Building Inclusive Futures through AI Literacy for India and the Global South

Abstract

The panel explored how AI can be responsibly and inclusively woven into K‑12 education across India and the broader Global South. Participants discussed the need for foundational AI literacy for students, teachers and parents, highlighted real‑world experiences of scaling AI‑enabled learning to nearly one million learners, and examined curriculum design, ethical safeguards and the “hope‑vs‑fear” mindset that shapes adoption. The conversation also surfaced practical challenges—such as uneven technology access, misconceptions about AI, and the risk of over‑reliance on generative tools—while showcasing concrete pathways, policy alignments and community‑driven training models that aim to democratize AI education.

Detailed Summary

Bhanu Potta opened with a cascade of thanks before noting that AI Samarth (the CSF‑led AI‑literacy initiative) is already reaching ≈ 0.9 million students.
He underscored two recurring patterns: (i) ethical concerns and bias in AI applications, and (ii) the necessity of responsible engagement.
Key point: Students in government schools are eager to use AI, but they need equitable access and guided literacy—the core promise of AI Samarth.

2. Storytelling: From Curiosity to Confidence

Tanushree Narain Sharma shared the narrative of two learners, Shraddha and Poonam, illustrating a trajectory from curiosity about AI to confident, agency‑rich usage.
She highlighted that when learners can exercise agency with digital tools, they avoid losing that agency to the technology.

3. Teacher Preparedness: Awareness, Skills & Mindset

Bhanu probed Chitra Ravi about the state of AI integration in government and low‑fee private schools.
Chitra (later) and Bhanu described a dual‑dimension preparedness framework:
1. Awareness – ranging from “AI is a buzzword I avoid” to “I’m dabbling on WhatsApp daily.”
2. Skill‑set – practical ability to identify, experiment with, and apply AI tools.
Sentiment spectrum:
- Hope → Stimulation (enthusiastic adoption)
- Fear → Resistance (avoidance).
AI Samarth is positioned as an equilibrating force, demystifying AI and fostering confidence among teachers who previously feared job displacement.

3.1. Curriculum Development & Relevance

The curriculum was co‑created by Wadhwani School and CSF, embedding highly relevant use‑cases that align with teachers’ day‑to‑day tasks.
Emphasis on purposeful use, not merely literacy about “what AI is,” but how it can enhance teaching practice.

3.2. Over‑utilisation Risks

Some teachers automatically generate lesson plans using LLMs (e.g., ChatGPT) without validating content, a pitfall the program seeks to correct.
Bhanu framed this as a “level‑er”: early over‑use may be noisy, but as competence grows AI becomes a true multiplier.

4. Global South Perspective: Survey Insights & Ground Realities

Ramya Venkataraman recounted her “Falcon” trajectory—from bootstrapping education practice at Mackenzie to leading Senta’s teacher‑engagement across 100+ countries.
Key data point: An India‑wide teacher survey revealed ≈ 70 % of teachers are already using AI in some capacity, albeit with wide variation in quality.
Contrast with other Global South regions:
- In Uganda, teacher assessments still required pen‑and‑paper, underscoring disparities in technology penetration.
Common challenge: Rapid usage growth outpaces conceptual understanding, leading to misconceptions about AI’s capabilities and limits.

4.1. Literacy vs. Skilling

AI Samarth provides foundational AI literacy (universal concepts) rather than role‑specific skilling, positioning teachers to critically assess AI outputs.
Example: a teacher in Jharkhand expressed relief that literacy helped her catch up with students who were moving faster into AI‑enabled learning.

5. Designing an Ethical, Critical‑Thinking‑Centred Curriculum

Dr. Shabana K M distinguished AI literacy (foundational concepts) from AI skilling (role‑specific tool use).
Four pillars of the AI Summit curriculum:
1. What is AI? – Applications: Recognizing everyday AI (e.g., recommender systems) and identifying the AI component.
2. Data Foundations: Understanding data’s role in training models, covering vision, NLP, and basic technical notions.
3. Societal & Environmental Impacts: Bias, fairness, carbon footprint, and broader ethical considerations.
4. Practical Interaction – Prompt Engineering: Crafting effective prompts for generative tools (ChatGPT, etc.).
Embedding Critical Thinking:
- Emphasize cross‑checking AI‑generated answers against verified sources.
- Promote a “AI‑review‑then‑improve” loop: students first produce work, then use AI for feedback, fostering human judgment.
- Classroom exercises reinforce vigilance and avoid over‑reliance on AI.

6. Teacher‑Training Model: Building Confidence & Cascading Knowledge

Chitra Ravi described the teacher‑training approach as creating an emotionally safe environment that acknowledges the hope‑fear spectrum.
Observations:
- Politeness contagion: LLMs habitually praise user queries, leading teachers to adopt more courteous classroom language.
- Cascading effect: Trained teachers become multipliers, delivering AI literacy downstream to peers and students.
Key insight: Resistance often stems from role identity (“I am the deliverer, not the receiver”). The Samarth model flips this by positioning teachers as both learners and trainers, cultivating a deep respect for the new content.

7. Guest Perspective: Policy, Inclusion & the Bigger Picture

Shri Krishnanji (MITI) offered a macro‑level view:
- Democratizing AI is essential for national progress; education is the primary lever.
- Technology as a multiplier, not a substitute for teachers; it should enable higher‑quality delivery.
- Policy alignment: The Indian government plans to introduce AI fundamentals from Class 3 onward, and higher‑education institutions are urged to embed AI across all disciplines, not just computer science.
- Job landscape: Only a tiny fraction (~300 globally) will be AI model builders; the majority will be AI‑augmented professionals in diverse fields.
- Inclusion imperative: Ensure no‑one is left behind as AI diffuses; the summit’s ethos is inclusive access.
Call to action: Visitors were encouraged to explore the Expo, showcasing social‑impact AI applications.

8. Closing & Acknowledgments

The session concluded with thanks from the moderator and a final round of applause for the speakers.

Key Takeaways

Scale with Care: AI Samarth has already touched ≈ 0.9 million learners, but responsible, equitable rollout remains crucial.
Dual‑Dimension Preparedness: Effective AI integration requires both awareness (knowledge of AI) and a mindset that balances hope and fear.
Curriculum Pillars: A robust AI‑literacy framework must cover foundational concepts, data fundamentals, societal impacts, and prompt engineering.
Critical Thinking First: Students should produce their own work first, then use AI for review and improvement, ensuring human judgment stays central.
Teacher as Multiplier: Training programs that foster emotional safety, confidence, and a cascading model amplify impact across schools.
Global South Variability: While 70 % of Indian teachers report AI use, many regions (e.g., Uganda) still lack basic digital infrastructure, highlighting uneven penetration.
Policy Momentum: Indian policy mandates AI education from Class 3, and the broader call is for AI literacy across all higher‑education disciplines.
Risk of Over‑Utilisation: Unchecked generation of lesson plans by LLMs can lead to unverified content; guidelines and validation are essential.
Inclusion as Core Goal: Democratizing AI must ensure no community is left behind, aligning with the summit’s overarching theme of inclusive futures.

See Also:

India AI Impact Summit 2026

Explorer

building-inclusive-futures-through-ai-literacy-for-india-and-the-global-south