At the third roundtable of the APEC Research Center's Joint Conference Youth Scholar Forum, titled “Broken Walls: Reshaping Education and Generational Conflict in the AI Era,” scholars from Australia, Chile, China, Papua New Guinea, Peru, and Hong Kong engaged in an interdisciplinary discussion about how artificial intelligence is changing educational goals, learning methods, institutional design, and human cognitive structures.

Unlike the previous two sessions, which focused more on industry and regional cooperation, this forum pushed the issues directly toward the core of education: how will the boundaries of traditional education be redefined when AI is no longer just an auxiliary tool but gradually becomes part of the learning process, knowledge production, and even cognitive ways? Moderator, Associate Professor Li Chen from the Chinese University of Hong Kong, pointed out at the beginning that from printing, television, and the internet to artificial intelligence, technology and education have always been in a co-evolutionary relationship, and the current wave of AI revolution is forcing the education sector to rethink the fundamental methods of learning, research, and teaching.

Universities Are More Than Just Teaching Places

Caitlin Pienaar, Senior Strategic Advisor at the APEC Research Center of Royal Melbourne Institute of Technology, approached the topic from the perspective of higher education governance. She believes that universities have never been neutral knowledge transfer institutions but rather co-shapers of a society's and the economy's future: they connect the labor market, regional movement, research and innovation, public discourse, and social infrastructure, thus taking on responsibilities far beyond “classroom teaching” in the AI transition.

In her view, the impact of AI on employment and skill systems will first manifest in knowledge-based jobs and information-intensive industries, with young people particularly vulnerable in entry-level positions and early career stages, as many foundational tasks originally used for experience accumulation are being absorbed by AI systems. The risks arising from this include not only changes in job roles but also interruptions in career paths, the entrenchment of educational inequalities, and a weakening of long-term skill formation capabilities.

She therefore proposed that universities must undergo a threefold transformation: shift from a linear “education-employment” path to a lifelong learning support system, transition from traditional disciplinary divisions to interdisciplinary collaboration, and move from passively responding to AI to actively participating in AI capability building and governance of responsibilities. This means that universities in the AI era must answer a larger question: what public role should they play in an economy and labor market deeply mediated by AI?

Educational Goals Are Being Reexamined

Associate Professor Julio Erasmo Godoy-del-Campo from the University of Concepción in Chile advanced the discussion to the level of educational philosophy. He made a clear contrast between traditional education and AI-driven education: the former emphasizes standardization, unified progress, and examination certification, while the latter emphasizes personalization, flexibility, and real-time feedback based on data.

However, in his view, this distinction is not merely about “whether technology is used” but about re-examining the fundamental question of “what kind of people education should cultivate.” AI can lead to higher learning engagement, more frequent feedback mechanisms, and friendlier support systems for disabled students, but it also raises a series of new issues such as academic integrity, platform disparities, data privacy, and the weakening of educational communities.

Therefore, he does not advocate for a simple binary choice between “traditional education” and “AI education," but emphasizes the need to seek a path that balances the advantages of both, allowing AI to truly serve the enhancement of educational quality while minimizing its negative externalities. On this point, many guests at the forum resonated: educational transformation is primarily a matter of value choice and secondly a matter of technology deployment.

From “Giving Maps” to “Teaching Navigation”

Associate Professor Zhu Xi from the School of Artificial Intelligence at the Chinese University of Hong Kong (Shenzhen) delivered one of the most thought-provoking comments of the session. He proposed the idea of “not just handing over maps to the next generation,” stating that the arrival of AI is fundamentally overturning the basic logic of “the elder transmitting knowledge to the younger” in education because today’s children, with the help of AI, can often obtain faster, more patient, and even more accurate knowledge support than adults.

He introduced the concept of “AI native generation,” arguing that this is not a demographic divided by age but a new cognitive state: for this generation, AI is not an external tool but part of the cognitive system. In this context, traditional education is at least failing in three ways: the knowledge transfer function is weakened, experience replication is no longer reliable, and the authority of the previous generation is diminishing.

In response to this change, he proposed the “minimum constraint principle”—educators should not try to fill the next generation with content but should provide necessary boundaries, allowing them to self-explore in a more open space. He believes that the truly essential lines to uphold are three: ethical foundations, the capability for autonomous AI navigation, and a community responsibility towards the APEC regional society.

