Introduction: The East Is Rewriting the Rules of AI in Education, AI in Eastern Academia.

While Western universities debate whether students should be allowed to use AI tools at all, China’s universities are making AI use mandatory. While UK and US institutions spend enormous energy on detection software, Indian government is pledging the equivalent of over a billion dollars to make AI infrastructure available to 500 universities. While European academics worry about academic integrity, Japanese institutions are training 50,000 educators to lead AI integration in classrooms.

The contrast could hardly be more striking. The Eastern approach to AI for academia isn’t cautious, consultative, or committee-driven. It is strategic, state-backed, and moving at extraordinary speed. Across China, India, Japan, South Korea, and Singapore, governments have identified AI education as a matter of national competitiveness, and they are acting accordingly.

This doesn’t mean the Eastern approach is without complexity or controversy. Questions of surveillance, equity, data privacy, and academic freedom feature prominently in debates across the region. But the dominant frame is opportunity rather than threat, integration rather than caution, and national strategy rather than institutional autonomy.

For researchers, academics, and education policymakers globally, understanding how the East is approaching AI for academia is no longer optional. These countries are home to some of the world’s largest student populations, most ambitious AI investment programmes, and most innovative education technology companies. Their approaches will shape global norms, tool development, and academic competition for decades.

This comprehensive guide examines how China, India, Japan, South Korea, Singapore, and other Asian nations are deploying AI across their education systems in 2025 — from primary schools to research universities, from classroom tools to national policy frameworks.

China: All In on AI Education

If any country embodies an unambiguous commitment to AI in education, it is China. While other nations deliberate, China has moved with characteristic speed and scale to make AI not just permitted in academic settings but embedded, expected, and strategically central.

The Policy Foundation: AI+ Education

In April 2025, China’s Ministry of Education released sweeping national guidelines calling for comprehensive “AI+ education” reforms, aimed at cultivating students who can work effectively alongside AI systems. This followed China’s “strong-education nation” action plan announced in January 2025, which targets major educational advancements by 2035 through technological innovation.

These policy documents represent more than aspirational rhetoric. China has a track record of translating education policy into rapid implementation through its centralised system. In Beijing, a structured city-wide AI curriculum launched in 2025, requiring schools to offer at least eight hours of AI instruction per academic year, with mandatory integration across STEM subjects and a strong emphasis on AI ethics.

Shenzhen moved even faster. The city began piloting weekly AI lessons in 2023 and partnered closely with leading technology companies to bring real-world applications into classrooms. Over 100 “AI model campuses” have already been launched, with plans to train 100,000 teachers in coming years.

The University Transformation

Perhaps more striking than school-level reform is the speed of change in Chinese higher education. According to a recent survey by the Mycos Institute, a Chinese higher-education research group, the use of generative AI on campus has become nearly universal, with just 1% of university faculty and students reporting they never use AI tools. Nearly 60% said they use them frequently — either multiple times a day or several times a week.

Unlike the West, where universities are still debating how students use AI in their work, top Chinese universities are going all in. Tsinghua University is establishing a new undergraduate general education college to train students in AI plus another traditional discipline like biology, healthcare, science, or humanities. Major institutions including Renmin, Nanjing, and Fudan Universities have rolled out general-access AI courses and degree programmes open to all students, not just computer science majors. At Zhejiang University, an introductory AI class became mandatory for undergraduates starting in 2024.

At Tsinghua University, AI-powered “learning companions” now support more than 220 pilot courses spanning multiple disciplines. Chemical engineering students can review materials and practise exercises via an AI assistant at any hour, while medical students at Sichuan University conduct simulated consultations with AI-based virtual patients, developing clinical skills in a risk-free environment.

Squirrel AI: China’s Adaptive Learning Pioneer

No examination of AI for academia in China would be complete without examining Squirrel AI, arguably the world’s most ambitious education AI deployment. Unlike large language model-based tools such as ChatGPT that can explain a topic or write an essay, Squirrel AI’s system is what it calls a Large Adaptive Model (LAM), which combines adaptive AI with education-specific multimodal models. The system draws on data from more than 24 million students and 10 billion learning behaviours, breaking down subjects into thousands of knowledge points to spot gaps in student understanding.

