India AI Impact Summit 2026

ai-literacy-building-skills-inclusion-and-global-leadership

4 min read

TRACK:Human Capital
SPEAKERS
  • Lakshmi Misra, The Garage Noida
  • Prabhat Kumar, AIforAll Global
  • Sourabh Choudhary, AIforAll Global
SESSION OVERVIEW

This session addresses the critical AI literacy gap limiting equitable access to AI knowledge across sectors and geographies. It will explore strategies to embed AI education across formal learning, workforce upskilling, and lifelong learning through training multipliers like teachers, government officials, and workforce development leaders. It will introduce the concept of the Universal AI Literacy Stack, and examine how multi-stakeholder collaboration among governments, industry, academia, and global partners can position countries as AI literacy leaders.

VIDEO RECORDING

AI Literacy: Building Skills, Inclusion, and Global Leadership

Detailed Summary

Economic Imperative

  • McKinsey projects that AI will generate ≈ $15 trillion in global GDP over the next five years.
  • India’s share could be ≈ $1 trillion, roughly 25 % of India’s current GDP – a conservative estimate.

Sectoral Access Gap

  • Current talent pipelines favor elite institutions; the “skill pathway” is slow for the broader population.
  • AI adoption must circumvent this bottleneck to avoid a widening productivity gap.

India’s Comparative Advantages

AdvantageExplanation
Demographic dividend> 50 % of Indians are under 35, a cohort that readily adopts new skills.
Frugal (necessity‑driven) innovationIndia excels at building low‑resource, edge‑computing AI solutions that do not rely on massive data‑centers.
Diversity of dataVast, heterogeneous population provides rich training data for AI models.

Risks of Inaction

  • AI‑productivity divide – without widespread literacy, only a privileged few capture AI‑driven efficiency.
  • Misinformation & harmful use – unchecked AI can destabilise societies, spread falsehoods, and undermine trust.
  • Missed opportunity – the chance to democratise elite‑level intelligence would be lost, hampering India’s global leadership.

Transition – Prabhat hands the floor to Lakshmi Mishra to spell out what AI literacy actually entails.

2. Defining AI Literacy & Introducing a Universal Framework – Lakshmi Mishra

Core Definition
AI literacy = technical knowledge + durable skill + future‑ready attitude (an “AI‑first mindset”). It is required for every citizen—both users and creators.

Why a Framework, Not a One‑Off Course

  • AI represents a fundamental shift; learning must be continuous, role‑based, and adaptable.
  • Traditional, tool‑centric, English‑only courses are insufficient.

The Universal AI Literacy Framework (derived from OECD & EU research)

DimensionWhat It Covers
Domains (Four Pillars)Engage, Create, Manage, Design – from awareness to technical leadership.
CompetenciesSpecific capabilities within each domain (e.g., AI awareness, prompting, governance).
PersonasLearner archetypes (citizens, business leaders, policy makers, technical professionals).
Proficiency LevelsAware → Fluent – progressive depth of mastery.

2.1. The Four Pillars

  1. Engage – Understanding AI’s presence, reliability, bias, privacy, societal impact.
  2. Create – Prompt engineering, applying AI to domain‑specific tasks, validating outputs, embedding AI into workflows.
  3. Manage – Governance, policy, risk mitigation, change‑management, aligning AI with organisational goals.
  4. Design – Deep technical design: data collection, preprocessing, model selection, training, evaluation, deployment, monitoring.

Illustrative Analogy – UPI’s rapid, nation‑wide adoption shows how a foundational service can become universal; AI literacy can follow the same trajectory.

Evidence of the Gap

  • India ranks #1 globally for Gen‑AI courses on Coursera, yet #89/109 for AI‑skill proficiency – a stark mismatch between enrollment and actual ability.

Personalisation Process

  1. Identify learner persona (e.g., K‑12 student, health‑care worker, policy maker).
  2. Map to primary domain(s) (Engage, Create, etc.).
  3. Select relevant competencies for each domain.
  4. Guide the learner through proficiency levels until fluency is reached.

3. Scaling AI Literacy to a Billion People – Sourabh Choudhary

Scale‑by‑Force‑Multipliers

  • Teachers (~10 million) are the primary “torch‑bearers”.
  • Government officials, corporate workforce, CSR/non‑profit entities, and local‑language creators act as secondary multipliers.

Principles for Effective Scaling

PrincipleOperational Meaning
BrevityCourses/modules must be short, fitting busy schedules.
Role‑BasedContent is tailored to specific job functions (e.g., IT engineer vs. mango farmer).
Local LanguageDelivery in regional languages to maximize reach and comprehension.
AI‑Assisted TrainingUse AI tools to generate content, assess learners, and provide feedback.
Judgment Over ToolsEmphasise critical decision‑making, not just tool operation.

Roadmap (7‑Year Vision, Achievable in 3‑5 Years with Focus)

  1. Foundation – Train‑the‑Trainers (teachers, senior officials).
  2. Acceleration – Rapid Deployment through digital platforms, leveraging AI for content creation.
  3. Saturation – Continuous Refresh as AI evolves; the process never truly ends.

Pitfalls & Mitigations

  • Waiting for a “perfect” curriculum stalls progress; iterative, feedback‑driven design is essential.
  • Over‑reliance on certifications – shift to AI‑driven competency evaluation rather than paper credentials.

Call to Action

  • Identify and empower “force multipliers.”
  • Launch concise, role‑specific, multilingual modules today.
  • Deploy AI‑based assessment tools to ensure quality over quantity.
  • Iterate fast – gather data, refine curricula, repeat.

Key Takeaways

  • AI will add ~$1 trillion to India’s GDP, making AI literacy a national economic priority.
  • India’s youthful demographics, frugal innovation culture, and data diversity give it a unique advantage to become the world’s most AI‑literate society.
  • AI literacy is not a single course; it is a continuous, role‑based learning journey built on the four pillars – Engage, Create, Manage, Design.
  • A major skill‑proficiency gap exists despite high enrollment in AI courses; the framework bridges this by aligning competencies with personas and proficiency levels.
  • Scaling must start with force multipliers (teachers, officials, corporate leaders) and rely on short, localized, AI‑assisted modules.
  • Quality, judgment, and continuous feedback outweigh certifications and static curricula.
  • Risks of neglect include a widening productivity divide, misinformation, and missed opportunities for inclusive growth.
  • Immediate next steps: launch pilot teacher‑training programs, develop role‑specific micro‑modules in regional languages, and embed AI‑driven assessment dashboards.

See Also:

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