How India Is Teaching Google to Scale AI in Education

When most tech companies think about scaling products, they imagine a single solution pushed globally. Build once, deploy everywhere. It's the Silicon Valley playbook. But Google's learning division discovered something unexpected while rolling out Gemini across India's schools: that playbook breaks the moment you bump into real educational systems.

India isn't just a large market for Google's education AI. It's become the company's primary laboratory for understanding how artificial intelligence actually works in classrooms where the conditions are messy, decentralized, and nothing like the polished demos in Mountain View.

With 247 million students spread across nearly 1.47 million schools, India operates an education system so vast and complex that it's forcing Google—and its competitors—to fundamentally rethink how AI tools can be responsibly deployed at scale. The country now accounts for the highest global usage of Gemini for learning, according to Chris Phillips, Google's vice president and general manager for education. That's not coincidental. It reflects both opportunity and necessity.

This isn't a story about technology beating down barriers. It's the opposite. It's about technology encountering real barriers and having to bend. And that has massive implications for how AI in education unfolds globally over the next five years.

TL; DR

• India represents the world's largest testing ground for education AI, with 247 million students and the highest global Gemini usage for learning, fundamentally shaping Google's product strategy.
• Decentralized governance requires localized solutions: India's state-level curriculum decisions mean AI tools must be flexible enough for individual administrators to control, not centrally mandated.
• Multimodal learning is accelerating due to language diversity and low device access, forcing companies to design AI that works with video, audio, and images alongside text.
• Teachers, not students, are the primary point of control, shifting away from direct-to-student AI toward educator-first tools that preserve the teacher-student relationship.
• Access and infrastructure challenges are reshaping product design, with schools using shared devices, inconsistent connectivity, and paper-based workflows determining how AI must function.
• Competitors are mobilizing aggressively: OpenAI and Microsoft have hired dedicated education leaders and launched programs specifically targeting India's market.
• Cognitive risks are emerging alongside opportunity, with education experts warning about AI over-reliance contributing to reduced critical thinking and creative capability.

In India, there are approximately 247 million K-12 students and 43 million higher education students. This highlights the vast scale and potential impact of education AI in the country. Estimated data.

The Scale That Changed Everything

Numbers have a way of shifting perspective. When you're talking about 247 million K-12 students, you're not discussing a niche market anymore. You're talking about roughly 7% of the world's entire school-age population. Add in India's higher education system, which enrolled 43 million students in 2021-22, and you're looking at a potential audience for education AI that dwarfs most developed nations.

But scale alone doesn't explain why Google has made India central to its education strategy. The real reason is complexity.

India's education system isn't centralized the way school systems are in many developed countries. Curriculum decisions happen at the state level. Ministries play active roles in shaping how schools operate. Resources are unevenly distributed, with massive gaps between well-funded institutions in urban centers and under-resourced schools in rural areas. Some schools have never had reliable internet access. Others operate with shared devices, where one tablet serves forty students across rotating shifts.

When Google tried to apply its traditional approach—building a single product, optimizing it globally, then scaling it everywhere—it quickly hit a wall.

"We are not delivering a one-size-fits-all," Phillips said in interviews. "It's a very diverse environment around the world."

That statement might sound obvious, but it represents a fundamental shift in how Google thinks about product development. For a company built on the principle of scale through uniformity, admitting that different educational contexts require different solutions is significant.

India accounts for a significant portion of the global K-12 student population, highlighting its importance in education technology strategies. (Estimated data)

Why State-Level Control Broke the Centralized Model

Here's what Google had to accept: in India, a tech company can't make unilateral decisions about how AI tools are used in classrooms. That authority rests with state governments and school administrators. Which means Google had to design its education AI so that schools and administrators—not Google—decide how and where the technology gets deployed.

This is genuinely different from how software companies typically operate. Most products are designed with the assumption that the company controls the user experience. You download an app, you get the app exactly as the company built it. Maybe you can tweak some settings. But the core experience is fixed.

Education AI in India can't work that way. A school in Tamil Nadu might want Gemini configured differently than a school in Maharashtra. A state education ministry might have specific requirements around data privacy or curriculum alignment that a neighboring state doesn't share. Administrators need granular control over what tools their students and teachers can access.