The system’s success stories are striking. One student who could barely pass mathematics tests saw his scores improve dramatically over two years of using Squirrel AI’s personalised tutoring, eventually achieving strong results that had previously seemed impossible. Carnegie Mellon University and Yixue Education (Squirrel AI’s parent company) have even launched a joint research initiative — the CMU-Squirrel AI Research Lab — focusing on AI, machine learning, cognitive science, and human-computer interface technologies.

Squirrel AI now has more than 1,700 learning centres across China, and its influence is going global. Chinese adaptive learning platforms such as Squirrel AI Learning are being piloted in markets across the Global South, offering scalable AI-powered tutoring where local resources are limited.

The Cultural Context: AI as National Pride

The cultural divide between Chinese and Western universities on AI is stark. A Stanford University HAI report found that China leads the world in enthusiasm for AI. As the Chinese-developed model DeepSeek gains in popularity globally, many see it as a source of national pride. The conversation in Chinese universities has gradually shifted from worrying about academic integrity implications to encouraging AI literacy, productivity, and staying ahead.

Eighty percent of job openings available to fresh graduates in China listed AI-related skills as a plus in 2025. In a slowed economy and competitive job market, many students see AI as a lifeline, understanding that mastering it is not just a studying hack but a necessary professional skill.

The Surveillance Question

China’s AI education approach comes with significant caveats. Government investment has included tools that rely on large-scale data collection and in some cases camera surveillance within classrooms. In China, ethics, equitable access, privacy, and related concerns are not high priorities in the way they are in Western educational contexts.

This creates a genuine tension. The personalization and performance gains from AI education in China are real and significant. But they are achieved through data collection at a scale and intimacy that would face significant resistance in Western educational contexts. International institutions working with Chinese partners must navigate these differences carefully.

China’s Global Influence

Beyond its domestic programme, China is actively exporting its AI education model globally. Under UNESCO’s G77 + China South-South cooperation project, regional seminars held in April 2025 gathered delegates from over 36 countries in Asia and the Pacific to co-design national AI competency frameworks. More than 50 countries and 1,300 policymakers have been involved to date.

Huawei’s ICT Academy initiative has partnered with over 3,000 universities globally — many in Africa, Latin America, and Southeast Asia — training 1.3 million students in AI, cloud, and ICT skills. China is actively positioning its AI education expertise as a form of soft power, building relationships through technical cooperation and educational technology export.

India: From Aspiration to Implementation at Scale

India’s approach to AI for academia is defined by ambition matched to its extraordinary scale. With 40 million students in technical education alone, and a government that has declared 2025 the “Year of AI,” India is making moves that will reshape global academic AI.

The IndiaAI Mission

The centrepiece of India’s approach is the IndiaAI Mission, backed by a budget of ₹10,372 crore (approximately $1.25 billion). This is a comprehensive framework for national AI leadership with education as a central pillar. The Union IT Ministry plans to furnish 500 universities with GPUs, datasets, and advanced AI tools, giving researchers and students unprecedented computational resources without needing to look elsewhere. The compute pillar receives almost 45% of total mission funds.

The All India Council for Technical Education declared 2025 the “Year of AI,” directly impacting 14,000 colleges and 40 million students nationwide. The Union Budget 2025-26 allocated ₹500 crore for an AI Centre of Excellence in Education, which IIT Madras will lead.

This infrastructure push addresses one of Indian higher education’s most significant constraints. Elite institutions like the IITs and IIMs have excellent computing infrastructure, but the vast majority of India’s universities — particularly state-funded institutions — lack the hardware to run or experiment with AI models. By distributing GPUs directly to 500 universities, the government is attempting a fundamental democratisation of AI access in academic settings.

Student Adoption: Already Ahead of Policy

The FICCI-EY-P AI Adoption Survey 2025 reveals that 86% of Indian higher education students are already using AI in their studies, with 54% using it weekly and 24% daily. The most cited academic applications include generative AI for teaching materials (53%), tutoring and chatbots (40%), adaptive learning platforms (39%), and automated grading (38%).

According to the survey, 89% of students are specifically using ChatGPT for academic work. Faculty adoption varies significantly — the study found that only 17% of faculty consider themselves advanced or expert AI users, with just 6% satisfied with their institution’s AI literacy resources.

