Illustrative decision application

Responsible AI in Education

Context archetype · Public education system / AI adoption

A public education system considering AI adoption must decide where AI creates measurable learning or teacher value, which uses require a controlled pilot, and which high-risk applications should remain prohibited.

Decision to unlock: Authorise national deployment, a controlled teacher-led pilot, redesigned use cases, or prohibit high-risk applications?
Why now: AI adoption is accelerating faster than public evidence, procurement standards and safeguards.
Value at stake: Learning quality, teacher productivity, territorial equity, child-data protection, public trust and avoidable technology expenditure.
Risk if wrong: Technology spend without learning impact; widening inequality; unsafe child-facing uses; vendor lock-in; weak teacher adoption.

STREDGE workstreams

Evidence & readiness baseline

Map connectivity, devices, teacher capability, curriculum fit, data governance and existing digital initiatives.

Use-case & risk architecture

Separate low-risk teacher-assistance uses from learner-facing or consequential applications requiring higher evidence.

Pilot economics & evaluation

Define sample, comparator, KPIs, whole-life cost and explicit scale/stop thresholds.

Procurement & governance

Create vendor-neutral requirements, human oversight, data protections and decision gates.

Government-owned outputs

Executive Decision Dossier
Pilot Design & Evaluation Pack
Responsible AI Controls
Vendor-Neutral Specification
Whole-Life Cost Model
Scale / Stop Criteria

Possible outcomes

Pilot / Redesign / Defer / Stop / Scale with conditions

Illustrative only. Anonymised and not presented as a disclosed client engagement, endorsement or result.

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Government & Public Institutions