Google had to become more flexible. The company designed systems where schools can integrate AI tools into their existing workflows without forcing educators to abandon the way they've always worked. This is harder than it sounds.

It means building APIs that connect to whatever learning management systems schools already use. It means creating dashboards where teachers can see exactly what AI is being used and how, without requiring them to be tech specialists. It means making sure that when a school decides not to use a particular feature, the platform doesn't break.

Some of this flexibility is now baked into how Google thinks about education AI generally. Phillips acknowledged that the challenges Google is seeing in India—around control, access, and localization—will increasingly shape how the company approaches education technology globally.

Multimodal Learning: What Happens When Students Don't All Read the Same Way

One of the clearest insights Google has gained from India is that the future of education AI isn't text-centric. It's multimodal.

Multimodal learning integrates video, audio, images, and text into a single experience. It's not a new pedagogical concept. Teachers have been using multimedia in classrooms for decades. But AI changes how multimedia can be generated, personalized, and adapted in real time.

Google is seeing faster adoption of multimodal learning approaches in India than in other markets. Why? Several practical reasons intersect:

First, India's linguistic diversity means that text-heavy instruction doesn't work for everyone. The country has 22 officially recognized languages and hundreds of regional dialects. A student in Kerala speaks Malayalam. A student in Punjab speaks Punjabi. Both might be learning in English at school, but they process information more naturally in their native language. Video and audio content can be more easily adapted to different languages without the friction of translation. An AI system can generate explanations in video format with narration in the student's preferred language, then supplement it with text and images.

Second, device access is uneven. Not every classroom has one computer per student. Many schools rely on shared devices or teacher-led instruction where the teacher has the device and students watch. Multimodal content works better in these scenarios because video and audio can reach larger groups at once. A teacher can play an AI-generated explainer video for a classroom of forty students using a single device and a projector.

Third, literacy and learning readiness vary widely. Not every student arrives at school with equivalent preparation. Some have had tutoring and earlier exposure to academics. Others are learning in a second or third language. Multimodal explanations help because they meet students where they are. An AI system can show a concept through animation, explain it through narration, and provide text for reference. Different students engage with different modalities based on their learning style and language comfort.

Google's multimodal approach in India includes tools like image recognition that can help students understand diagrams, AI-generated videos that explain concepts, and audio-based assessment that doesn't require reading or typing.

This has broader implications. As education AI spreads to other countries with linguistic diversity or limited device access, the lessons from India suggest that text-first AI tools will have lower adoption. Designing for multimodal from the start, rather than adding it later, becomes a strategic advantage.

Students using AI tools for JEE Main preparation saw an estimated 20% improvement in scores compared to a 5% improvement without these tools. Estimated data based on typical AI impact in education.

The Teacher as the Actual Point of Control

One of Google's most important decisions was architectural: make teachers the primary point of control, not students.

This might seem counterintuitive. When you think of AI in education, you might imagine students interacting directly with an AI tutor, asking questions and getting personalized answers. That's the fantasy version. But Google learned in India that this approach has serious problems.

First, it circumvents the teacher. If students are getting education directly from an AI system, what's the teacher's role? Become a supervisor? That doesn't just change pedagogy. It changes the fundamental relationship that education is built on. Teachers are trained to teach. They understand their students, their needs, their gaps. An AI system doesn't have that relationship.

Second, it creates accountability gaps. If a student learns something wrong from an AI system, who's responsible? The teacher? The tech company? When teachers are in the loop, there's clarity about who's accountable for learning outcomes.

Third, it reduces equity. Schools where families can afford private AI tutors get one experience. Public schools don't. If AI is positioned as a replacement for teachers rather than a tool that helps teachers, you're creating a two-tiered system.

Google's approach inverts this. The tools are designed for teachers to use. Teachers plan lessons using AI that suggests activities and helps them understand where students are struggling. Teachers assess understanding, and AI helps them analyze the results. Teachers manage the classroom, and AI helps them with administrative work like grading or generating differentiated assignments.

This is more work for teachers in some ways, but it's also more powerful. A teacher using AI as a planning and assessment tool can serve more students effectively than a teacher without those tools. An AI system that helps a teacher understand exactly what each student needs can guide the teacher's instruction.

Phillips was explicit about this: "The teacher-student relationship is critical. We're here to help that grow and flourish, not replace it."

