India's ambition of becoming a global economic powerhouse rests not only on innovation, manufacturing or digital transformation, but also on a far more fundamental building block—its children's ability to read, write and understand numbers. As conversations around human capital increasingly dominate policy and corporate boardrooms, Foundational Literacy and Numeracy (FLN) is emerging as a strategic national priority that extends well beyond the education sector. The challenge is no longer merely about school enrolment; it is about ensuring that every child acquires the foundational skills needed to thrive in an increasingly knowledge-driven economy.
In this exclusive interview with TheCSRUniverse, Ms. Sangita Dandapat, Project Head – Foundational Literacy & Numeracy, Kotak Education Foundation, argues that FLN should be viewed as India's first layer of human capital infrastructure rather than a conventional social development programme. She explains why weak foundational learning carries significant long-term economic costs, how CSR investments can be better aligned with workforce development, and what policy and funding shifts are required to build scalable, sustainable learning ecosystems.
Drawing from global evidence, national assessments and Kotak Education Foundation's classroom innovations such as the Stations Model, she offers valuable insights into how strengthening early learning today can shape a more skilled, productive and equitable India tomorrow. For policymakers, corporate leaders, CSR professionals and education practitioners alike, this conversation presents a compelling case for reimagining FLN as an investment in India's future competitiveness rather than simply an educational intervention.
Read on for the full interview.
Q. How should FLN be reframed as a national economic priority rather than a social sector issue?
A. FLN should be reframed as the first layer of India’s human capital infrastructure. It is often treated as an early education or social development concern, but its implications go far beyond the classroom. FLN should be reframed as the earliest point at which India begins building its future workforce. Economic discussions often begin too late, at the stage of higher education, skilling, employability training, or industry readiness. But the ability to learn, adapt, communicate, solve problems, and work with information is built much earlier.
The World Bank describes reading as a foundational skill and a gateway to other learning outcomes, including science, mathematics, humanities, creative thinking and computational skills. This means that foundational learning is not only about early-grade education; it is the base on which later technical, vocational and higher-order skills are built.
The World Bank also argues that learning poverty is economically unacceptable because, without human capital accumulation, countries cannot thrive in the global economy. It notes that in richer countries, 70% of wealth is derived from accumulated human capital, compared to 40% in poorer countries. This makes FLN not just a welfare issue, but a long-term growth issue.
The FLN report also makes this connection clearly. It states that basic reading, writing and arithmetic provide the foundation for higher-order thinking, logical reasoning, understanding different perspectives and contributing to the economy after school. In that sense, FLN is the precondition for skilling. Without it, later investments in skill development risk becoming remedial rather than transformative.
This is why FLN must be viewed as economic infrastructure. Roads connect people to markets, digital infrastructure connects people to information, and foundational learning connects children to future education, work and citizenship. If the base is weak, later investments in skilling become corrective rather than developmental.
This reframing is also central to India’s progress on SDG 4. SDG 4 is not only about children being enrolled in school; it is about inclusive and equitable quality education. The State of Global Learning Poverty 2022 report warns that high learning poverty is an early signal that education systems are not on track to achieve SDG 4 by 2030. In that sense, FLN is not a welfare issue alone. It is a national development issue linked to education quality, labour productivity and long-term economic competitiveness.
Q. What are the long-term economic costs of weak foundational literacy in India?
A. Weak foundational literacy creates long-term economic costs because it weakens the pathway from schooling to employability. When children do not learn to read with understanding in the early years, they struggle to access the curriculum in later grades. This affects not only language learning, but also mathematics, science, social studies, digital learning and eventually vocational or skill-based education.
The World Bank defines this as learning poverty, that is, the inability of children to read and understand a simple text by age 10. It notes that children who do not master reading by this age find it difficult to catch up later, are less likely to continue learning in higher grades, and may even be at greater risk of leaving school early.
For India, this becomes a serious economic concern. The State of Global Learning Poverty 2022 report records India’s learning poverty at 56.1%, based on 2017 National Learning Assessment data. This includes 53.7% learning deprivation and 5.1% schooling deprivation. In simple terms, the larger challenge is not only that some children are out of school, but that many children who are in school are still not acquiring minimum reading proficiency.
The latest PARAKH Rashtriya Sarvekshan 2024, a competency-based national assessment, further shows how learning gaps become sharper as children move to higher grades. At the Grade 3 level, the national average was 64% in Language and 60% in Mathematics. By Grade 6, this declined to 57% in Language and 46% in Mathematics. By Grade 9, it further dropped to 54% in Language and 37% in Mathematics. This pattern shows that gaps in foundational literacy and numeracy do not automatically get corrected over time; in many cases, they become deeper as curriculum complexity increases.
