Bharat EduAI Stack and the Future of Digital Learning in India

Digital Learning

Digital Learning Latest News

  • The Centre has announced the integration of AI tools in teaching from the next academic session, backed by the launch of Bodhan AI and the Bharat EduAI Stack.

AI in Education: Background and Policy Context

  • Artificial Intelligence (AI) has emerged as a transformative force across sectors, including healthcare, agriculture, governance, and education. 
  • In the education sector, AI can enable personalised learning, real-time assessment, multilingual content delivery, and data-driven policy decisions.
  • India’s policy shift toward AI in Education aligns with the National Education Policy (NEP) 2020, which emphasises technology-enabled learning, foundational literacy and numeracy, and multilingual education. 
  • The NEP also advocates adaptive learning systems and the integration of emerging technologies in teaching and evaluation.
  • Globally, AI tools are used for:
    • Personalised content recommendation
    • Automated grading and feedback
    • Intelligent tutoring systems
    • Language translation and speech recognition
  • However, most existing AI systems are built primarily for English and rely on global platforms. 
  • This creates limitations for a linguistically diverse country like India, where school education takes place in multiple regional languages.

Centre of Excellence and Institutional Framework

  • The Centre’s initiative is anchored at the Centre of Excellence in AI for Education at IIT Madras, which was announced in the Union Budget with an allocation of Rs. 500 crore.
  • To operationalise the initiative, a not-for-profit company named Bodhan AI has been launched. It will function as a technology backbone provider and build core AI infrastructure tailored to Indian needs.

Bharat EduAI Stack as Digital Public Infrastructure

  • Bodhan AI will develop the Bharat EduAI Stack as a Digital Public Infrastructure (DPI) for education.
  • Digital Public Infrastructure refers to scalable digital systems that provide public services efficiently, similar to how UPI transformed digital payments. In this case, the EduAI Stack will serve as a foundational layer for AI applications in education.
  • Key components include:
    • AI models trained in Indian languages
    • Automatic speech recognition systems
    • Speech synthesis tools
    • Language understanding and diagnostics models
  • Instead of directly building classroom apps, Bodhan AI will create the “basic building blocks.” Edtech companies and state governments can plug their applications into this sovereign AI infrastructure. This approach aims to:
    • Reduce dependence on foreign AI platforms
    • Promote indigenous AI models
    • Enable scalable deployment across schools

Likely AI Tools and Applications

  • Personalised Learning for Students
    • AI tools will help students understand concepts in their mother tongue and identify learning gaps. 
    • Voice-based exercises can be delivered through phones, tablets, or laptops. The system can:
      • Provide instant feedback
      • Generate personalised worksheets
      • Suggest targeted practice modules
    • This is especially significant for foundational literacy and numeracy, where early interventions are critical.
  • Assistance for Teachers and Parents
    • AI-generated reports can help teachers track student performance and recommend interventions. Teachers can use AI-driven diagnostics to design remedial strategies.
    • For parents, AI-based dashboards may provide insights into their child’s progress.
  • Administrative and Policy Support
    • At the district or state level, AI tools can analyse aggregated data to assess school performance. This enables evidence-based policy decisions, resource allocation, and targeted interventions.

Funding and Operational Model

  • The initial funding comes from the Union Budget allocation for the Centre of Excellence. Over time, the system is expected to become self-sustaining through:
    • Maintenance contributions from state governments
    • Equity participation by start-ups using the infrastructure
    • Collaborative partnerships with edtech firms
  • The long-term vision is to evolve into a community-driven ecosystem, similar to open-source platforms like Linux.

Data Protection and Ethical Concerns

  • Data Privacy
    • Student inputs, written responses, and voice recordings constitute personal data. Authorities have emphasised that such data should not be stored in public forums and must remain secure.
    • This aligns with India’s broader digital governance framework, including the Digital Personal Data Protection Act.
  • Screen Time
    • To reduce excessive screen exposure, voice-based tools are being prioritised. The focus is on assistive use rather than replacing classroom teaching.
  • Equity and Access
    • Digital infrastructure gaps remain a challenge in rural and remote areas. Effective implementation will require device access, connectivity, and teacher training.

