Daily Editorial Analysis 24 August 2026

Not ‘Delulu’ — An AI Tutor for Every Child in India 

Context

• India has a vast pool of academically capable students, but access to quality test preparation remains unequal.
• The test-preparation market is estimated at $14.8 billion in FY26 and may reach $23-26 billion by FY30, with nearly 2,00,000 coaching centres.
• Around 17–20 million students in Classes 9-12 participate in private coaching, imposing a substantial financial burden on families.
• The central challenge is not a shortage of educational material but unequal access to quality instruction, personalised mentoring, doubt-solving and credible assessment.
• Artificial Intelligence (AI), combined with Digital Public Infrastructure (DPI), can make high-quality preparation accessible without replacing private coaching.

The Need for Better Learning Outcomes

• The goal should not be to eliminate the private coaching industry but to ensure that students unable to afford expensive coaching receive comparable opportunities.
• A free AI-powered coach and tutor could provide personalised academic support, especially to students dependent on costly or poor-quality coaching.
• No single private platform can serve India’s enormous student population. Therefore, the solution should be a platform-and-network model rather than a vendor contract.
• The government should become the market-maker, infrastructure provider and trust layer, while private companies, teachers and institutions compete to provide content and mentoring.

Public Infrastructure, Private Innovation

• India’s experience with UPI and ONDC demonstrates how open digital networks can challenge closed platforms.
• Education can adopt a similar model. The government should provide identity, discovery, payments, credentialing, standards and interoperability, while private providers supply lessons, tests, doubt-solving and mentoring.
• Traditional coaching bundles content, mentoring, peer learning, discipline and quality signalling.
• Digital infrastructure can unbundle these services, reducing the cost of content and AI-based doubt-solving while allowing students to choose specialised services.
• An education-focused not-for-profit institution, potentially modelled on NPCI, could steward the network and establish common standards.

Begin with JEE and NEET

• The initiative should initially focus on JEE and NEET, India’s major competitive entrance examinations.
• Their standardised syllabi, objective questions and machine-gradable assessments make them particularly suitable for an AI-enabled preparation network.
• The government need not create all content itself. High-quality material already exists across digital platforms.
• What is scarce is trusted discovery, personalised learning, reliable assessment and effective doubt-solving.
• An open content registry could organise lessons, problem sets and mock examinations according to a common JEE/NEET taxonomy.
• Educational companies and individual teachers could contribute material and compete on mentoring and specialised services.

Interoperability and Student Ownership

• The foundation of the network should be content and data portability. Students must be able to change providers without losing their learning history.
• A government reference application with open APIs could allow DIKSHA, State education applications and other platforms to access approved resources.
• An AI recommendation engine could identify weaknesses and direct students to the best available module, irrespective of its provider.
• A student-owned learning wallet and progress ledger could record lessons, mock scores and topic-wise mastery.
• Aadhaar/DigiLocker could support authentication, while APAAR could connect learning records with academic credentials, subject to appropriate consent and privacy safeguards.

Ensuring Equity and Accountability

• The system must reach students without smartphones or reliable internet.
• Data-light streaming, downloadable content, school computer laboratories and Common Service Centres can extend access to disadvantaged communities.
• The government should publish provider- and module-level outcome data, enabling students to judge resources through actual performance rather than advertising.
• Independent accreditation should verify content quality and prevent misleading claims. A strict no-lock-in rule should guarantee portability of student data.
• Basic preparation should remain free, while premium services such as live mentoring and graded assignments could be offered through affordable payments or government-funded vouchers.

AI as the Personal Tutor

• AI’s greatest value lies in personalised practice, instant doubt-solving and adaptive learning.
• Affordable AI models can provide continuous academic assistance at a fraction of the cost of conventional tutoring.
• AI can identify weaknesses, generate targeted questions, explain errors and adjust difficulty according to individual performance.
• Human teachers would continue to provide advanced mentoring, motivation and complex conceptual guidance, while AI could serve as an always-available first layer of academic support.
• The government should enable AI tutors rather than build one itself.
• Its responsibility should be to create standards, infrastructure, interoperability, accreditation and universal access.

Conclusion

• India’s coaching economy reflects enormous demand for examination preparation but also a serious access and affordability gap.
• The solution is neither government-controlled coaching nor displacement of private providers, but an open education network built on public digital infrastructure.
• By aggregating demand, establishing standards, enabling interoperable content, protecting student-owned data and deploying AI for personalised tutoring, India can transform test preparation from an expensive paywall into a free, competitive and high-quality digital public service.

Not ‘Delulu’ — An AI Tutor for Every Child in India FAQs

Q1. What is the main problem with India’s coaching system?
Ans. India’s coaching system suffers from high costs and unequal access to quality preparation.

Q2. Why should the government create an open education network?
Ans. The government should create an open network to provide affordable, high-quality preparation to every student.

Q3. Why should JEE and NEET be the starting point?
Ans. JEE and NEET are suitable because they have standardised syllabi and objective, machine-gradable examinations.

