By bringing AI into primary classrooms, India has made an early bet in the global AI race, but its success will hinge less on policy intent and more on delivery at scale.
In October 2025, India’s Ministry of Education announced that Artificial Intelligence and Computational Thinking (AI & CT) would be introduced as school subjects from Class 3 onwards, affecting every CBSE (Central Board of Secondary Education), KVS (Kendriya Vidyalayas), and NVS (Navodaya Vidyalayas) school across the country. Six months later, in April 2026, CBSE launched a structured curriculum aligned with the NCF-SE (National Curriculum Framework for School Education), 2023. Education Minister Dharmendra Pradhan described it as “a transformative step towards future-ready learning”, which “formally introduces structured AI education into the school ecosystem at scale”.
Three key shifts make this initiative transformative:
First, it redefines how India’s school system understands basic universal skills. Foundational skills are no longer limited to literacy and numeracy alone; they now include the ability to think logically, recognise patterns, break down problems, use data carefully, and engage with technology responsibly.
Second, there is a move away from a previous pedagogical focus in which AI readiness was approached through optional skill modules, coding, or exposure to AI tools in senior grades. In the new curriculum, even in Classes 6–8, AI is only one part of the curriculum: 40 hours are assigned to advanced computational thinking, 20 hours to AI concepts, and 40 hours to interdisciplinary projects. The strength of the design, therefore, is its CT-first approach, which frames AI readiness not as tool use but as a habit of reasoning that children can begin developing early.
Foundational skills are no longer limited to literacy and numeracy alone; they now include the ability to think logically, recognise patterns, break down problems, use data carefully, and engage with technology responsibly.
Third, computational thinking is not being introduced as a separate technical subject in the early years. Rather, it is treated as a way of thinking that can be built across subjects. From Class 3, children are expected to develop these capacities through puzzles, visual reasoning, patterns, decomposition, and step-by-step problem-solving embedded across multiple subjects.
This move by the Ministry of Education (MoE) and CBSE must be viewed as a response to a rapidly evolving global policy landscape in which schooling is emerging as a critical component in preparing for the global AI race.
Several countries are increasingly focusing on AI education as a strategic investment for economic competitiveness, mandating a national-level pipeline for AI-ready talent from a young age. The World Economic Forum’s Future of Jobs Report 2025 projects the creation of 170 million new jobs by 2030, with AI and big data skills identified as the fastest-growing competencies globally, outpacing all other skill categories. For India—a nation of 1.4 billion people, 15.5 percent of whom are aged 0–9 years—this is a question of national competitiveness.
In China, the Ministry of Education (MoE) issued two detailed guidelines on AI education in primary and secondary schools last year, with local authorities developing implementation plans; in Beijing, for instance, schools are required to provide a minimum of eight hours of AI instruction annually at the primary and middle school levels.
Since the 2025–2026 academic year, the United Arab Emirates has integrated AI as a formal subject from kindergarten to Grade 12. Meanwhile, Saudi Arabia has launched a nationwide AI curriculum targeting six million students, aligned with Saudi Vision 2030 for economic diversification and national development.
Early exposure to AI is increasingly seen as a gateway to future skills, work, innovation, and productivity, ultimately strengthening a country’s economic and technological leadership.
In Singapore, a national digital literacy pathway introduces primary students to AI through computational thinking, coding, cyber wellness, emerging technologies, and carefully controlled exposure to AI tools under the Code for Fun programme. From 2025, additional AI for Fun modules will allow primary students to undertake 5–10 additional hours of instruction in generative AI and smart robots. At the upper primary level, students receive 10 hours of lessons in computational thinking, coding, and emerging technologies, including AI.
In the United States, a 2025 executive order on advancing AI education emphasises early exposure, teacher training, and workforce alignment, while agencies such as the National Science Foundation are investing in teacher capacity for AI-integrated instruction.
Across these national priorities, a common thread is visible: building an AI talent pipeline is no longer seen as a late-stage skilling agenda. It is now becoming an early-stage schooling agenda, with an emphasis on turning technological familiarity into guided skill development, logical reasoning, and responsible use. Early exposure to AI is increasingly seen as a gateway to future skills, work, innovation, and productivity, ultimately strengthening a country’s economic and technological leadership.