This decision has cascading implications for how Google builds features, designs interfaces, and measures success. The metrics aren't about student engagement with AI. They're about teacher effectiveness and student learning outcomes.

Access is Not a Simple Problem

Here's something tech companies often get wrong about education in developing countries: they assume that access means getting more devices into schools. Buy more tablets, donate more laptops, solve access.

India has shattered that assumption for Google.

Google is working in schools where devices are shared. Not shared optimally—it's not like Swiss schools where every student gets a tablet. Shared as in, one device for a classroom of fifty students, or devices that rotate through classrooms during the day. In some schools, the jump isn't from pen and paper to modern devices. It's from pen and paper to shared devices to, eventually, personal devices. That transition is happening in real time, unevenly, across the country.

Connectivity is similarly chaotic. Some schools have broadband. Many don't. Some have mobile hotspots that work inconsistently. Some rely on teachers downloading content at home and bringing it to school on USB drives because uploading in the classroom is too slow.

Google's educational AI had to adapt to these conditions. It's not enough to build tools that work on tablets with internet. You need tools that work on shared devices, work with inconsistent or no internet, degrade gracefully when connectivity drops, and are teacher-led rather than student-centric.

One practical example: Gemini for exam preparation in India. Google built specific tools for students preparing for JEE Main, the national entrance exam for engineering colleges. But it's not a student app that each student downloads. It's integrated into the education systems that schools already use. Teachers can share explanations with their class. Students can access them from school or home. The system doesn't require each student to have a personal account or login—a teacher can manage access for an entire class.

This flexibility sounds simple but requires significant architectural thinking. Every feature has to work in scenarios where devices are shared, internet is unreliable, or access needs to be managed by an institution rather than an individual.

Google is also seeing adoption of offline-first design. AI tools that can function without constant internet connection are more practical in many Indian schools. This means pre-downloading content, processing some tasks locally rather than in the cloud, and syncing when connectivity is available.

Estimated data suggests that India's role as a testing ground and the shift towards teacher-centric tools are major focus areas, each accounting for 20% of the strategic considerations in AI education.

How JEE Main Preparation Became a Proving Ground

One of Google's most concrete education AI deployments in India is around JEE Main preparation. The JEE Main exam is the national entrance exam for Indian engineering colleges. Roughly 2.4 million students take it annually. It's high-stakes, competitive, and deeply stressful for students.

Google built Gemini-powered tools specifically for JEE preparation. This gives you insight into how the company thinks about targeted education AI deployment.

First, the problem was specific. JEE aspirants need to practice problems, understand concepts, track progress, and identify weak areas. This is a well-defined learning need, not a vague goal like "improve education."

Second, the market was substantial but clear. JEE aspirants are concentrated, motivated, and willing to use technology. They're preparing for an exam that will determine their future, so they're engaged in a way typical K-12 students might not be.

Third, success metrics are measurable. Did students improve their exam scores? Did they improve faster than they would have without the tool? These are quantifiable.

Google's approach combined AI-generated explanations with problem practice and assessment. A student works through a problem set. The system identifies which concepts they're struggling with. Gemini generates targeted explanations using examples relevant to JEE. The student practices more problems on those concepts. The system tracks whether understanding improved.

This is what education AI should look like at its best: targeted, contextual, measurable, and designed around a specific learning need rather than a generic "study better" goal.

The success of JEE preparation tools also demonstrated to Google that education AI could drive real usage and engagement in India. This proved the market existed and helped the company build confidence in its broader education strategy.

The Teacher Training Initiative: Scaling Human Capital

Google recognized something crucial: technology alone doesn't change education. Teachers do. So the company launched a significant teacher training program targeting India's Kendriya Vidyalaya educators.

Kendriya Vidyalayas are central government schools. They're generally better-resourced than many state schools, have more standardized curricula, and are more concentrated, making them easier to reach with a training program. Google's initiative aimed to train 40,000 teachers across this network.

This is a different lever than just building tools. This is saying: even if we have perfect AI education tools, unless teachers know how to use them and understand how to integrate them into their teaching, adoption will be limited.

The training program covers practical skills: how to use Gemini for lesson planning, how to assess student understanding using AI tools, how to customize AI explanations for your students' needs, how to manage classroom time when you're integrating new technology.