This gap between student adoption and faculty capability is one of India’s most pressing educational challenges. Students are innovating with AI tools independently, while many instructors lack the skills or confidence to guide them productively. The result is uneven and potentially risky use, without the pedagogical framing that would maximise learning outcomes.

From School to University: A Comprehensive Framework

India’s AI education ambitions extend from the earliest years of schooling through postgraduate research. India plans to implement an AI curriculum across all schools starting from Grade 3, beginning in the 2026-27 academic year. By December 2025, the creation of learning materials, teacher guides, and digital content will be completed. Educators will undergo structured, grade-specific training through NISHTHA and other recognised institutions.

At the higher education level, the University Grants Commission’s National Programme on Artificial Intelligence (NPAI) framework encourages institutions to integrate AI across both technical and non-technical disciplines. The aim is to ensure that not just engineering and computer science students, but business, humanities, and social science students also develop meaningful AI competency.

Chandigarh University has launched India’s first private AI-augmented multidisciplinary university in Uttar Pradesh, offering 43 undergraduate and postgraduate programmes across engineering, health sciences, business, liberal arts, and legal studies. The campus promises to leverage AI to drive innovation and facilitate holistic learning across all disciplines.

The Language Challenge

One dimension of AI for academia in India that distinguishes it from Western contexts is language. India has 22 officially recognised languages and hundreds of regional dialects. AI tools built primarily for English or Mandarin provide limited value to students learning in Telugu, Tamil, Bengali, or Marathi.

The IndiaAI Mission’s emphasis on sovereign datasets through AIKosha — building datasets in Indian languages, governance, and domain-specific contexts — directly addresses this challenge. The government wants every engineering and science campus to experiment with large-scale models in local languages, ensuring AI tools genuinely serve India’s linguistically diverse student population.

The Digital Divide

India’s AI education ambitions must grapple with significant inequality. Significant challenges persist: infrastructure remains uneven between elite and state-funded institutions, and comprehensive AI literacy resources are lacking across much of the system.

The contrast between an IIT student with access to computing clusters, premium AI subscriptions, and AI-literate faculty, and a student at a rural state university with limited connectivity and instructors who have never used an AI tool, is stark. Addressing this divide is both an equity imperative and a prerequisite for realising India’s broader AI ambitions.

Japan: Thoughtful Innovation with Deep Cultural Roots

Japan’s approach to AI for academia reflects its broader cultural values: meticulous, consensus-building, and grounded in long-term strategic thinking. Rather than the breakneck pace of China or the scale ambitions of India, Japan has pursued careful integration, emphasising critical thinking, academic integrity, and teacher development.

The Policy Architecture

Japan’s engagement with AI in education is grounded in its AI Strategy 2019, which laid out three core principles: human dignity, diversity, and sustainability. The strategy emphasised that AI development must be human-centred, uphold human dignity, and foster broad, sustainable applications across sectors. A critical focus is education reform, including dual-degree programmes that integrate AI and domain-specific studies.

Building on this foundation, Japan introduced a certification system for mathematics, data science, and AI education in 2021, led by the Ministry of Education (MEXT), aiming to accredit programmes that meet established standards and ensure high teaching quality.

In 2024, Japan released guidelines on generative AI use in educational settings and selected pilot schools to test them as the government weighs which regulations make most sense. This iterative, evidence-based approach reflects Japanese educational culture, which values rigorous piloting before national rollout.

The Teacher Training Revolution

Teacher training is the linchpin of Japan’s classroom AI rollout. The national AI Education Accelerator Programme trained approximately 50,000 educators by 2025 through public-private partnerships including firms like SoftBank Robotics, addressing a long-standing confidence gap. A 2022 survey found that 58% of Japanese teachers felt underprepared for AI in the classroom.

This investment in teacher confidence is characteristic of Japan’s approach. Rather than simply deploying tools and hoping educators adapt, Japan has invested substantially in developing educators who can lead pedagogical change — using AI thoughtfully rather than following technology vendors’ lead.

MEXT is also exploring a proposed one-year graduate teacher licensing pathway to attract IT and global-affairs professionals into teaching, bringing fresh technical skills into the education system from adjacent industries.