The FLN report also notes that according to a World Bank assessment, around 50% of children in India lack foundational learning, and by the time they reach Grade 5, it becomes difficult for them to grasp grade-level teaching. It further reports that during the pandemic, 92% of students in India lost at least one specific language ability and 82% lost at least one specific mathematical ability.
The cost of this is not limited to lower test scores. Weak foundational literacy can lead to grade repetition, dropout, low confidence, poor comprehension, and limited ability to benefit from later investments in skilling or workforce development. The FLN report also links weak early learning with long-term outcomes such as poor health, youth unemployment and poverty.
At a macro level, weak literacy affects labour productivity and competitiveness. The World Bank argues that learning poverty is economically unacceptable because countries cannot thrive in the global economy without accumulating human capital. It also notes that in richer countries, 70% of wealth is derived from accumulated human capital, compared to 40% in poorer countries.
The State of Global Learning Poverty 2022 report makes the economic risk even clearer. It states that lost foundational learning translates into lower adult skills, which in turn reduces productivity and earnings once today’s children enter the workforce. Without action, the current generation of students risks losing $21 trillion in lifetime earnings, equivalent to 16% of today’s global GDP.
So, the long-term economic cost of weak foundational literacy is that India risks having a large young population that is enrolled, certified and even trained, but not fully equipped to learn, adapt, solve problems or participate productively in the economy. In that sense, the demographic dividend depends not only on how many young people India has, but on how well they learn in the earliest years.
Q. How can CSR investments in FLN be aligned with workforce development goals?
A. CSR investments in FLN need to move beyond viewing education as a philanthropic activity and begin treating it as long-term capability building. If workforce development is the goal, then investments cannot start only at the stage of skilling, employability training or higher education. They must begin much earlier, when children are developing comprehension, communication, reasoning, problem solving and learning habits.
One important shift is moving from short-term, visibility-driven interventions to sustained system support. In many cases, CSR funding in education focuses on distributing devices, creating infrastructure, or introducing standalone innovations. While these may have value, foundational learning outcomes improve when investments strengthen everyday classroom processes, teacher support, multilingual learning materials, assessment systems, early-grade pedagogy, school leadership and community engagement.
CSR can play a particularly important role in supporting implementation quality. Large-scale policies such as NEP 2020 and NIPUN Bharat already provide national direction. The challenge is often not policy intent, but how consistently high-quality support reaches classrooms. CSR partnerships can help bridge this gap by supporting evidence-backed models, implementation research, teacher mentoring systems, and low-cost scalable instructional practices.
Another important area is investing in context-responsive innovation rather than trend-driven innovation. For example, AI and digital tools can support FLN if they help teachers identify learning gaps, reduce administrative workload, generate multilingual practice material, or improve formative assessment. But technology should not be introduced simply because it is new or marketable. The starting point should always be: What problem faced by the teacher or child is this solving?
CSR investments should also align with workforce development by focusing on continuity across stages. Foundational learning, school education, skilling and employability should not be treated as disconnected sectors. A child who develops strong comprehension, communication and numeracy skills early is more likely to adapt to later vocational training, digital systems and changing labour-market demands.
CSR can contribute by strengthening ecosystems rather than running isolated projects/ creating parallel systems. This includes supporting district-level academic structures, local-language content development, public-private partnerships, implementation data systems and longitudinal learning studies. Sustainable workforce development depends not only on producing skilled individuals, but on building systems where learning quality improves consistently over time.
Q. What makes a literacy model scalable enough to qualify as “infrastructure”?
A. A literacy model becomes scalable enough to qualify as infrastructure when it is not dependent on one organisation, one trainer, one geography, or one short-term project cycle. It must be simple enough to be adopted widely, flexible enough to work across diverse contexts, and strong enough to produce measurable learning improvement.
The first requirement is clarity of learning goals. A scalable literacy model should clearly define what children are expected to achieve, for example, oral language development, print awareness, vocabulary, decoding, fluency, comprehension, writing readiness and meaning-making. Without this clarity, scale often becomes activity expansion without learning depth.
The second requirement is teacher usability. A model may look strong on paper, but if teachers cannot use it in real classrooms with mixed learning levels, multilingual children, limited time and varying resources, it will not scale meaningfully. The model should offer simple routines, classroom examples, low-cost materials, formative assessment tools and differentiated strategies.
The third requirement is contextual adaptability. India’s classrooms are linguistically and socially diverse. A literacy model must work across home languages, school languages, regional contexts and different levels of print exposure. The FLN report highlights that language is a critical determinant of foundational learning and that 40% children learn literacy and numeracy in languages different from their home language. Therefore, a scalable model cannot be rigid or language-neutral; it must actively respond to children’s linguistic realities.