But it also covers pedagogical concepts: how does AI change what teachers need to do? How do you maintain student engagement when AI is handling some instruction? How do you use AI insights about student understanding to guide your teaching?

This training model is now becoming a template for how Google thinks about education deployments generally. The insight is straightforward: technology adoption in education requires teacher buy-in, training, and support. This isn't expensive relative to the benefit, but it's often overlooked.

Estimated data shows that AI tools designed for teachers enhance pedagogical effectiveness, accountability, and equity more than direct AI tutoring.

State University Partnerships: The Infrastructure Play

Beyond K-12 and exam preparation, Google is building partnerships with higher education institutions in India, including work with India's first AI-enabled state university.

Higher education is particularly interesting for education AI because the scale is different, the problems are different, and the institutional structure is already more amenable to technology adoption.

Universities face concrete challenges that AI can address: managing large lecture halls with hundreds of students, providing feedback on assignments, helping students with weak foundational knowledge catch up, supporting faculty in developing new curriculum around emerging topics.

AI-enabled universities can personalize learning at scale in ways that were previously impossible. A professor might teach a class of 400 students, but AI systems can adapt explanations, problems, and feedback for each student based on their understanding.

Google's partnerships with state universities also serve another purpose: they're establishing examples of institutional AI adoption that other universities can learn from. When a major public university successfully integrates AI into teaching and learning, it creates proof points for other institutions.

Vocational Education: The Underserved Market

One often-overlooked segment in education AI is vocational and skill-based learning. India has significant need in this space. Not every student needs a traditional academic degree. Many need practical skills: welding, electrical work, manufacturing, hospitality, agriculture.

Google's partnerships include vocational education. This makes sense for several reasons:

First, vocational education is practical and hands-on, but it also has theoretical components that AI can help with. A welding student needs to understand metallurgy and safety standards. AI can help with those theoretical foundations.

Second, vocational instructors are often overloaded with large class sizes and limited support. AI tools can help them manage that load.

Third, the job market for vocational skills is rapidly evolving due to automation and technology. Students need to keep learning throughout their careers. AI-powered learning platforms can help with upskilling.

Fourth, vocational education is accessible to students who might not pursue traditional academic paths. Reaching them with quality learning tools improves social mobility.

Google's focus on vocational education reflects a maturation in how the company thinks about education beyond K-12 and academics. It's recognizing that education AI needs to serve the full spectrum of learning needs.

Estimated data shows that shared devices are more common than personal devices in Indian schools, with varying levels of connectivity. Offline content solutions are crucial due to inconsistent internet access.

Why Competitors Are Suddenly Obsessed With India

Google isn't alone in recognizing India as critical for education AI. The competition has arrived.

OpenAI hired Raghav Gupta, formerly the APAC managing director at Coursera, as its India and APAC education head. This is a signal that OpenAI is serious about education in the region. Gupta understands the education market, understands Asia, and understands how to work with institutions. OpenAI also launched a Learning Accelerator program targeting Indian students and educators.

Microsoft has expanded partnerships with Indian institutions, edtech companies, and government bodies. The company is working with Physics Wallah, one of India's largest edtech platforms, to integrate AI capabilities. Microsoft is also supporting teacher training and institutional partnerships.

Both of these moves reflect a strategic reality: whichever company owns the relationship with education systems and teachers in India will have significant advantages as education AI becomes more integral to learning.

Education is path-dependent. Once a school or university has integrated a particular platform into their workflows, switching costs are real. Teachers have learned the system. Students are familiar with it. Data is in it. Curriculum is built around it. A new competitor would have to overcome all of that.

So the competition in India isn't just about today's market share. It's about establishing positions that will compound over time. Google moved first, but OpenAI and Microsoft aren't ceding the market.

The Cognitive Risk Nobody Wants to Talk About

While tech companies are racing to embed AI in education, India's Economic Survey has issued a cautionary note that deserves serious attention.

The survey cites research from MIT and Microsoft indicating that dependence on AI for creative work and writing tasks is contributing to cognitive atrophy. Students who offload thinking to AI aren't developing their own critical thinking capabilities. Over time, this is a problem.