University-Level Innovation: Osaka’s Comprehensive Framework

The University of Osaka has developed a particularly comprehensive approach to AI in higher education, drawing on interviews with AI research leaders to outline directions universities are taking as they move toward deeper AI integration. The institution’s approach addresses generative AI in teaching, learning, administrative processes, assessment, and research, balancing opportunities with safeguards for academic integrity and educational quality.

This case study is instructive for institutions globally. Rather than treating AI as purely a student tool to be managed, Osaka’s framework considers the full ecosystem — how AI changes the work of researchers, administrators, and teachers, as well as students.

Corporate Investment in Academic AI

Microsoft committed nearly $2.9 billion to expand AI infrastructure and workforce programmes in Japan through 2025. SoftBank is developing large-scale AI capacity, and KDDI’s Osaka Sakai project — built with HPE and NVIDIA technology — brings liquid-cooled, rack-scale GPU clusters to Japanese researchers and startups.

This corporate investment is reshaping Japanese academic AI research. Universities can now access computing resources that were previously available only to the largest tech companies, opening new possibilities for AI research in medicine, materials science, climate modelling, and beyond.

Robotics: Japan’s Unique AI Education Contribution

Japan brings something unique to the global conversation on AI in education: its world-leading expertise in robotics. Japanese universities and companies have pioneered the integration of physical robots into educational settings, from teaching tools that demonstrate physics concepts to social robots that support students with autism spectrum conditions.

This robotics expertise enriches Japan’s approach to AI in ways that other countries are only beginning to explore. It also creates international collaboration opportunities, particularly in industrial and engineering education, where Japanese institutions remain globally pre-eminent.

South Korea: Building AI into the National Curriculum

South Korea’s approach to AI for academia demonstrates what is possible when a highly educated, technologically sophisticated society makes AI literacy a national priority.

From High School to University

South Korea aims to have AI coursework embedded in its national curriculum across all grade levels, with initiatives beginning at the high school level. The Korean Ministry of Education’s KERIS unit is designing and piloting extensive teacher development programmes around AI and other technologies. The Ministry’s Future of Education Center provides model classrooms where visitors can experience advanced technologies in education.

This systematic, curriculum-embedded approach means South Korean students will encounter AI concepts not as an optional extra or single course, but as a thread running through their entire educational experience. The cultural context matters here: South Korea’s highly competitive education culture, where students rigorously prepare for university entrance examinations, means that skills seen as career-enhancing spread rapidly through the student population.

Research Leadership

South Korea is the second-largest contributor to AI in K-12 education research in Asia. China leads the Asian research domain with 180 publications indexed in Scopus accounting for 33.9% of the total output, while South Korea follows in second place with 67 publications. Hong Kong ranks third with 62, demonstrating significant contributions to AI education research.

Korean universities are not merely implementing AI tools developed elsewhere — they are generating original research on effective AI pedagogy, equity in AI education, and the learning science behind AI-assisted instruction. This research capacity gives South Korea an important voice in shaping global norms for AI in education.

Singapore: Small Nation, Global Leader

Singapore punches well above its weight in AI for academia, consistently cited as a global exemplar of thoughtful, equitable AI education integration.

The Strategic Advantage

Singapore’s advantages are significant. As a small, wealthy, highly connected city-state with a world-class education system, Singapore can experiment, iterate, and scale AI education initiatives faster than any large country. Decisions made nationally can be implemented in every school within months.

Singapore is a leader in the use of technology in education. The government has invested heavily in AI for education, and Singaporean schools and universities use generative AI extensively.

Singapore was also an early pioneer in AI education research. Singapore published the first studies on AI in K-12 education in Asia, with the first research appearing in 1996 when Fok and Ong initiated a high school project integrating AI in robotics.

The International Benchmark

Singapore’s National Institute of Education (NIE) is globally recognised for its research on AI in teaching and learning. Its work on how teachers develop AI competency, how students learn with AI tools, and how assessment must evolve in AI-rich environments is cited extensively by researchers worldwide.

Singapore’s experience is particularly valuable because it combines high educational quality with genuine commitment to equity. The government has consistently worked to ensure AI tools and education are accessible to all students regardless of background — a challenge that larger nations with more uneven education systems find considerably more difficult.