Significance for India’s Education System

  • The Bharat EduAI Stack represents a structural shift in how technology can be embedded into public education. Its importance lies in:
    • Building sovereign AI capabilities
    • Strengthening multilingual learning
    • Supporting teachers rather than replacing them
    • Creating scalable Digital Public Infrastructure
  • If implemented effectively, the initiative can improve learning outcomes, reduce regional disparities, and strengthen India’s position in educational technology innovation.

Source: IE

Digital Learning FAQs

Q1: What is the Bharat EduAI Stack?

Ans: It is a Digital Public Infrastructure platform that will provide AI models and tools for education across India.

Q2: What is the role of Bodhan AI?

Ans: Bodhan AI will develop core AI building blocks and infrastructure for educational applications.

Q3: Which institution hosts the Centre of Excellence in AI for Education?

Ans: IIT Madras hosts the Centre of Excellence, backed by a Rs. 500 crore Budget allocation.

Q4: What kind of AI tools will be introduced in classrooms?

Ans: Tools for personalised learning, speech recognition, multilingual support, and teacher assistance will be introduced.

Q5: What are the main concerns regarding AI in education?

Ans: Key concerns include data privacy, screen time, and equitable access to digital infrastructure.

AI-Powered Distributed Renewable Energy (DRE) – Building India’s Citizen-Centric Energy Future

AI-Powered Distributed Renewable Energy (DRE)

AI-Powered Distributed Renewable Energy (DRE) Latest News

  • At the India AI Impact Summit held at Bharat Mandapam, senior policymakers and global experts deliberated on the theme ‘Global Mission on AI for Energy Scaling through citizen-centric India Energy Stack’.
  • Reflecting the global interest in India’s AI-energy convergence model, the Indian government highlighted how Artificial Intelligence (AI) can become a game changer for India’s rapidly expanding Distributed Renewable Energy (DRE) sector. 

Understanding Distributed Renewable Energy (DRE)

  • DRE refers to small-scale, decentralised renewable power systems (few kW to MW scale) located near the point of consumption — such as rooftop solar systems, small wind turbines, biomass-based units, and solar pumps.
  • Unlike conventional centralised grids, DRE promotes energy decentralisation, local generation, and consumer participation.

India’s Renewable Energy Landscape

  • Key data points:
    • 52% (about 272 GW) of India’s total installed power capacity is now from non-fossil fuel sources.
    • Solar capacity: ~140 GW.
    • DRE: 38 GW. Nearly 18 GW was added in the DRE segment in the last 15 months.
  • Public expenditure: Approximately $9 billion on rooftop solarisation, and $4 billion on PM-KUSUM.
  • Major schemes driving DRE expansion: Pradhan Mantri Surya Ghar Muft Bijli Yojana, and Pradhan Mantri KUSUM Yojana.
  • Enabling factors: This rapid scale-up was enabled through technology integration benefiting consumers, vendors, banks, field workers, and DISCOMs.

Why AI is Crucial for the Next Phase of Energy Transition

  • Structural challenges in the grid: 
    • Transformers designed for unidirectional power flow. Emergence of ‘prosumers’ (consumers who also generate electricity). Increased stress on distribution networks. 
    • Need for demand response management and predictive maintenance.
  • AI applications in DRE: 
    • AI can enable -
      • Weather forecasting and predictive analytics for solar generation.
      • Asset performance monitoring across geographies.
      • Peer benchmarking for rooftop systems.
      • B2B electricity trading enablement.
      • Predictive load management.
      • Grid stability management.
    • Government’s emphasis: AI will move the system from reactive governance to predictive governance — enabling India to “act, not react”.

AI as Development Infrastructure

  • AI should be viewed as core development infrastructure, similar to power grids, DISCOMs, and smart meters.
  • This aligns with India’s digital public infrastructure (DPI) approach — suggesting the creation of an India Energy Stack, analogous to India Stack in fintech.
  • Strategic vision:
    • Scale AI deployment — not treat it as pilot projects.
    • Position India as the “Google of AI for Energy” globally.
    • Build interoperable digital architecture for energy markets.

Governance and Regulation

  • Concerns:
    • Energy transition increases system complexity.
    • AI innovation does not automatically equal progress.
    • Poor digital regulation (e.g., social media concentration) led to Big Tech dominance.
  • Key governance principles:
    • Open standards (like TCP/IP model).
    • Open-source AI systems.
    • Prevent monopolisation by global AI giants.
    • Promote local solutions tailored to farms, grids, and decentralised energy systems.
  • This raises critical questions about data sovereignty, digital regulation, energy security, and technological self-reliance (Atmanirbhar Bharat).