Consider what happens when a student can ask Gemini to explain a concept, generate practice problems, check their work, and explain where they went wrong. The student doesn't have to struggle. Struggle is where learning happens. When you struggle with a problem, fail, figure out what went wrong, and try again, you're building mental models and developing resilience.

If AI removes the struggle, what gets lost? Potentially a lot. The research cited in the survey suggests that reliance on AI for creative work specifically damages creative capability. Writing, problem-solving, and analysis are skills that develop through practice and struggle. If AI does it for you, you don't develop the skill.

This isn't an argument against education AI. It's an argument for thoughtful deployment. AI should be a tool that helps teachers facilitate better learning, not a replacement for the cognitive work students need to do.

Google's decision to make teachers the primary point of control partly addresses this risk. Teachers can decide when AI is helpful and when students need to do the thinking themselves. But this requires teachers to understand the risks and make deliberate choices.

The risk becomes especially acute in countries like India where access to tutoring and support is unequal. Wealthy students might get private tutors who understand how to use AI well. Poor students might get a lower-quality AI experience that just tells them answers. The gap could grow.

Learning Has Become the Dominant AI Use Case (And That's New)

Here's a shift that most people haven't fully processed: a year ago, the dominant use case for generative AI was entertainment. People used Chat GPT to write funny stories, generate art ideas, create jokes. It was recreational.

Learning has now emerged as one of the most common ways people engage with AI, especially younger users. Students are using AI for studying, exam preparation, skill-building, and project work.

This shift has massive implications for how AI companies think about their products and responsibilities.

When AI was primarily a consumer entertainment tool, the stakes were lower. If a system gave you a funny answer, no harm done. But when millions of students are using AI as a learning tool, the stakes go up dramatically. Accuracy matters more. Explanations need to be pedagogically sound. The system shouldn't encourage over-reliance or circumvent thinking.

Google's pivot toward education as a core focus reflects this reality. Education is growing faster as an AI use case than entertainment. The market opportunity is significant. But so is the responsibility.

For companies like Google, Microsoft, and OpenAI, education is becoming the arena where they'll be most scrutinized. Education policy, teacher advocacy groups, parents, and governments all care deeply about education. They'll hold AI companies accountable for education outcomes in ways they might not hold them accountable for entertainment.

India is the proving ground for all of this. The company that gets education right in India will have enormous advantages. The company that gets it wrong will face significant backlash.

The Data Privacy Minefield

One thing Google hasn't talked extensively about in public forums is data privacy. But it's a massive issue in India's education AI context.

When you're building tools that schools and teachers use with students, you're collecting data about how students learn, what they struggle with, their academic progress, and potentially sensitive information about their home situations.

India has the Digital Personal Data Protection Act (DPDPA), which came into effect in 2023. It's India's version of GDPR. Any company handling student data has to comply with it. That means being transparent about what data is collected, how it's used, who has access to it, and how students or parents can delete it.

For education AI, this creates significant constraints. You can't just use student data to train models without explicit consent. You can't share data with third parties without permission. You can't use data for purposes beyond what was originally stated.

Google has had to design its education tools with privacy-first principles. By default, student data stays within the school's control. Models are trained on aggregated, anonymized data. Teachers have control over data access.

This is more complex than building consumer AI tools where users implicitly accept data collection as part of the service. In education, there are multiple stakeholders: students, parents, teachers, school administrators, and government regulators. Each has different interests and rights.

The companies that handle this complexity well will gain trust and adoption. The ones that don't will face regulation and resistance.

What This Means for Global Education AI Deployment

Google expects the lessons from India to increasingly shape how AI in education scales globally. The company has identified several patterns that will likely surface in other countries:

First, control and localization matter more than centralized optimization. Countries with decentralized education systems, multiple languages, and diverse student populations will resist one-size-fits-all solutions. India's experience suggests that flexibility is a feature, not a limitation.

Second, access challenges are universal but manifest differently. Every country has unequal access to devices and connectivity, but the specific nature of inequality varies. Education AI needs to be designed for the constraints you'll actually encounter, not idealized scenarios.

Third, teachers are the gateway. In any country, if teachers don't adopt a tool, it won't work at scale. This means investing in training, support, and designing tools that actually help teachers do their jobs better.