Southeast Asia: Emerging Voices

Beyond the major players, Southeast Asia is an increasingly important frontier for AI for academia, with countries developing distinct approaches reflecting their own educational contexts and national priorities.

Indonesia: Scale and Ambition

Indonesia, with the world’s fourth-largest population and a young demographic profile, faces enormous challenges in providing quality education to students across thousands of islands. AI tools that can deliver personalised tutoring and quality content to remote areas represent a potential solution to longstanding equity problems.

The Jakarta-based education technology sector is growing rapidly, with startups developing AI tools specifically designed for Indonesian languages, curricula, and learning contexts. International companies including those from China — whose tools are increasingly designed for multilingual deployment — see Indonesia as a priority market.

Vietnam: Rapid Reform

Vietnam has moved swiftly to modernise its education system with AI tools, driven by government recognition that workforce AI skills are essential for economic competitiveness. Three amended education laws recently modernised Vietnam’s system and opened doors for global partnerships, creating opportunities for international collaboration on AI education.

Vietnamese universities are increasingly partnering with international institutions on AI research, and the government is investing in teacher training programmes to ensure educators across the country can use AI tools effectively.

Malaysia: The Balanced Approach

Malaysia’s approach to AI in education reflects a careful balancing act between embracing innovation and maintaining cultural and linguistic diversity. The country’s multilingual character — with significant populations speaking Malay, English, Chinese, and Tamil — makes language-inclusive AI tools a particular priority.

Malaysia’s Minister of Education participated in the UNESCO Digital Learning Week in 2025, engaging with global leaders on how AI is transforming learning futures and positioning Malaysia as an active contributor to international policy development.

Cross-Regional Themes: What Eastern AI Education Teaches the World

Drawing together the experience of China, India, Japan, South Korea, Singapore, and Southeast Asia, several themes emerge that have profound implications for AI for academia globally.

Theme 1: State Strategy Beats Institutional Deliberation

The most striking difference between Eastern and Western approaches to AI in education is the role of the state. In China, India, Japan, and South Korea, national governments have made strategic decisions about AI in education and are actively funding, mandating, and incentivising implementation.

In contrast, Western countries tend to leave educational AI policy largely to individual institutions, creating the fragmented landscape of varying policies, patchy implementation, and inconsistent student experiences documented in our earlier articles on US and UK academia.

The Eastern model has significant advantages in speed and scale. National investment in infrastructure, teacher training, and curriculum development creates conditions for rapid, consistent change. The limitations involve academic freedom, diversity of approach, and the ability of frontline educators to innovate within top-down frameworks.

Theme 2: AI as Opportunity, Not Threat

A Stanford HAI report found that China leads the world in enthusiasm for AI. The cultural divide is stark compared to Western contexts where many educators see AI as a threat to manage. This cultural orientation shapes everything from policy to classroom practice.

When students and educators approach AI as an opportunity — a skill to master rather than a shortcut to police — the entire conversation changes. Institutions focus energy on developing AI literacy rather than detecting misuse. Educators innovate with AI rather than defending against it. Students develop genuine competency rather than hiding their tool use.

This is not simply naivety about AI’s risks. Chinese, Japanese, and South Korean academics are as aware of AI’s limitations and potential harms as their Western counterparts. But they have made a deliberate choice to lead with opportunity while managing risk, rather than leading with caution while grudgingly permitting use.

Theme 3: Equity and Access as Central Concerns

Across the region, the digital divide and equitable access to AI education are central concerns — though they manifest differently in each context. In India, the challenge is geographic and infrastructural, ensuring students in rural state universities have the same access as those at elite urban institutions. In China, the challenge involves ensuring that commercially driven AI tools don’t benefit only families wealthy enough to pay for premium services.

Japan and South Korea, with their well-funded public education systems, face smaller access gaps but still must address disparities between well-resourced urban schools and those in rural or economically disadvantaged areas. Singapore’s combination of national wealth and small scale makes equitable distribution easier, but the city-state remains attentive to ensuring AI tools serve all communities.

Theme 4: Teacher Development as the Critical Variable

From Japan’s 50,000-educator training programme to India’s NISHTHA teacher development system to South Korea’s KERIS teacher development initiatives, Eastern countries consistently identify teacher training as the crucial lever for educational AI.

