Omkar Sehlar
The education system in India though enjoying near universal enrollment, suffers a crisis in the foundation of learning. Even in early grades, large cohorts of children are failing to learn the rudiments of literacy and numeracy, a fact that is weakening the prospects of long-term educational equity and economic development. To help solve this pressing issue, the Government of India has undertaken the NIPUN Bharat Mission in July 2021 under the Samagra Shiksha Abhiyan, and the aim of this initiative is to provide universal foundational literacy and numeracy (FLN) to all children by Grade 3 by 202627. Although the mission has recorded progress in at least some of the states, the targets are still overwhelming in the vast social, economic, and demographic context of the country of India. This blog discusses the capital of inter-state differences in FLN, the role of poverty, health and social infrastructure, and the role of AI-empowered strategies in sealing these gaps.
The Scope of the Challenge
Prior to the pandemic, India had a learning poverty level of 56.1 which is the proportion of ten-year-olds who cannot read a simple text. This crisis was compounded by the COVID-19 pandemic, as surveys have reported over 90 percent of students lost at least one specific language or cognitive skill. The ASER 2022 survey brought to light that in rural India only one out of five, Grade 3 students were able to read a Class 2-level text. The crisis is further exacerbated by teacher shortages, archaic training methods, lack of proper infrastructure and socioeconomic limitations. The National Education Policy (NEP) 2020 of India has provided a roadmap of change, with its recommendation of competency-based, technology-oriented, and individualized learning. It focuses on the use of artificial intelligence (AI) to enhance equity and outcomes and prioritizes ethical protection. However, the implementation process has not been uniform among states, and has shown some systemic bottlenecks.
Early Successes of NIPUN Bharat.
Nevertheless, the mission has had certain successes: The improvement in reading skills and subtraction skills showed a 12-percentage point and 14-point gain respectively in Uttar Pradesh in the years between 2022 and 2024. In the mission of Ennum Ezhuthum in Tamil Nadu, engagement and localised pedagogy were enhanced, with Grade 3 proficiency in reading and addition of two-digits improving. In the district of Pune, interventions that were targeted increased the proportion of students proceeding to high-level achievements by 8%. There has been growth in teacher training: Bihar has reached 1.8 lakh teachers with the help of micro-learning modules, and Assam was training almost 1.2 lakh teachers. Services such as DIKSHA started special FLN verticals, which increased the content availability. In 2015-16 to 2019-21, an average of 135 million individuals have left the ranks of poverty, alleviating some background barriers to education.
Persistent Barriers
However, there are a number of severe obstacles that continue to hamper the achievement of universal FLN.
1. Infrastructural gaps: A large number of classrooms experience high student to teacher ratios (4060 students in one teacher), low levels of digital preparedness, and even lack of basic infrastructure.
2. Inadequate funding: India ends up spending only 3.1% of its GDP on education which is significantly lower than the 6 percent that the Kothari Commission suggests and NEP 2020 restates.
3. Weak monitoring systems: Instruments such as UDISE+ are usually based on heavy reliance on quantitative reporting and they do not provide pertinent qualitative information of the teachers and students.
4. Gaps in teaching: Each year between 10 and 15 per cent of teaching positions are unfilled, and much of teacher training does not reflect the science that underlies early literacy acquisition.
5. Social barriers: Poverty, caste, gender inequality, and geographic isolation have a disproportional impact on learning outcomes. To most kids, going to school means having access to midday meals and not education.
6. Diversity in language: There are 22 official languages, and thousands of dialects of the language, which is why standardized textbook-based methods can hardly address the needs of the learners, particularly when the language used is not the mother tongue.
The other key determinant is health. Malnutrition, stunting, and anemia are still rife, and have a direct effect on the cognitive capacity of children. Nutrition indicators explain more than a quarter of MPI (Multidimensional Poverty Index) deprivation in some states.
Inter-State Variations
The heterogeneity of the regions in India is one of the biggest challenges. The NIPUN Bharat Mission is carried out in the same way in principle, although the results differ significantly across states.
1. Economic inequality: Bihar and Jharkhand have some of the worst MPI scores and worst FLN results, and Kerala is doing well because of good health and educational systems.
2. Access and governance: Gujarat and Rajasthan are weak in access metrics, but Gujarat has made investments in Command and Control Centers based on AI, which monitors schools.
3. Health and nutrition: Bihar, Jharkhand and Uttar Pradesh have pitiable health scores in the health pillar of the FLN Index with Puducherry and Kerala having the highest scores.
4. Demographics: States with high numbers of children like Uttar Pradesh, Bihar and Madhya Pradesh find it more difficult than small states with high capacity like Kerala or Himachal Pradesh.