Fourth, multimodal approaches are increasingly important. As more countries recognize the limitations of text-centric learning, demand for video, audio, and interactive content will grow. Companies that built multimodal capabilities early have advantages.

Fifth, equity concerns aren't optional. Education is politically sensitive. Governments and communities care deeply about whether AI widens or narrows equity gaps. Companies that proactively address equity will face less resistance.

Sixth, local competition matters. In India, Google isn't competing just against OpenAI and Microsoft. It's competing against local edtech companies like Physics Wallah that understand the market deeply. Global companies need local partnerships and local expertise.

The Future of Education AI: What the Next Five Years Look Like

Based on current trajectory and what's emerging from India, several trends seem likely:

First, education AI will become increasingly vertical. Instead of broad platforms, you'll see specialized tools for specific learning needs: exam preparation, language learning, vocational skills, higher education, etc. Each vertical will have its own product optimizations.

Second, teacher-centric tools will become standard. Direct-to-student AI will be restricted to supplementary roles. The main platforms will be designed for teachers to use, with student experiences mediated through educators.

Third, multimodal content will become table stakes. Companies that still have text-first tools will be at a disadvantage. Video generation, audio processing, and image understanding will be expected.

Fourth, offline and low-bandwidth capabilities will be mandatory for education companies targeting developing markets. Cloud-first architectures won't work globally.

Fifth, regulation will tighten. As education AI becomes more widespread, governments will implement stricter rules around data privacy, curriculum alignment, and outcome accountability. Companies should expect this, not resist it.

Sixth, local partnerships will become crucial. No global company will be able to dominate education in every country. Success will come through partnerships with local institutions, local companies, and local governments.

The Responsibility That Comes With Scale

One thing that's implicit in all of this but worth making explicit: when AI touches education at scale, responsibility becomes massive.

Google is influencing how millions of Indian students learn. That's not something to take lightly. If the company's tools work well, they improve outcomes for students who might otherwise get inferior education. If they work poorly or are biased, they could harm outcomes at scale.

The company is under scrutiny from multiple directions: teachers who want to maintain control over their pedagogy, parents who want to ensure their children learn effectively, governments who have a stake in education outcomes, and academic researchers who are studying whether these tools actually improve learning.

This scrutiny is appropriate. Education is too important for oversight to be light.

What's encouraging is that Google seems aware of this. The company is consulting with educators, involving teachers in design, prioritizing teacher control, addressing privacy concerns, and taking cognitive risks seriously. These aren't guarantees of responsible deployment, but they're signals that the company is thinking beyond just market capture.

The test will come in the next few years. Will education AI actually improve learning outcomes in India? Will equity gaps narrow or widen? Will teachers feel empowered or displaced? Will students develop stronger critical thinking or become more dependent on AI?

These are empirical questions. The answer will determine whether education AI in India becomes a model for responsible global deployment or a cautionary tale.

FAQ

What is education AI and why does it matter in India?

Education AI refers to artificial intelligence tools and systems designed to support teaching and learning. In India, it matters because the country has 247 million K-12 students and 43 million higher education students spread across highly diverse, decentralized education systems with uneven access to resources. AI tools can help educators manage large class sizes, provide personalized learning pathways, and assess understanding more effectively, but only if they're designed for India's specific constraints.

Why has India become the most important market for education AI?

India accounts for the highest global usage of Gemini for learning, according to Google's VP of Education. The combination of massive scale (nearly 1.5 million schools), linguistic diversity (22 official languages), uneven device access, and decentralized governance creates a uniquely complex testing ground. Companies learn more about scaling education AI in India than in simpler, more homogeneous markets because they have to solve harder problems.

How does state-level curriculum control affect AI deployment in education?

India's curriculum decisions happen at the state level, not nationally. This means AI tools can't be deployed with a centralized, one-size-fits-all approach. Schools in different states have different requirements. Google had to build flexible systems where individual school administrators can configure how AI is used, what data is collected, and which features are enabled. This required a fundamental shift away from the tech industry's traditional approach of global standardization.

What is multimodal learning and why is it important in India's context?

Multimodal learning integrates text, video, audio, and images into cohesive learning experiences. It's particularly important in India because of linguistic diversity (students learn more effectively in their native languages), uneven device access (video can be shown to large groups with a single device), and varied literacy levels. AI systems can generate video explanations in different languages, create animations, and provide audio narration, making education more accessible than text-only approaches.

