Broken Walls: Reshaping Education and Intergenerational Conflict in the AI Era

At the third roundtable forum of the Youth Scholars Forum at the APEC Research Center Joint Conference, titled “Broken Walls: Reshaping Education and Intergenerational Conflict in the AI Era,” scholars from Australia, Chile, China, Papua New Guinea, Peru, and Hong Kong, among other places, held a cross-disciplinary discussion on how artificial intelligence is changing educational objectives, learning approaches, institutional design, and people’s cognitive structures.

Unlike the first two sessions, which focused more on industry and regional cooperation, this forum pushed the question directly to the core of education: when AI is no longer just an assistive tool, and gradually becomes part of the learning process, knowledge production, and even cognitive ways of thinking, how will the boundaries of traditional education be redefined? At the opening, the moderator, Li Chen, Associate Professor at the Chinese University of Hong Kong, pointed out that from the printing press, television, and the internet to artificial intelligence, technology and education have always been in a co-performance relationship. With the current round of the AI revolution, the education sector is being forced to re-examine the basic methods of learning, research, and teaching.

Universities are more than just places of instruction

Caitlin Pienaar, Senior Strategic Advisor at the APEC Research Center of RMIT University in Australia, first approached the topic from the perspective of higher education governance. She believes universities have never been neutral institutions for transferring knowledge; they are co-creators of society’s and the economy’s future. By connecting labor markets, regional mobility, scientific research innovation, public discourse, and social infrastructure, they bear responsibilities far beyond “classroom teaching” in the process of AI transformation.

In her view, AI’s impact on employment and skills systems will first be felt in knowledge-based jobs and information-intensive industries. Young people are particularly vulnerable at entry-level positions and in the early stages of their careers, because many foundational tasks that were originally used to accumulate experience are being absorbed by AI systems. The risks brought about by this are not only job changes, but also interrupted career pathways, the entrenchment of educational inequality, and a weakening of people’s ability to form long-term skills.

Based on this, she proposed that universities must complete three transformations: shift from a linear “education-to-employment” path to a lifelong learning support system; move from traditional disciplinary silos to interdisciplinary collaboration; and transition from passive responses to AI to active participation in AI capability building and responsibility governance. This means that in the AI era, universities need to answer a bigger question: in an economy and labor market deeply mediated by AI, what public role should universities actually play?

Educational objectives are being questioned again

Julio Erasmo Godoy-del-Campo, Associate Professor at 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, uniform pacing, and examination certification, while the latter emphasizes personalization, flexibility, and real-time feedback based on data.

But in his view, this is not merely a difference of “whether technology is used”; it is a renewed inquiry into the fundamental proposition of “what kind of people education should cultivate.” AI can bring higher learning engagement, denser feedback mechanisms, and support systems that are more friendly toward students with disabilities. At the same time, 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 a simple either-or choice between “traditional education” and “AI education.” Instead, he stresses the need to find a path that balances the advantages of both—so that AI can genuinely serve the improvement of education quality while keeping its negative externalities as low as possible. On this point, multiple panelists at the forum reached a shared resonance: educational transformation is first a matter of value choices, and only second a matter of technological deployment.

From “giving maps” to “teaching navigation”

Zhu Xi, Associate Professor at the School of Artificial Intelligence at the Chinese University of Hong Kong (Shenzhen), delivered one of the most intellectually charged parts of the forum. He proposed, under the title “Don’t Only Give Maps to the Next Generation,” that the arrival of AI is overturning the basic logic in education of “elders unilaterally transmitting knowledge to juniors,” because today’s children, with the help of AI, can often obtain knowledge more quickly, more patiently, and even more accurately than adults.

Based on this, he put forward the concept of the “AI-native generation.” He believes this is not a group defined by age, but a new state of cognition: for this generation, AI is not an external tool but part of their cognitive system. Under these circumstances, traditional education has at least three failures: the weakening of the knowledge transmission function, the unreliability of experience replication, and the decline of the authority of the older generation’s positioning.

In response to these changes, he proposed the “principle of minimal constraints.” Educators should not try to fill the next generation with content; instead, they should provide the necessary boundaries so that they can explore themselves in a more open space. In his view, there are only three lines that truly must be upheld: an ethical foundation, the ability to steer AI autonomously, and a shared sense of community responsibility toward APEC-region society.