India’s approach sits between centralised curriculum-led models and decentralised ecosystem approaches. Its strength lies in its scale and its emphasis on foundational thinking rather than early technical specialisation. As primary students increasingly interact with AI—through chatbots, image generators, and coding tools—computational thinking (CT) will provide them with a basic understanding to interpret these systems. However, three contextual gaps in the Indian educational context are likely to shape outcomes.
First, while the CBSE focus ensures minimum infrastructure—as affiliation norms require computer labs and internet connectivity—minimum infrastructure does not guarantee meaningful access. Schools still differ widely in the availability of functioning devices, bandwidth, teacher preparedness, lab time, maintenance, and the ability to integrate technology into everyday teaching. This makes the curriculum’s CT-first design useful, but primarily in the early grades and does not eliminate the access gap in later years. Moreover, curriculum reform of this nature depends largely on how teachers interpret and deliver it. While CBSE has identified CT and AI as training priorities and the Ministry has indicated capacity-building through national programmes, the scale of India’s school system means that variations in teacher readiness will remain substantial for many years.
India is moving early but has not yet translated intent into practice. It has signalled intent at scale, but the supporting ecosystem—including digital access, teacher readiness, assessment reform, and attention to child safety—remains uneven.
Second, AI and CT remain peripheral to the core assessment system. Since they are not consistently integrated into high-stakes examinations, parents and teachers may deprioritise them in favour of traditionally assessed subjects. This misalignment may reduce instructional time, attention, and accountability for them and weaken implementation. Therefore, while the CBSE circular’s emphasis on valuing the “process of thinking” is important, it requires alignment across textbooks, teacher guidance, and evaluation systems. The OECD and European Commission’s joint AI Literacy Framework for Primary and Secondary Education highlights the significance of clear, measurable assessment benchmarks for AI literacy curricula to result in meaningful learning outcomes.
Finally, broader systemic issues such as curriculum overload, language diversity, and socio-economic disparities shape how students engage with AI. Many learners are first-generation digital users, which influences their ability to critically understand concepts such as data, bias, privacy risks, and automation. Without contextual adaptation—through local language resources, age-appropriate data literacy, and attention to ethical and safety concerns—AI education does not adequately address the existing risks associated with children’s use of AI tools.
Therefore, in comparative global terms, India is moving early but has not yet translated intent into practice. It has signalled intent at scale, but the supporting ecosystem—including digital access, teacher readiness, assessment reform, and attention to child safety—remains uneven.
The long-term significance of this move lies in its potential to contribute to the AI talent pipeline. Early exposure to CT, coupled with digital literacy, data literacy, and AI ethics, can strengthen analytical ability across disciplines and may widen the base from which advanced AI skills emerge.
At present, however, this remains an aspiration and, given contextual constraints, risks becoming symbolic rather than transformative. For system-level transformation at scale, the current mandate should be viewed as the first phase of a national AI education architecture anchored in four sequential priorities.
First, prioritise teacher capability through sustained, practice-based training that demonstrates how CT can be taught through everyday classroom activities across subjects and languages.
Second, there is a need to establish child-centred governance frameworks for the use of AI in schools. This includes clear guidelines on data protection, platform use, transparency, and accountability, aligned with global principles on children’s rights in digital environments, within the framework of the Digital Personal Data Protection Act (DPDPA), 2023.
Third, invest in public, low-cost, and multilingual learning resources that reduce dependence on proprietary tools and ensure that AI literacy is not confined to better-resourced schools.
Fourth, align assessment with reasoning, ensuring that students are evaluated on explanation, problem-solving, and judgment rather than recall.
India has issued the right mandate at the right moment. The next task is to convert that mandate into a delivery architecture that can sustain learning across classrooms, different school systems, and regions. If this is done well, the Class 3 rollout could serve as the foundation for a broader AI education pathway. If not, it risks becoming another future-facing reform whose ambition is visible on paper but remains uneven in practice.
Arpan Tulsyan is a Senior Fellow with the Centre for New Economic Diplomacy at the Observer Research Foundation.
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Arpan Tulsyan is a Senior Fellow at ORF’s Centre for New Economic Diplomacy (CNED). With 16 years of experience in development research and policy advocacy, Arpan ...
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