This focus reflects a sophisticated understanding of how educational change actually works. Technology alone doesn’t improve learning. It requires educators who understand how to use tools pedagogically, who can guide students in developing genuine competency rather than dependency, and who can adapt their practice as AI capabilities evolve.

The teacher development investments being made across Asia will compound over time. Countries that train educators well today will have school systems better prepared for each successive generation of AI tools. Those that invest in technology without corresponding investment in human capital will find their tools underused or misused.

Theme 5: Language and Cultural Context Matter Enormously

AI tools built primarily for English speakers have significant limitations across Asian educational contexts. The investment in language-specific AI — whether India’s push for models in local languages, Japan’s domestic LLM development through LLM-jp, or China’s deployment of tools like DeepSeek — reflects recognition that culturally and linguistically appropriate AI is essential for genuine educational impact.

This has implications for the global AI tool market. Companies developing educational AI tools that are not designed for multilingual, multicultural deployment will find themselves excluded from the fastest-growing education markets in the world.

Research Collaboration: Building East-West Academic Bridges

Despite the different approaches, research collaboration between Eastern and Western institutions on AI in education is growing. Several important initiatives deserve attention.

The Carnegie Mellon-Squirrel AI Partnership

The CMU-Squirrel AI Research Lab focuses on AI, machine learning, cognitive science, and human-computer interface technologies, aiming to improve large-scale personalised educational experiences for K-12 students worldwide. This collaboration brings together American cognitive science expertise with Chinese educational data at unprecedented scale.

UNESCO’s Bridging Role

UNESCO’s Digital Learning Week and its G77+China South-South cooperation framework are actively creating spaces for East-West dialogue on AI education. The UNESCO Digital Learning Week 2025 gathered education ministers, senior policymakers, scholars, and industry leaders from over 50 countries to discuss deep integration of AI and education and key pathways for digital transformation worldwide.

These UNESCO platforms are important because they create shared frameworks that allow different national approaches to be compared, critiqued, and refined through international dialogue.

The British Council’s India-UK AI Partnerships

The British Council has identified AI education as a priority area for India-UK partnership. For the UK higher education sector, new Indian AI university initiatives present both competition and opportunity. UK institutions could benefit from joint research, AI-focused education, collaborative certification models, and targeted upskilling courses enabling progression into postgraduate study and professional pathways.

Practical Implications for Academics Worldwide

For researchers and educators in the UK, US, and Europe, the Eastern AI education revolution has several immediate practical implications.

The Competitive Dimension is Real

India pledged $1.25 billion for AI development, while Canada pledged $2.4 billion and China launched a $47.5 billion semiconductor fund. These investment figures represent a profound shift in the global academic landscape. Countries that have historically sent their best students to Western universities for graduate education are now building world-class AI research infrastructure domestically.

Within a decade, the flow of global academic talent in AI-related fields may look very different from today. Western institutions that wish to remain attractive for AI research must offer genuine advantages — in academic freedom, interdisciplinary collaboration, or research quality — that justify the choice over increasingly well-resourced Eastern alternatives.

Eastern Tools Are Worth Exploring

Many academics outside Asia are unaware of the sophisticated AI education tools developed in China, India, and Japan. Tools like Squirrel AI represent genuine pedagogical innovation — not just chatbot-style content generation but deep learning science applied to personalised instruction. Researchers in education technology should be engaging with these tools and the research supporting them.

Language AI Is the Frontier

The investment in multilingual AI tools across Asia will produce capabilities relevant for Western academics working with multilingual student populations. Tools that can support students whose first language is not English, or that can bridge cultural contexts in international research collaborations, are being developed with intensity in Asian markets. Western academics would benefit from following these developments.

The Data Question

Eastern AI education approaches generate educational data at a scale Western institutions rarely approach. Research drawing on Chinese or Indian educational AI deployments has access to datasets involving millions of students and billions of learning interactions. This creates both extraordinary research opportunities and important ethical questions about data use, consent, and privacy that must be navigated thoughtfully in international collaborations.

Looking Ahead: The Asian AI Education Frontier

Several developments will define the next phase of AI for academia across Asia.

