India’s classrooms are reshaping Google’s approach to AI in education

Google’s effort to fold generative AI into formal schooling is being stress‑tested across India’s vast and diverse education system, producing practical product and policy shifts. Instead of pushing a single global platform, the company is building configurable tools that let school administrators and state authorities decide how features are used, reflecting India’s state-driven curricula and strong government oversight. The emphasis is squarely on teacher-facing functionality—lesson planning, assessments and classroom management—so educators remain central rather than students receiving standalone AI tutors. India’s classrooms are rapidly adopting multimodal resources that combine video, audio, images and text to bridge language barriers and work on devices with limited capacity or intermittent connectivity. These infrastructure realities—shared devices, teacher-mediated access and variable bandwidth—have forced Google to tolerate and design for heterogeneity rather than assume one-to-one device access. The company has translated those learnings into programs such as large-scale teacher training and exam‑prep tools tailored to local needs, while rival firms establish regional teams and partnerships to win institutional influence. Researchers and policymakers in India have also flagged pedagogical risks: overreliance on automated tools can dull critical thinking and creativity, raising questions about assessment integrity and long-term learning outcomes. Those cautionary voices complicate commercial incentives to scale quickly within public systems that are politically sensitive to measurable results. By contrast, other national approaches—most notably China’s decision to fold AI instruction into mandatory, grade‑staged IT curricula—illustrate a different model: centralized rollout, uniform learning objectives, and an explicit tie to industrial strategy and domestic capacity building. That top‑down pathway can deliver scale and coherence but raises its own concerns about early exposure, online safety and unequal access across regions. The juxtaposition of India’s decentralized, teacher‑centric experiments and China’s curriculum-driven push shows that vendor strategies cannot be one-size-fits-all: companies must support administrative controls, local language and content adaptation, teacher training, and variable connectivity while also navigating national industrial and regulatory priorities. The result is that education has become a strategic battleground where technical capability meets public accountability, and where the pace and shape of AI adoption will be determined as much by governance, pedagogy and national strategy as by raw model performance.