Amid a global rush to put generative AI in classrooms, Google is stress-testing what scale really means in India. The company says the country now leads worldwide usage of Gemini for learning, and its frontline lessons—spanning governance, language, and access—are shaping how Google designs education AI for public systems, not just private pilots.
Those lessons crystallized this week in New Delhi at Google’s AI for Learning Forum, where Chris Phillips, Google’s vice president and general manager for education, met with school leaders and policymakers. The headline takeaway: India’s complexity is forcing Google to localize, decentralize, and put teachers firmly in the driver’s seat.
Why India Became the Testbed for Education AI
India’s school system serves roughly 247 million students across about 1.47 million schools, supported by 10.1 million teachers, according to the government’s Economic Survey 2025–26. Higher education enrollments topped 43 million in 2021–22, up 26.5% since 2014–15. Scale alone makes India a consequential proving ground; fragmentation makes it indispensable.
Curriculum decisions sit with states, not a national authority, and instruction spans dozens of languages and dialects. Access is uneven: classrooms often rely on shared or teacher-led devices, and connectivity can swing from fiber to intermittent mobile data. That blend of size, diversity, and constraint is pushing Google to move beyond its traditional one-size-fits-all product playbook.
Localization Over One-Size-Fits-All Approaches
In India, Google is learning that control must flow to schools and ministries. Instead of a centrally defined AI bundle, the company is building for modularity: state boards and administrators can decide which features are on, which subjects get support, and how outputs align with local curricula from NCERT and state boards.
Language coverage and cultural context are equally non-negotiable. Multimodal prompts—voice, images, and video alongside text—matter in multilingual classrooms where text-heavy instruction is not always realistic. This mirrors patterns seen across India’s digital public infrastructure, from DIKSHA’s QR-coded textbooks to PM eVidya, where content localization and distribution are built in from the start.
Teacher First, Not Direct-to-Student Deployments
Google’s India approach centers teachers as the primary point of control. The product emphasis is on lesson planning, assessment support, and classroom management—not bypassing educators with direct-to-student chatbots. That stance aligns with what ministries and school networks repeatedly ask for: AI that enhances the teacher-student relationship, with human oversight kept intact.
Early deployments reflect that philosophy. Google is supporting AI-powered JEE Main preparation through Gemini, launching a nationwide training program for 40,000 Kendriya Vidyalaya educators, and working with public institutions on vocational and higher education, including India’s first AI-enabled state university. The metric that matters most in these rollouts is practical: hours saved for teachers and measurable gains in student comprehension, not just usage spikes.
Scaling Education AI Under Real-World Constraints
India’s classrooms often jump directly from pen-and-paper to AI tools. Devices are shared, labs are scheduled, and bandwidth may drop at peak times. Google’s teams are therefore prioritizing lightweight, voice-forward, and image-aware interactions that work on smartphones and shared screens as well as PCs—an approach that reduces the penalty for not having one-to-one devices.
This is also where multimodal learning is accelerating fastest. Teachers can snap a photo of a handwritten solution for instant feedback, generate bilingual explanations for mixed-language classes, or convert lesson plans into visuals for projectors. The constraint-driven design choices forged in India—a bias toward low-bandwidth robustness and teacher-mediated workflows—are likely to travel well to other public systems.
The Competitive Race and Emerging Guardrails
Rivals are staking out India too. OpenAI has built a local education leadership presence, tapping former Coursera APAC chief Raghav Gupta and launching a Learning Accelerator program. Microsoft has expanded partnerships with public institutions and edtech players like Physics Wallah to deliver AI-powered learning and teacher training. The battleground is clear: become the default layer embedded in public education procurement and practice.
Guardrails are tightening in parallel. India’s Economic Survey flags risks from uncritical AI use, citing research from MIT and Microsoft that links heavy AI assistance to reduced critical thinking on creative tasks. That evidence is steering policy conversations toward “human-in-the-loop” models, transparency about sources, and assessments that test reasoning rather than mere recall—areas where teacher-first design is a practical safeguard.
A Playbook for Education AI With Global Implications
The emerging India playbook is straightforward to state and hard to execute: localize deeply, put educators in charge, design for low connectivity and shared devices, and measure outcomes that school leaders actually track. With more than a billion internet users and the highest global usage of Gemini for learning, India offers both scale and stress that few markets can match.
Whether this becomes the blueprint elsewhere will depend on evidence from large, state-run rollouts: what improvements show up in independent evaluations, and at what cost per learner. But one conclusion is already clear from Google’s India push: the future of AI in education will be decided in systems that look less like a campus lab and more like a crowded classroom with a single shared device—and a teacher firmly at the center.
