entirely different. What is needed now is not faster adoption, but clearer design.