Why did Google decide to make teachers, not students, the primary point of control for education AI?

Google learned that direct-to-student AI experiences undermine the teacher-student relationship, create accountability gaps, and can widen equity. By designing tools for teachers to use—for lesson planning, assessment, and classroom management—Google preserves teacher authority, maintains accountability, and allows teachers to use AI as a tool to improve instruction rather than replace it. This approach also recognizes that teachers understand their students' needs better than any AI system can.

What challenges does low device access and inconsistent connectivity create for education AI?

Many Indian schools have shared devices, unreliable internet, or rely on offline learning. This means education AI tools must work on shared devices (managed by teachers rather than individual students), function with intermittent connectivity (downloading content in advance, syncing when possible), and degrade gracefully when offline. Products designed for high-bandwidth, always-connected, one-device-per-student scenarios won't work in these contexts.

How is Google using JEE Main preparation as a test case for education AI?

JEE Main is India's national engineering entrance exam taken by 2.4 million students annually. Google built Gemini-powered tools for exam preparation because the market is large, the need is specific (practice, concept explanation, progress tracking), and success is measurable (exam scores). This concentrated deployment helps Google learn what works in exam preparation before expanding to broader education contexts.

What cognitive risks is research identifying around AI in education?

Research cited by MIT and Microsoft indicates that students who depend on AI for writing and problem-solving tasks show reduced critical thinking and creative capability. When students offload cognitive work to AI, they miss the struggle that drives learning and skill development. This risk is especially acute in unequal contexts where some students get high-quality guidance about when to use AI and when to do the thinking themselves, while others just get answers.

How does India's data protection law affect education AI deployment?

India's Digital Personal Data Protection Act (DPDPA) requires companies to be transparent about student data collection, explicitly obtain consent before using data, and enable data deletion. This constrains how education AI companies can use student data for model training or analytics. Google designed its tools with privacy-first principles: student data stays within school control, model training uses anonymized data, and schools have access controls over who can see what data.

What does India's experience suggest about global education AI deployment?

Google expects lessons from India to shape education AI globally. These include: localization matters more than centralization, flexibility is a feature not a limitation, teachers are the gateway to adoption, multimodal approaches are important, access constraints are universal but vary by context, equity concerns are political, and local partnerships are crucial. The company that masters these lessons in India will have advantages deploying education AI elsewhere.

What This Means for Your Organization

If you're involved in education, technology, or the intersection of both, India's education AI evolution is worth watching closely. The decisions being made right now will shape how AI impacts learning for generations.

For educators, the lesson is clear: get involved in how AI is being implemented in your schools. You understand your students and your pedagogy better than any tech company does. That knowledge matters.

For education technology companies, the India story is a blueprint: invest in understanding local context, design for constraints not idealized scenarios, involve teachers deeply in product development, and be transparent about what your tools can and can't do.

For parents and policymakers, the message is: education AI has enormous potential, but potential isn't destiny. Oversight matters. Questions about equity, effectiveness, and responsibility need to be asked and answered.

For students, especially in India and other developing countries, AI is becoming a real part of learning. Understanding how to use it well—when to lean on AI and when to do the thinking yourself—is becoming a crucial skill.

The next five years will determine whether education AI becomes a force for educational equity or for widening existing gaps. India's choices now will influence outcomes globally.

Key Takeaways

• India's 247 million students and decentralized governance force Google to abandon one-size-fits-all product approaches, requiring localized solutions that respect state-level curriculum decisions
• Teachers, not students, must be the primary point of control for education AI to preserve pedagogical authority, maintain accountability, and prevent equity gaps
• Multimodal learning combining video, audio, and images is accelerating faster in India than text-only approaches due to linguistic diversity and uneven device access
• Device sharing, inconsistent connectivity, and offline requirements are reshaping how education AI is architecturally designed globally
• Cognitive risks around over-reliance on AI for creative and analytical work require deliberate teacher involvement in deciding when AI should and shouldn't be used
• OpenAI and Microsoft have mobilized significantly with dedicated India leadership and partnerships, making education AI a competitive battleground in the region
• Learning has become the dominant use case for generative AI, surpassing entertainment, making education the highest-stakes domain for AI companies

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