Echoing this view, Yuan Randong, Deputy Director of the Qianhai Institute for International Affairs at the Chinese University of Hong Kong (Shenzhen), further suggested that in the AI era, young people should no longer be just recipients of knowledge, but should move earlier into the role of “co-creators.” He emphasized that the significance of AI is not to replace human creativity; rather, it lies in lowering the barriers for young people to participate earlier in solving real problems, designing, experimenting, and collaborating—so they can join the people-centered process of knowledge co-creation at an earlier stage.

He further pointed out that the core of education should shift from “accumulating knowledge” to “building capabilities,” especially abilities such as posing questions, making judgments, connecting across disciplines, and collaborating with others. And if curricula, assessments, and diploma systems remain within the old linear framework, the institutional system will become increasingly good at measuring “intelligence from yesterday,” but increasingly difficult to respond to today’s realities.

Local differences determine the real difficulty of AI education

Julian Melpa from the Papua New Guinea National Research Institute reminded participants that AI education is not an issue that can be advanced on its own, independent of infrastructure and cultural context. Using Papua New Guinea as an example, she pointed out that local traditional education relies heavily on communities, teacher-centered classrooms, and face-to-face teaching. Once AI education enters real life, it will immediately encounter structural constraints such as electricity, networks, teacher availability, costs, and linguistic diversity.

Especially in a society with more than 800 local languages, complex geographic conditions, and pronounced urban-rural gaps, the “efficiency dividends” brought by AI will not automatically be distributed equally. If there is a lack of infrastructure development, offline learning platforms, support for local languages, and systematic training for teachers, AI may instead intensify educational inequality and conflict with local cultural logics.

Therefore, she argued that AI education should not replace existing educational systems. Instead, it should be carefully embedded into local social conditions. This also makes the forum’s “inclusiveness” discussion more concrete: genuinely sustainable digital education is not technology-first, but context-adaptation-first.

Public-sector governance, legal governance, and cognitive change

Rommel Abilio Infante Asto, Legal Advisor at the AI Laboratory of Peru’s National Electoral Jury, presented another education scenario: AI does not happen only in classrooms—it also happens in public services, in the dissemination of legal knowledge, and in vocational learning processes. He introduced that Peru is one of the economies in the Asia-Pacific region that developed an AI legal framework relatively early, but in higher education, adult education, and skills upgrading, there are still clear gaps in existing regulations.

Using multiple tools developed by the AI Laboratory of the National Electoral Jury as examples, he explained how AI is used for civic election education and for legal information retrieval learning by public officials. At the same time, he also pointed out that Peru’s universities and academic publishing institutions do not hold consistent views on AI: some encourage critical use, while others remain clearly cautious. This difference precisely shows that as AI enters education and public knowledge systems, different institutions are still exploring their own boundaries and norms.

At the end of the forum, Li Hui, Assistant Vice President of the Education University of Hong Kong, further advanced the discussion to the question of whether “AI is reshaping the brain.” Combining his own research, he proposed that the widespread adoption of AI and digital devices is changing people’s attention networks, memory processes, executive functions, and motivational mechanisms—especially potentially having long-term impacts on the neural plasticity of children and adolescents.

In his view, AI is not just a tool for improving efficiency; it is also a “cognitive technology” that actively intervenes in thinking structures. This also means that education-related issues are no longer limited to curriculum reform, but extend to broader social problems, such as household digital-use rules, children’s exposure duration, social-media restrictions, and possible future topics like “AI co-parenting.”

A deep discussion about the future of education

Looking back at the entire forum, what is most worth noting is not whether the guests are optimistic or cautious about AI in education. Rather, it is that almost all of them are reminding the audience of the same fact: AI’s impact on education is not only a tool-level replacement, but a joint reorganization of goals, relationships, institutions, and even people themselves. From the role of universities and capability development to institutional adaptation, and from cultural differences and legal governance to the impacts studied in brain science, educational issues in the AI era are being reopened and are becoming more tightly interwoven with the labor market, the public sector, and social structures.

This also means that education reform in the AI era should not stop at technical discussions like “how to use AI in the classroom.” The deeper question is: what kind of people should education cultivate, and how should people maintain judgment, creativity, a sense of responsibility, and a community mindset in a world where intelligent systems are deeply involved? This roundtable forum did not provide a single unified answer, but it clearly shows that what matters most may be the continuous pursuit of asking and re-asking these questions themselves.