Q4. How can AI improve test preparation?
Ans. AI can provide personalised practice, instant doubt-solving and adaptive learning.

Q5. What should be the government’s role?
Ans. The government should build digital infrastructure, ensure interoperability and regulate quality rather than become the teacher.

Source: The Hindu

A Civilisational Approach to Social Media 

Context

• The debate over banning social media for minors has become a major policy issue in the digital age.
• Countries such as Australia and the U.K. are introducing or considering restrictions, while the U.S. follows varied approaches.
• India faces a similar choice. Concerns about addiction, cyberbullying, manipulation, harmful content and privacy risks are genuine, but bans may not provide a lasting solution.
• The larger challenge is preparing young people for a world increasingly shaped by social media, artificial intelligence and digital technologies.

Why Fear is Not the Answer

• Lessons from Earlier Communication Revolutions

  • Every major communication revolution has generated anxiety.
  • The printing press, radio, television and the Internet were once viewed as threats to established social structures.
  • Yet societies gradually learned that the objective should not be to prevent information from flowing, but to promote its responsible use.

• The Digital Transformation

  • The smartphone has combined the functions of a printing press, library, classroom, marketplace and public square.
  • Social media and AI are accelerating this transformation. Young people are growing up amid technological disruption, economic uncertainty, geopolitical tensions and social instability, making fear a powerful influence on policy.

• Limits of Technological Restrictions

  • Fear should not become the principal basis for regulation. Protecting children is essential, but child safety and technological innovation need not be opposing goals.
  • Governments should create safeguards without discouraging innovation.
  • Technological restrictions are also increasingly difficult to enforce. VPNs, encrypted applications, alternative platforms and AI-enabled tools can help users circumvent barriers.
  • Completely isolating young people from digital platforms is therefore becoming increasingly impractical.

• Beyond Social Media: The AI Challenge

  • The debate may soon extend beyond social media. Children will increasingly interact with AI systems capable of educating, entertaining, advising and influencing them.
  • The real question is not how to keep young people away from technology, but how to prepare them to use it safely, intelligently and responsibly.

• Regulation with Responsibility

  • Government regulation remains necessary, but regulation alone cannot solve the problem. Authorities should ensure platform transparency, child protection and privacy while combating online crime.
  • At the same time, families, schools and society must develop the judgment, self-control and resilience required to navigate the digital world.

The Role of Government and Regulation

• Authorities should demand greater transparency from technology platforms, establish effective child-protection standards, strengthen privacy safeguards, combat online criminal activity and encourage responsible innovation.
• However, regulation alone cannot address a problem that is simultaneously social, cultural, educational and psychological.
• Laws can establish boundaries, but they cannot create judgment, self-control or resilience. India therefore needs a broader strategy combining sensible regulation with strong human institutions.

Drawing from One’s Roots

• India has a long tradition of transmitting knowledge, values and responsibility across generations. Families, educational institutions and cultural traditions historically helped young people navigate uncertainty.
• The guru-shishya tradition emphasised not merely knowledge but also character, discipline, responsibility and ethical development.
• These principles can be adapted to the digital age. Parents should become more digitally literate and actively engage with their children’s online lives.
• Schools should teach digital citizenship, critical thinking, ethical reasoning, online safety and resilience, alongside technical skills.
• Religious and cultural organisations can contribute by engaging constructively with technology rather than retreating from it.
• Technology companies, educators, parents and civil society should cooperate to promote responsible digital behaviour.

Building Digital Resilience

• The ultimate objective should be to develop young people capable of navigating technology responsibly rather than simply avoiding it.
• Children must learn to identify manipulation, evaluate information, protect privacy, manage screen use and respond appropriately to harmful online experiences.
• India’s ability to absorb external influences while preserving its cultural core offers an important advantage.
• As a rapidly expanding digital society, India can demonstrate that technological progress and cultural continuity can coexist.

Conclusion

• A social-media ban for minors may appear attractive because it offers a simple response to complex dangers.
• Yet blanket restrictions cannot adequately address rapidly evolving technologies. India should pursue a balanced framework combining regulation, platform accountability, parental involvement and education.
• The goal should not be to build a larger digital firewall, but to create capable, resilient and responsible digital citizens.
• The future will favour societies that learn to govern technology while preserving human judgment, values and responsibility.

A Civilisational Approach to Social Media FAQs

Q1. Why is banning social media for minors controversial?
Ans. A ban may protect children temporarily but cannot prepare them for a technology-driven future.

Q2.  Why are technological restrictions difficult to enforce?
Ans. Technological tools such as VPNs and alternative platforms can help users bypass restrictions.

Q3. What should be the primary focus of digital education?
Ans. Digital education should develop critical thinking, ethical reasoning and responsible online behaviour.

Q4. How can India draw from its traditional values?
Ans. India can adapt the guru-shishya tradition to promote character, discipline and responsibility in the digital age.

