Digital, AI & Technology
Digital tools should expand access, language support, practice, feedback and system visibility while teachers retain pedagogic responsibility.
What is failing—and what must be checked locally?
Technology that strengthens teachers, access and feedback without weakening safety or thinking.
Hardware procurement can precede electricity, connectivity, maintenance and teacher readiness.
Personalisation claims may be opaque, unvalidated or insensitive to language and disability.
Student data, attention and independent thinking can be harmed by poorly governed AI use.
Record the state, district, block/city, village/ward and school. Then identify the learner group, date, frequency, severity and observed consequence. A national statement must never be pasted onto a local school without verification.
Treat each cause as a hypothesis until tested.
Technology chosen before the learning problem
Check records, observe practice, interview affected people and test credible alternatives.
Fragmented platforms and weak interoperability
Check records, observe practice, interview affected people and test credible alternatives.
Insufficient teacher training and technical support
Check records, observe practice, interview affected people and test credible alternatives.
Unclear data protection, procurement and evaluation rules
Check records, observe practice, interview affected people and test credible alternatives.
A correlation, complaint or plausible story is not enough to prove causation. Triangulate administrative data, direct observation, learner/teacher experience and outcome measures.
Build a claim-by-claim evidence register.
| Question | Preferred evidence | Decision use |
|---|---|---|
| Does the problem exist? | Dated school/local record + corroboration | Define urgency and affected group |
| How large is it? | Comparable indicator with denominator and subgroup | Set baseline and allocate support |
| Why is it happening? | Mixed-method analysis testing alternatives | Select the mechanism to change |
| Will the option work here? | Relevant synthesis + India/near-context pilot | Pilot, modify or reject |
| Did it work and last? | Baseline, follow-up, cost and independent review | Stop, improve or scale |
Use the Pillar 01 evidence desk for NEP, NIPUN Bharat, UDISE+, ASER, PM POSHAN and official international sources. This page intentionally does not invent a national percentage where geography and methodology are unspecified.
Open source register →A connected solution, not a shopping list.
Start with a defined learning or administrative problem and a non-digital alternative.
Provide shared-device, offline and Indian-language modes.
Train teachers in selection, verification, bias, privacy and age-appropriate AI use.
Require accessibility, data minimisation, security testing, auditability and outcome evaluation.
Act now, test carefully, build for durability.
Diagnose and protect
- Audit device uptime and connectivity
- Publish student-data rules
- Train teachers on verification and safe AI use
Pilot the mechanism
Test one bounded AI-assisted use case with human oversight, a comparison group, learning outcomes, teacher workload and safety review.
Institutionalise
Integrate standards, workforce, finance, procurement, data, review and maintenance. Scale only after effect, equity and delivery capability are demonstrated.
Measure capability, equity and durability.
Good intentions do not remove implementation risk.
- RiskVendor lock-inDefine prevention, owner and incident response before pilot.
- RiskData extraction from childrenDefine prevention, owner and incident response before pilot.
- RiskHallucinated or biased contentDefine prevention, owner and incident response before pilot.
- RiskScreen time displacing human interactionDefine prevention, owner and incident response before pilot.