Defining Success - What Will AI-RE Convergence Achieve in the Next 2-3 Years?

  • Reduction in overall cost of power to consumers.
  • Increased industrial competitiveness.
  • Transition from consumer empowerment to prosumer empowerment.
  • Grid readiness for high renewable penetration.
  • Improved energy access and reliability.

Key Challenges and Way Forward

  • Legacy grid infrastructure constraints: Build an India Energy Stack - interoperable digital layers for generation, distribution, trading.
  • DISCOM financial stress: Promote open-source AI ecosystem - encourage startups, enable local innovation, avoid concentration risks.
  • Data governance, cybersecurity risks and risk of AI monopolisation: Strengthen regulatory frameworks - open standards, anti-monopoly safeguards, data privacy protections.
  • AI-energy integration:
    • Invest in AI-driven grid modernisation - smart transformers, real-time load balancing, AI-based forecasting.
    • Integrate AI with climate goals - support India’s Net Zero 2070 target, align with Nationally Determined Contributions (NDCs).

Conclusion

  • India stands at the intersection of energy transition and digital transformation. 
  • With over half its installed capacity already non-fossil, and rapid growth in distributed renewable energy, the next phase will depend not just on adding capacity but on intelligently managing complexity.
  • The convergence of AI and DRE may well determine whether India becomes a passive technology adopter — or a global leader shaping the future of sustainable, citizen-centric energy systems.

Source: IE

AI-Powered Distributed Renewable Energy (DRE) FAQs

Q1: What is the role of AI in scaling Distributed Renewable Energy (DRE) in India?

Ans: AI enables predictive analytics, demand response management, grid optimisation and prosumer empowerment.

Q2: How does the rise of ‘prosumers’ pose structural challenges to India’s power distribution network?

Ans: The emergence of prosumers disrupts traditional unidirectional grids, necessitating AI-driven bidirectional load management.

Q3: Why should AI be treated as development infrastructure in India’s energy transition?

Ans: Like grids and DISCOMs, AI forms a foundational digital layer that enhances efficiency, scalability.

Q4: What are the governance concerns associated with AI integration in the energy sector?

Ans: Without open standards, regulatory safeguards and data sovereignty frameworks, AI-driven energy systems risk monopolisation.

Q5: How can AI–Renewable Energy convergence contribute to India’s climate and economic goals?

Ans: AI lowers power costs, improves industrial competitiveness, strengthens grid readiness and accelerates renewable penetration.

Fertiliser Industry in India – Explained

Fertiliser Industry

Fertiliser Industry Latest News

  • The Uttar Pradesh government has banned the sale of non-subsidised fertilisers by urea manufacturers and suppliers, raising concerns over excessive controls in the Indian fertiliser industry.

Structure of the Fertiliser Industry in India

  • The fertiliser sector in India is one of the most regulated industries in the country. 
  • It plays a crucial role in ensuring food security, given India’s large agricultural base and dependence on chemical fertilisers such as urea, DAP, MOP and NPK complexes.
  • The maximum retail price (MRP) of urea is fixed at Rs. 266.5 per 45-kg bag, and this rate has remained largely unchanged since November 2012.
  • Although some fertilisers such as Di-Ammonium Phosphate (DAP) are officially “decontrolled”, companies receive a fixed subsidy per bag, subject to maintaining a capped MRP. 
    • For instance, the Centre provides a flat subsidy for DAP, but companies must sell it at a notified price to receive that subsidy.
  • Similarly, for other fertilisers such as Muriate of Potash (MOP) and NPK complexes, MRPs are indirectly regulated. Companies must align prices with subsidy rates notified by the government, and “unreasonable” profits can be recovered from subsidy claims.
  • Thus, while partial decontrol exists on paper, effective price control continues in practice.