Q5. What should India’s overall approach to social media be?
Ans. India should combine regulation, parental involvement, education and platform accountability to build responsible digital citizens.

Source: The Hindu

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Rethinking India’s Manufacturing Growth – Data, Deflators and Diverging Indicators

Context

• China’s manufacturing surge is intensifying competitive pressure on India, making it crucial to accurately assess the health, productivity and competitiveness of Indian manufacturing.
• While the government has promoted Make in India and Production-Linked Incentive (PLI) schemes, conflicting signals from manufacturing data raise three questions.
• The Ministry of Statistics and Programme Implementation (MoSPI) has undertaken substantial revisions under the new GDP methodology.
• Hence, there is the need to analyse whether the sector is actually growing as strongly as the latest national accounts suggest, as problems with the earlier manufacturing data series were widely recognised.

Q1: Why is the Manufacturing GVA Deflator Showing Negative Growth?

• The new estimates show the manufacturing Gross Value Added (GVA) deflator recording negative growth for nine consecutive quarters between 2023 and 2025.
• This is puzzling because –
  • Core CPI inflation remained positive.
  • The Wholesale Price Index (WPI) was negative only for some quarters.
  • The GVA deflator should broadly capture changes in prices relevant to manufacturing output.
• The GVA deflator is not identical to either CPI or WPI; it is intended to capture price movements associated with the value added generated by a sector.
• Therefore, divergences from consumer or wholesale inflation do not automatically imply an error, but unusually persistent divergence warrants methodological scrutiny.
• The issue is important because real GVA is obtained by deflating nominal GVA for price changes.
• If the deflator is unusually low or negative, real manufacturing growth can be significantly overstated.

Q2: Why Has Real Manufacturing GVA Diverged from IIP?

• The second concern is the growing gap between real manufacturing GVA and the Index of Industrial Production (IIP).
• Using 2022-23 as the common base (=100), by 2025-26,
  • Real manufacturing GVA was at least 15 percentage points above IIP.
  • Average manufacturing growth between 2022-23 and 2025-26 was approximately 11% according to GVA, compared with about 6% according to IIP.
• The two indicators, however, measure different things. GVA measures the economic value created after deducting intermediate consumption, while IIP is primarily an index of industrial production volumes.
• Real GVA can therefore grow faster than physical output if firms improve productivity, efficiency or the value generated from intermediate inputs.
• The informal sector is sometimes cited as an explanation because real GVA includes informal manufacturing while IIP largely captures the organised/formal sector.
• However, this explanation appears insufficient, because recent GVA estimates are increasingly benchmarked using formal-sector data.

Q3: Why Has the Correlation Between GVA and IIP Weakened?

• Before the 2011-12 methodology change, IIP and real manufacturing GVA moved broadly together, with a correlation of around 0.8.
• Afterwards, the relationship weakened substantially and appears to have become even more divergent in the latest data.
• Since September 2022, the two series have behaved very differently: real GVA has shown strong growth while IIP has remained relatively stable.
• Though this does not prove that the new GDP series is flawed, the absence of convincing explanations makes it difficult to confidently interpret the recent manufacturing revival.

Broader Implications

• The reliability of manufacturing statistics matters beyond statistical methodology.
• India’s efforts to counter Chinese manufacturing competition, deepen domestic value chains and attract investment depend on an accurate understanding of manufacturing performance.
• The debate also affects assessment of recent government interventions, including Make in India, PLI schemes and other industrial-policy measures.
• If output, value addition and productivity are sending substantially different signals, policymakers need to identify the reasons before claiming a structural manufacturing transformation.

Way Forward

• India needs greater transparency in national accounts methodology, including detailed documentation of deflators, sectoral estimates and the relationship between GVA and IIP.
• Better reconciliation of different datasets would strengthen confidence in economic policymaking.

Conclusion

• India’s manufacturing revival may be real, but the magnitude and sustainability of that revival require careful scrutiny.
• Manufacturing is central to India’s employment generation, export competitiveness, import substitution and strategic economic resilience.
• Therefore, improving the credibility and interpretability of manufacturing data is as important as improving manufacturing itself.

Rethinking India’s Manufacturing Growth FAQs

Q1. Why has the manufacturing GVA deflator raised concerns about India’s growth estimates?

Ans. Its negative growth raises questions about the measurement of price changes and the estimation of real manufacturing GVA.

Q2. Why is real manufacturing GVA growing faster than IIP?

Ans. While IIP measures physical output volumes, GVA measures value added.

Q3. What explains the divergence between real manufacturing GVA and IIP?

Ans. Differences in coverage, methodology and the treatment of the formal and informal sectors.

Q4. Why is the weakening correlation between IIP and manufacturing GVA significant?

Ans. Their growing divergence creates uncertainty about the magnitude of manufacturing growth.

Q5. How can India improve the credibility of manufacturing growth estimates?

Ans. By ensuring greater transparency in national accounts methodology, improve documentation of deflators, etc.

Source: IE