Human capital: Upskilling Tamil Nadu's workforce for AI

• Imagine an AI-ready Tamil Nadu where every graduate can read, reason and innovate. Achieving this begins with rebuilding foundational learning. Tamil Nadu’s high tertiary enrolment masks persistent learning deficits that threaten the state’s readiness for an AI-augmented labour market. Urgent, measurable reforms are required in remedial education, diagnostics, curricula, financing, and accountability to convert access into capability.

Tamil Nadu’s Gross Enrolment Ratio (GER) in tertiary education — reported at nearly 47% in the recent AISHE/CEIC series — places the state among India’s leaders in higher-education access (CEIC, 2023). Yet household-survey evidence shows foundational learning remains weak. According to ASER 2024, only 37% of Class 5 government-school children in Tamil Nadu could read a Class 2-level text, an improvement from 26% in 2022 but still below the 46% recorded in 2018 (ASER Centre, 2024). Arithmetic proficiency similarly lags, with large fractions unable to perform basic subtraction or division. These are not statistical outliers but indicators of systemic gaps that higher-education expansion alone cannot repair.

Tamil Nadu’s sharper skills paradox

The coexistence of higher-education access and uneven foundational learning is not unique to Tamil Nadu, but the contrast is sharper here. The state industrialised early, expanded colleges rapidly, and built one of India’s strongest social-welfare systems. Comparative ASER data show that states like Himachal Pradesh and Maharashtra, despite lower GER, report stronger early-grade learning outcomes, suggesting that Tamil Nadu’s challenge is not enrolment but capability formation (ASER Centre, 2024). Internationally, this resembles the “massification trap” found in East Asia, where rapid expansion of higher education without parallel improvements in foundational competencies led to graduate underemployment (Mok, 2016).

Experiences from countries like Vietnam show that durable human capital emerges from sequenced curriculum reform and teacher development, not merely broader access (World Bank, 2020). Tamil Nadu’s planners may find important parallels there: strong cultural commitment to education coexisting with gaps in foundational comprehension and mathematics that later constrain innovation-driven growth.

Why AI raises the stakes

The policy problem is structural. AI adoption increases demand for higher-order cognitive skills — critical reading, interpretation, reasoning, abstraction, data literacy and judgement — while automating routine tasks. Workers lacking these foundations risk dependence on opaque tools and exclusion from value-added roles. Economic research on technology–skill complementarities shows that returns to strong foundational skills rise as automation deepens (Acemoglu & Autor, 2011). For Tamil Nadu, this means that unless foundational competencies strengthen, the proliferation of degrees will no longer translate into productivity.

Early labour-market signals already reflect this tension. Tamil Nadu reports high tertiary participation but rising underemployment among graduates, particularly in engineering and arts. Studies on India’s workforce show that weaker cognitive skills suppress firms’ ability to adopt technology and reduce productivity gains (World Bank, 2018). Without strong foundations, Tamil Nadu’s workers may struggle to adapt to AI-enabled industries — from precision manufacturing to logistics analytics—constraining the state’s competitive advantage.

Tamil Nadu strategy: Repair, redesign

A pragmatic, evidence-based strategy for the Tamil Nadu State Planning Commission and the State Education Ministry should combine short-term remediation with medium-term system redesign.

First, mandatory first-year bridge programmes across public and private colleges must target reading comprehension, numeracy and logical reasoning, assessed through continuous diagnostics rather than single high-stakes exams. Progress metrics should feed into accreditation and funding decisions. Evidence from the US community-college system shows that structured, credit-bearing remediation improves retention and long-term outcomes when paired with sustained academic support (Bailey et al., 2015).

Second, entry diagnostics — a brief, standardised “AI-readiness” test — should triage students into remedial, blended, or direct-to-core tracks, enabling targeted resource allocation and faculty deployment. Countries such as Singapore and South Korea, which implemented system-wide diagnostics coupled with teacher training, saw measurable improvements in higher-order competencies during periods of technological acceleration (OECD, 2019).

Third, curricula must shift from content coverage to capability development. Embedding data literacy, ethical reasoning, collaborative problem solving, and AI-tool fluency into core courses is essential. Equally important is preserving mentorship and critical-evaluation skills so students learn to interrogate algorithmic outputs. Research on AI in education emphasises that learning shifts from information acquisition to meaning-making; the cognitive load moves from recalling facts to evaluating and contextualising them (Luckin, 2018).

Financial accountability

Financing and incentives are central. A Learning Infrastructure Fund, ring-fenced within the state budget and prioritised for historically disadvantaged districts and first-generation college entrants, should co-finance libraries, labs, digital devices and trained remedial faculty. Disbursements must be tied to measurable improvements in foundational indicators. Evidence from outcomes-based financing, including India’s own skills impact bonds, suggests that pay-for-performance mechanisms can align providers and employers, though safeguards such as third-party verification, phased payments and equity cushions are essential to prevent perverse incentives (Brookings, 2021).

Transparency and accountability complete the architecture. Public district-level dashboards should track school-to-college learning trajectories and employment outcomes, enabling iterative policy correction and community engagement. Independent evaluations commissioned by the State Planning Commission should prioritise cost-effectiveness and distributional impact.

Conclusion: Infra for future

Implementation risks are real: remediation requires sustained funding, faculty development and political commitment. But the alternative — large cohorts of degree-holders without foundational competencies — would weaken Tamil Nadu’s competitiveness in an AI-driven economy. Rebuilding foundational competencies is therefore a strategic investment: it protects equity, enhances productivity, and strengthens the state’s capacity to harness AI for inclusive growth.

Thakur is Professor and Dean at Vinayaka Mission’s School of Economics and Public Policy, Chennai