The fourth requirement is integration with public systems. A model qualifies as infrastructure only when it can be embedded into teacher training, classroom routines, curriculum, assessment, academic mentoring, school leadership and district-level monitoring. It should not remain an external “project” that disappears when funding/project ends.
The fifth requirement is measurement and feedback. A strong literacy model should include simple ways to track whether children are progressing, not only through formal tests, but also through observation of reading, retelling, vocabulary use, comprehension, oral expression and confidence. Data should be used to improve teaching, not merely to report numbers.
Finally, infrastructure requires durability. A scalable literacy model must be affordable, evidence-backed, easy to train on, adaptable across states, and capable of being sustained by teachers, schools and government systems over time.
Q. How can classroom models such as KEF’s Stations Model contribute to faster, large-scale human capital development?
A. From KEF’s perspective, faster human capital development in foundational education requires models that are not only pedagogically sound, but also practical for teachers to use in real classrooms. One such approach is the Stations Model, where a classroom session is divided into multiple learning stations. These stations allow children to engage with literacy, numeracy, oral language, stories, manipulatives, peer learning, skill practice, and homework through structured activities.
The strength of the Stations Model is that it responds to a core challenge in FLN: children in the same classroom are often at different learning levels. A single whole-class method may not support all children equally. Through stations, teachers can create differentiated numeracy opportunities. For example, some children may work on counting objects with one-to-one correspondence, some may compare quantities using concrete materials, some may group objects to understand place value, while others may practise simple operations through manipulatives. This makes the classroom more responsive to children’s actual learning needs.
The model also supports foundational skills beyond basic reading and numeracy. Children learn to communicate, listen, collaborate, solve problems, use materials, explain their thinking, and participate with confidence. These are essential building blocks for 21st-century skills and future workforce readiness.
For large-scale human capital development, the value of such a model lies in its classroom usability. It does not depend only on technology or one-time training. It can be implemented through teacher training, classroom routines, simple materials, structured lesson plans, and ongoing mentoring. This makes it easier to adapt across schools and contexts.
In that sense, the Stations Model contributes to human capital development by turning FLN from a policy goal into an everyday classroom practice. It helps children build the learning behaviours and foundational competencies they need to benefit from later schooling, skilling, and lifelong learning.
Q. What kind of policy or funding shifts are needed to treat FLN as infrastructure?
A. To treat FLN as infrastructure, policy and funding need to move from short-term programme delivery to long-term system building. Infrastructure is not created through one-time activities; it requires sustained investment, clear standards, maintenance systems, monitoring and institutional ownership. FLN should be approached in the same way.
The first shift needed is from annual project funding to multi-year investment. Foundational learning cannot be improved through short funding cycles focused only on training numbers or material distribution. It requires continuous teacher support, classroom mentoring, child-level assessment, local language resources and academic leadership over several years.
The second shift is from input-based funding to learning-focused funding. Budgets often track how many teachers were trained, how many materials were distributed or how many sessions were conducted. These are important, but they are not enough. Funding should also track whether children are progressing in oral language, reading fluency, comprehension, writing readiness, number sense and problem-solving.
The third shift is focused investment in teacher support systems, not just teacher training. Teachers need ongoing mentoring, demonstration, peer learning, classroom observation and feedback. If teachers are expected to deliver FLN differently, the system must support them differently.
The fourth shift is stronger funding for language-responsive and context-sensitive learning materials. India’s classrooms are multilingual, and children often enter school with home languages different from the language of textbooks. Treating FLN as infrastructure means investing in local-language resources, culturally familiar content and materials that help children move from oral language to reading and writing with meaning.
The fifth shift is to invest in learning data that actually returns to the classroom. The State of Global Learning Poverty 2022 report points to the need to assess learning levels regularly and provide teachers with formative assessment tools they can use in classrooms. Data should not remain only at dashboard level; it should help teachers decide what to do next.
The sixth shift is around technology. Digital tools and AI should be funded only when they solve clearly identified problems such as reducing teacher workload, supporting differentiated practice, generating local-language resources, or helping track learning gaps. Technology should strengthen the FLN system, not become a parallel intervention.
Finally, FLN funding should explicitly support SDG 4 goals. If children are enrolled but not learning, India may show progress on access while still falling short on quality education. The World Bank warns that high learning poverty and slow progress are early signals that SDG 4 education targets are at risk. Therefore, governments, CSR partners, philanthropies and implementation organisations need to align around common FLN goals. Fragmented projects cannot build infrastructure. Long-term funding should support scalable public systems: teacher capacity, academic mentoring, assessment, leadership, community engagement and implementation research.
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