The DeepSeek Effect

China’s development of DeepSeek — a large language model that achieved comparable performance to leading US models at dramatically lower cost — has significant implications for educational AI globally. If high-quality AI capabilities can be achieved more cheaply through Chinese model architectures, the cost barrier to AI education deployment in lower-income countries falls substantially. Expect to see DeepSeek-based educational tools spreading rapidly through Asian and Global South educational markets.

Agentic AI in Education

Japan in particular is exploring agentic AI — systems that can take sequences of actions autonomously — in educational contexts. Rather than simply answering questions, agentic AI tutors could design personalised learning pathways, identify misconceptions, and adapt instruction in real-time. Japan’s robotics expertise gives it a unique advantage in developing AI that can both respond to and initiate educational interaction.

The Assessment Revolution

As AI becomes ubiquitous in student work across Asia, universities are being forced to rethink assessment fundamentally. The shift is from evaluating what students can produce in controlled conditions to assessing how they think, collaborate, and add value when AI tools are available — mirroring real professional contexts. East Asian universities, with their historically exam-focused cultures, face particular challenges here, but also significant opportunities to pioneer new models of competency assessment.

Regional AI Education Governance

The emergence of regional AI education governance frameworks — through ASEAN, UNESCO’s Asia-Pacific work, and bilateral agreements — will create shared standards and practices that shape how AI is used in education across the region. These frameworks will increasingly influence global debates, as the countries they cover represent the majority of the world’s students.

Conclusion: The Global Conversation Needs Eastern Voices

The global conversation about AI for academia has been dominated by Western institutions, Western concerns, and Western frameworks. The Russell Group principles, US university policies, and European AI regulations set the tone of debate even for institutions far beyond their jurisdictions.

This is changing. East Asia accounts for 74.9% of all AI in K-12 education research output in Asia, with China leading as the dominant contributor globally, followed by South Korea, Hong Kong, and other regional powers. The research base, the investment, the student numbers, and the implementation experience are increasingly Eastern.

For academics and institutions anywhere in the world, engaging with what China, India, Japan, South Korea, and Singapore are doing with AI in education is no longer a matter of exotic interest. It is essential professional awareness. The tools being developed, the pedagogical approaches being tested, the policy frameworks being built, and the research being generated across Asia will shape educational AI globally.

The Eastern approach — strategic, state-backed, opportunity-focused, and operating at extraordinary scale — offers both inspiration and challenge to Western institutions still debating whether to engage. The debate has been decided in the East. The only remaining question is how quickly and how thoughtfully to move forward.

For researchers, academics, and educators everywhere, that is perhaps the most important lesson Asia’s AI education revolution has to teach.

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Frequently Asked Questions

How does China’s approach to AI in education differ from Western countries? China has moved from debating whether students should use AI to actively requiring it, with nearly universal adoption in universities. Major institutions have made AI courses mandatory for all students and the cultural frame treats AI mastery as an essential career skill rather than an academic integrity risk.

What is India’s IndiaAI Mission and how does it affect higher education? The IndiaAI Mission is backed by approximately $1.25 billion and plans to equip 500 universities with GPUs, datasets, and AI tools, dramatically expanding access to AI research infrastructure. The government has declared 2025 the “Year of AI,” with the All India Council for Technical Education targeting 14,000 colleges and 40 million students.

What makes Japan’s approach to AI in education distinctive? Japan combines its world-leading robotics expertise with a human-centred AI philosophy grounded in its AI Strategy 2019 principles of human dignity, diversity, and sustainability. The country has invested heavily in teacher training — around 50,000 educators were trained by 2025 — emphasising pedagogical leadership rather than simply deploying tools.

Is South Korea ahead of Western countries in AI education? In terms of national curriculum integration, yes. South Korea is embedding AI coursework across all grade levels from high school upward, backed by extensive teacher development programmes. The country is also the second-largest contributor to AI in K-12 education research in Asia.

Can Western academics collaborate with Asian institutions on AI research? Yes, and such collaborations are growing. Initiatives like the CMU-Squirrel AI Research Lab demonstrate how Western cognitive science expertise can combine with Asian educational data and deployment scale. UNESCO provides multilateral frameworks for cross-regional collaboration on AI education policy and research.