Control Over Distribution and Movement

  • Government control is not limited to pricing. The Centre also regulates the movement and allocation of subsidised fertilisers across states.
  • The Department of Fertilisers (DoF) prepares an “agreed supply plan” based on the requirement assessed by the Union Agriculture Ministry and state governments. 
  • This plan is broken down state-wise, season-wise and month-wise.
  • At the state level, district-wise allocation is decided by the agriculture authorities. 
  • Companies must dispatch fertilisers according to official railway rake and road movement plans. 
  • Once a rake reaches a designated railhead, the district agriculture officer allocates stock dealer-wise.
  • In essence, even private fertiliser companies operate under a framework where price, quantity, location and timing of sale are largely determined by the government.

Non-Subsidised and Speciality Fertilisers

  • Apart from subsidised fertilisers, companies also sell non-subsidised speciality nutrients. These include:
    • Water-soluble fertilisers, Calcium nitrate, Zinc sulphate, Bentonite sulphur, Micronutrients and bio-stimulants
  • These products are used in high-value crops such as fruits, vegetables and sugarcane. They are typically applied in smaller quantities but offer higher nutrient efficiency.
  • Unlike subsidised fertilisers such as urea (around Rs. 5.9 per kg), speciality products can cost Rs. 60-90 per kg.
  • However, their market size is small, about 0.4 million tonnes annually, compared to 67 million tonnes of subsidised fertilisers.
  • These products are officially notified under the Fertiliser Control Order (FCO), 1985.

The Uttar Pradesh Ban

  • In January 2026, the Uttar Pradesh agriculture directorate issued an order prohibiting urea manufacturers and suppliers from selling any “gair-anudaanit” (non-subsidised) fertilisers in the state.
  • The ban applies to several major fertiliser companies, including cooperative, public and private entities.
  • Reason Behind the Ban
    • The state government acted on allegations of “tagging”, forcing farmers to buy non-subsidised products along with subsidised fertilisers. However, industry representatives argue that:
    • Both product categories are sold through the same dealer networks.
    • Cross-selling is a normal business practice.
    • The market for speciality fertilisers in UP is relatively small compared to subsidised fertilisers.

Implications of the Ban

  • Impact on Nutrient Use Efficiency
    • Speciality fertilisers are often more nutrient-efficient and environmentally sustainable. Restricting their sale may discourage balanced fertiliser use and worsen overdependence on cheap urea.
    • India already faces the problem of excessive nitrogen application due to the highly subsidised price of urea.
  • Investor Sentiment
    • The fertiliser industry operates in a capital-intensive environment. Frequent regulatory interventions can: Reduce private sector investment, Discourage innovation, Create policy uncertainty
  • Market Distortions
    • Ministry sources argue that banning established players could open space for unorganised operators selling low-quality products.
    • This may undermine quality control and farmer education.

Structural Challenges in the Fertiliser Sector

  • Overdependence on Subsidies: The fertiliser subsidy bill remains a major fiscal burden.
  • Imbalanced Nutrient Use: Artificially cheap urea leads to overuse of nitrogen relative to phosphorus and potassium.
  • Supply Constraints: Reports of urea selling above MRP have been linked to rising consumption and production constraints.
  • Policy Overreach: Layered controls on price, movement and sales restrict market flexibility.

Way Forward

  • Gradual rationalisation of fertiliser subsidies.
  • Promotion of balanced nutrient application under schemes like Soil Health Cards.
  • Encouragement of speciality and efficiency-enhancing fertilisers.
  • Clear and predictable regulatory framework to attract investment.
  • The fertiliser sector is central to India’s food security. However, excessive controls may hinder innovation, efficiency and long-term sustainability.

Source: IE

Fertiliser Industry FAQs

Q1: What is the MRP of urea in India?

Ans: Urea is sold at a fixed MRP of Rs. 266.5 per 45-kg bag.

Q2: Are DAP and other fertilisers fully decontrolled?

Ans: No, although technically decontrolled, their prices remain linked to subsidy conditions.

Q3: What are non-subsidised fertilisers?

Ans: They are specialty nutrients such as water-soluble fertilisers and micronutrients sold at market-determined prices.

Q4: Why did Uttar Pradesh ban non-subsidised fertilisers?

Ans: The ban was issued over allegations of “tagging” of non-subsidised products with subsidised fertilisers.

Q5: What is a key concern regarding excessive controls in the fertiliser sector?

Ans: Overregulation may discourage innovation, distort markets and weaken nutrient use efficiency.

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