In line with this view, Yuan RANDong, Deputy Director of the Qianhai Research Institute for International Affairs at the Chinese University of Hong Kong (Shenzhen), suggested that young people in the AI era should not only be passive recipients of knowledge but should enter the position of “co-creators” earlier. He emphasized that the significance of AI lies not in replacing human creativity but in lowering the barriers for youth to engage in real problem-solving, design, experimentation, and collaboration, enabling them to join the knowledge co-creation process among people sooner.

He further pointed out that the core of education should shift from “knowledge accumulation” to “ability formation,” especially the ability to pose questions, make judgments, connect across disciplines, and collaborate with others. If curricula, assessments, and diploma systems remain trapped in old linear frameworks, then the systems will increasingly excel at measuring “yesterday's intelligence” while becoming less capable of responding to today’s realities.

Local Differences Determine the True Difficulty of AI Education

Julian Melpa from the National Research Institute of Papua New Guinea reminded attendees that AI education is not a topic that can be advanced independently of infrastructure and cultural context. Using Papua New Guinea as an example, she pointed out that local traditional education heavily relies on community, teacher-centered classrooms, and face-to-face teaching; when AI education enters reality, it will immediately encounter structural limitations such as electricity, internet access, qualified teachers, costs, and language diversity.

Especially in a society with over 800 local languages and significant geographical complexities between urban and rural areas, the “efficiency dividends” brought by AI will not be automatically distributed equally. If there is a lack of infrastructure, offline learning platforms, local language support, and systematic training for teachers, AI may exacerbate educational inequalities and conflict with local cultural logic.

She thus argued that AI education cannot replace existing educational systems but should be cautiously embedded within local social conditions. This also made the topic of “inclusivity” at the forum more concrete: truly sustainable educational digitization does not start with technology but precedes with contextual adaptation.

Public Sector, Legal Governance, and Cognitive Change

Rommel Abilio Infante Asto, Legal Advisor of the AI Laboratory of the National Jury of Elections in Peru, presented another educational scenario: AI does not only happen in classrooms but also in public services, legal knowledge dissemination, and vocational learning processes. He introduced that Peru is one of the first economies in the Asia-Pacific region to establish an AI legal framework, but in higher education, adult education, and skills enhancement, existing norms still have significant gaps.

He used several tools developed by the AI laboratory of the National Jury of Elections as examples to explain how AI is used for civic election education and legal research learning for civil servants; meanwhile, he also pointed out that Peruvian universities and academic publishing institutions have inconsistent attitudes towards AI, with some encouraging critical use and others maintaining clear caution. This differential signifies that, as AI enters the education and public knowledge systems, different institutions are still exploring their own boundaries and norms.

At the end of the forum, Li Hui, Associate Vice President of Hong Kong Educational University, further advanced the discussion to the level of “Is AI reshaping the brain?” He combined his research to propose that the proliferation of AI and digital devices is changing people's attention networks, ways of remembering, executive functions, and motivational mechanisms, especially potentially having a long-term impact on the neural plasticity of children and adolescents.

In his view, AI is not just a tool to enhance efficiency but a form of “cognitive technology” that actively intervenes in cognitive structures. This also means that educational issues are no longer limited to curriculum reform but extend to family digital usage rules, children's exposure times, social media restrictions, and broader social issues like the potential emergence of “AI co-parenting.”

A Deep Discussion on the Future of Education

Looking back at the forum, what stands out is not whether the guests hold optimistic or cautious attitudes towards AI education, but that they all remind us of one fact: the impact of AI on education is not just a replacement at the tool level but a reconfiguration of goals, relationships, institutions, and humanity itself. From the role of universities, ability cultivation, institutional adaptation, to cultural differences, legal governance, and brain science impacts, educational issues are being reopened in the AI era, intertwining more closely with the labor market, public sector, and social structures.

This also means that educational reform in the AI era should not merely stay in technical discussions about “how to use AI in classrooms.” The deeper issue is: what kind of people should education cultivate, and how can individuals maintain judgment, creativity, a sense of responsibility, and community awareness in a world where intelligent systems are deeply involved? This roundtable forum did not provide a unified answer, but it clearly indicated that what is truly important may be the continuous questioning of these very issues.

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