5. Policy orientation: The language sensitive, decentralized policy of Tamil Nadu is an antithesis of the previously inflexible centralized approach of Uttar Pradesh. Punjab, with its focused programs of state action, also shows the value of policy flexibility.
These inequalities highlight the necessity of varying approaches to the domestic context.
Learning by Global Experiences.
The insights that India can learn can be found in other countries:
1. Finland: Focuses on ethical AI, privacy and national AI literacy, making it a part of the curriculum at the teacher and student level.
2. Brazil: Frugal AI-based projects, like Letrus and Geekie One are used to personalize learning in schools with resource limitations, with a focus on human-AI collaborative models.
3. South Africa: AI-based predictive analytics is being used to predict struggling students and maximize resource allocation, and there is a very strong focus on low-bandwidth, mobile-first solutions.
4. Vietnam: Demonstrated the ability to scale low-cost EdTech platforms like ReadToMe even to infrastructure-poor areas, demonstrating scalability in areas comparable to India.
Their similarity is in the fact that all are using inclusive AI, are not lavish, and communicate in local languages.
The AI-Enabling Digital Infrastructure.
AI has the potential to be transformative in a number of areas:
1. Predictive analytics: by monitoring students in real-time, predictive analytics can identify those at risk and intervene early enough to prevent dropouts or failure.
2. Adaptive learning applications: AI-driven applications are able to customize the content, modify learning speed, reinforce weak points, and facilitate multilingual learning. In Andhra Pradesh and Maharashtra, pilot projects have demonstrated between 20-percent improvement in test scores.
3. Language and accessibility: Natural Language Processing (NLP)-based applications such as the Read Along of Google can assist early learners in local languages. Language obstacles can be broken by using AI-driven translation and speech-to-text applications.
4. Assessment and feedback: Auto grading, customized progress dashboards, and feedback assessment tools can conserve time on the part of teachers and enhance responsiveness.
5. Inclusive education: AI-based assistive technologies enable children with disabilities by assisting them in speech recognition, emotion detection, and real-time enhancements of accessibility.
Gujarat has already made investments in advanced data-driven monitoring centers; these state-level centers may be a model nationwide with an ethical state government.
Ethical Concerns and Risks
The threat of AI is a reality that needs to be controlled:
1. Data privacy: There is a high need to protect the sensitive academic and behavioral data of children, promote clear consent norms, anonymization, and stronger legal protection.
2. Algorithmic bias: The existence of inequities can be sustained by poorly-designed systems when datasets underrepresent those groups of people who are marginalized.
3. Digital divide: As few as 25% of households are connected to the internet (2019), the implementation of AI can exacerbate inequality unless solutions are developed with low-cost and mobile-first systems in mind with low-cost and offline compatibility.
Therefore, in education, AI governance should be focused on equity, fairness, and accountability, as well as efficiency.
Policy Recommendations
To achieve success by 2026-27 with the mission, a number of priorities can be identified:
1. Enhance infrastructure: Invest in low-cost devices, universal internet penetration, and low bandwidth AI solutions to rural regions.
2. Facilitate state autonomy: Promote local customization of pedagogy and learning resources, as was done in Tamil Nadu by decentralization.
3. Empower teachers: Increase to lifelong mentorship and peer learning systems, including AI literacy training in the teacher education.
4. Make AI ethically governable: Implement ethnic India-specific data privacy, transparency and bi-annual bias audit regulations.
5. Invest in education: Invest a minimum of 6 percent of GDP in education, which will cover teacher recruiting, school facilities and oversight.
6. Make use of multi-stakeholder collaborations: Greater public-private partnerships (PPPs) and an AI Innovation Sandbox can guarantee scalable and context-related digital tools.
Conclusion
The NIPUN Bharat Mission is an initiative of the Indian government to address the root cause of the learning crisis in the country. To meet its goals, however, it must overcome profound structural obstacles–poverty and malnutrition, but also inflexible centralized implementation patterns and digital gaps. The secret of success is regional flexibility, the empowerment of teachers, and ethical and inclusive applications of AI-based monitoring systems. Experience in Tamil Nadu and other international experiences such as Brazil and South Africa suggests that low-cost, adaptive, community-sensitive, AI-based designs can have a stunning impact, even in resource-poor environments. NIPUN Bharat can not only achieve universal foundational literacy and numeracy, but also deliver huge socioeconomic payoffs to India, including intensifying productivity, providing equity, and turning education into a truly democratizing influence, should it be implemented in a hurry and inclusively.
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Omkar Shelar is pursuing Master’s in Public Policy and Governance from Tata Institute of Social Sciences, Hyderabad
Disclaimer: All views expressed in the article belong to the author and not necessarily to the organisation.
Acknowledgement: This article was posted by Aashvee Prisha, a research intern at IMPRI.
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