How Indian schools and colleges are adopting AI, and why it matters for education

Artificial intelligence has moved from labs into classrooms across India. Schools and colleges are experimenting with new curricula, training teachers, and building projects that use real datasets. At the same time, policy is starting to set the direction. The goal is simple. Help students learn better, help teachers teach better, and prepare graduates for a labour market that now expects basic AI skills.

What has changed in policy and infrastructure

In March 2024, the Union Cabinet approved the IndiaAI Mission with an outlay of over ₹10,300 crore. The plan includes a national compute infrastructure of at least 10,000 GPUs, a public marketplace for models, and a FutureSkills pillar to expand AI education. This gives schools and universities a clearer pathway to content, labs, and computing power in the coming years.

For foundational awareness, the government’s AI For All program provides a free, short online course that schools often assign to students and parents. It was launched with CBSE and Intel and remains open to anyone.

What schools are doing right now

CBSE introduced Artificial Intelligence as a skill subject in Class 9 in 2019 and later in senior secondary. The board also released teacher handbooks and an integration manual that shows how to weave AI themes into subjects from Classes 6 to 12. Enrolments have risen sharply. Ministry data shared in Parliament shows roughly 7.9 lakh students opted for AI at the secondary level in 2024–25, and more than 50,000 at the senior secondary level, across thousands of schools.

States are running their own experiments. Kerala’s public education mission has rolled out AI projects in classrooms and trained teachers at scale through KITE, with an eye on safety and inclusion.

Schools are also using AI around the classroom in practical ways:

• Low-stakes practice with instant feedback for math, reading, and language.
• Support for students with different needs, such as text-to-speech and language translation.
• Project-based work using simple computer vision or data analysis kits in Atal Tinkering Labs, which now reach thousands of campuses.

These uses sit well with global guidance from UNESCO and the OECD, which stress human-led learning, equity, and teacher capacity.

What colleges and universities are doing

On the degree side, AICTE’s model curriculum for B.Tech in Computer Science with Artificial Intelligence and Data Science has aligned many institutes on core subjects, labs, and ethics topics. Universities across states have adopted these templates for full programs and minors. New departments and centres are being announced in state and central universities, often with industry help.

Beyond degree titles, colleges are adding AI across the campus:

• Common AI literacy modules for first-year students, often delivered through SWAYAM or vendor-neutral workshops.
• Domain courses that apply AI in business analytics, healthcare, agriculture, media, and law.
• Research projects that use compute from national facilities or cloud credits while following basic data governance rules.

University leaders are also drafting usage policies for generative tools in coursework and exams, aligning with UNESCO’s advice on responsible use and academic integrity

Why AI matters for Indian education

Better learning outcomes

Adaptive practice tools can identify gaps and suggest targeted exercises. They are not a replacement for teachers. They give teachers more time for explanation, discussion, and projects. Global guidance warns against over-reliance and urges classroom supervision, which Indian boards are already stressing in training.

Inclusion and language support

AI can help students who need assistive technologies or learn in more than one language. The OECD’s recent work on equity and inclusion with digital tools sets out practical ways to use AI without widening gaps. The principle is simple. Use tech to support students who need more, not to sort or label them.

Employability

Entry-level roles now expect basic data handling, prompt writing, and the ability to judge AI outputs. Structured school exposure through CBSE’s skill subjects and college minors gives students a starting point. The IndiaAI FutureSkills pillar aims to push this further into Tier 2 and Tier 3 cities.

Research and innovation

Affordable access to models and shared datasets allows students to build prototypes with clear social value, from crop advisory chatbots to local-language interfaces. The IndiaAI Mission’s compute and marketplace plans are designed to reduce the friction of starting such projects.

Risks and how schools can manage them

1. Academic integrity
   Set clear rules on when students can use generative tools and how to cite them. Use oral checks, drafts, and viva-style questioning for high-stakes work. UNESCO recommends institutional policies, teacher training, and age-appropriate limits.
2. Data privacy and safety
   Do not upload students’ personal data into public tools. Prefer platforms with school contracts and audit trails. Keep a simple record of what data leaves the campus and why. International guidance calls for safety-by-design and human oversight.
3. Bias and fairness
   Have students test tools for errors and bias. Treat this as part of digital citizenship. Encourage counterexamples and manual checking.
4. Access and equity
   Plan for shared devices, offline modes, and local language content so AI does not deepen divides. The OECD’s equity framework is a useful checklist for leaders.

A simple adoption plan for school leaders

• Start with awareness. Enroll students and parents in AI For All. Run a staff workshop that covers both uses and limits.
• Use what CBSE already provides. Offer AI as a skill subject where feasible. Use the integration manual to insert small AI themes into existing subjects.
• Pick one classroom use case this term. For example, AI-assisted reading support or formative quizzes with teacher review.
• Write a one-page usage policy. State when AI tools are allowed, what must be cited, and what remains teacher-only. Base it on UNESCO guidance.
• Track outcomes. Measure time saved, gaps identified, and student confidence. Share findings with parents and your school board.

A practical roadmap for colleges

• Align with AICTE model syllabi for core programs and add minors in non-CS departments.
• Create a campus policy for GenAI in teaching, exams, and research. Include disclosure rules and human oversight in grading and admissions.
• Build faculty capacity. Set up short courses for instructors in prompt design, data handling, and assessment redesign.
• Partner for projects. Use IndiaAI marketplace offerings and cloud credits to power capstone work with real users.

Summary

AI is not a magic fix. It is a set of tools that can help teachers personalise learning, make classrooms more inclusive, and give students a head start in a changing economy. India’s policy push, CBSE’s curriculum, and AICTE’s model degrees show that the shift is already underway. The most successful schools and colleges will be the ones that keep humans in charge, follow simple rules on safety and fairness, and measure what works in their own context.

Reference

Press Information Bureau

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