A lightweight, browser-based exploratory tool for examining how a current AI capability profile may hold up under plausible change scenarios, using the CloudPedagogy AI Capability Framework.
Phase: Phase 3 — Capability System
Role: Pressure-tests organizational AI capability by modeling resilience and fragility under specific high-risk or disruptive scenarios.
Upstream Inputs: Diagnostic risk profiles and priority gaps from the Capability Gaps & Risk tool.
Downstream Outputs: Provides strategic foresight and mitigation signals used for institutional risk registers and capability building plans.
Does NOT:
- Provide baseline capability measurements (this is for the Assessment tool).
- Calculate historical trends (this is for the Dashboard).
For a full system overview, see: SYSTEM_OVERVIEW.md
The Scenario Stress-Test helps teams surface tensions, fragilities, and pressure points that may emerge when AI use is stressed by rapid change, disruption, or increased scrutiny. It is designed to support strategic discussion, foresight, and judgement — not prediction or decision-making.
This tool is part of the CloudPedagogy AI Capability Tools suite.
👉 http://cloudpedagogy-ai-capability-scenario-stress-test.s3-website.eu-west-2.amazonaws.com/
git clone [repository-url]
cd [repository-folder]npm installnpm run devOnce running, your terminal will display a local URL (often http://localhost:5173). Open this in your browser to use the application.
npm run buildThe production build will be generated in the dist/ directory and can be deployed to any static hosting service.
- Fully local: All data remains in the user's browser
- No backend: No external API calls or database storage
- Privacy-preserving: No tracking or data exfiltration
- Suitable for use in sensitive organisational and governance contexts
The AI Capability Scenario Stress-Test helps individuals, teams, and organisations:
- explore how existing AI capability might perform under plausible future or disruptive scenarios
- surface stress points where current practice may become fragile or contested
- examine how scenario demands interact with strengths, gaps, and imbalances across capability domains
- support foresight, risk-aware discussion, and strategic sense-making
- identify small stabilising steps before scaling or extending AI use
The tool is capability-led, exploratory, and interpretive.
It is explicitly designed to support professional judgement, not replace it.
This tool is not:
- a prediction engine or forecasting model
- a compliance audit, checklist, or benchmark
- a maturity model or ranking system
- a risk register or legal assessment
- a policy recommender or automated decision system
- a substitute for institutional governance or accountability
All outputs are signals, prompts, and lenses for discussion, not decisions.
The Scenario Stress-Test is grounded in the six interdependent domains of the CloudPedagogy AI Capability Framework:
Shared understanding, boundaries, risks, and realistic expectations of AI in context
Role clarity, partnership practices, prompting as collaboration, and human judgement in the loop
Practical use of AI in workflows, experimentation, iteration, and improvement of practice
Fairness, inclusion, harm reduction, transparency, and attention to downstream impacts
Accountability, approvals, oversight, policy alignment, and decision hygiene
Review cycles, learning from experience, capability renewal, and institutional memory
These domains act as lenses, not checklists.
- Enter basic context information (team or organisation name, optional notes)
- Provide reflective baseline capability scores (0–4) across the six domains
- If completing as a team, scores should be agreed through discussion
- Select one plausible or strategically relevant scenario, such as:
- rapid AI uptake in assessment
- regulatory tightening or audit scrutiny
- reputational or public incident
- vendor disruption or withdrawal
- loss of a key AI champion
- shifts toward more agentic AI workflows
- Optionally select context signals (e.g. high-stakes use, public-facing outputs, sensitive data)
- Optionally provide coverage estimates (0–100%) to highlight structural emphasis or neglect
- Run the stress-test to generate:
- scenario pressure signals by domain
- stress-amplified gap and imbalance signals
- “why this matters” explanations
- committee- and workshop-ready discussion prompts
- suggested small stabilisers to consider
The tool is designed to be used collaboratively, not mechanically.
The Scenario Stress-Test generates a structured results view including:
- scenario summary and framing
- baseline capability profile
- scenario pressure by domain
- stress-amplified risk and imbalance signals
- domain-specific context and explanations
- structured discussion prompts
- example stabilising actions to consider
-
Copy summary for discussion
Copy/paste outputs into:- committee papers
- QA or governance notes
- scenario planning workshop materials
- programme or institutional documentation
-
Print / Save as PDF
Uses the browser’s print function for archiving or sharing.
- Strategic curriculum foresight and scenario planning
- AI governance and risk-aware leadership discussions
- Preparing for regulatory, audit, or reputational scrutiny
- Stress-testing AI use before scaling or formalisation
- Design sprints and futures-oriented workshops
- Exploring resilience under disruption or change
The tool is especially effective when used with cross-functional groups (educators, professional staff, leaders, governance roles).
- The application runs entirely client-side
- No accounts, analytics, or tracking
- No data is uploaded or transmitted
- All inputs exist only in the user’s browser session
- Clearing the browser resets the session
- Suitable for static hosting (e.g. AWS S3)
This repository contains exploratory, framework-aligned tools developed for reflection, learning, and discussion.
These tools are provided as-is and are not production systems, audits, or compliance instruments. Outputs are indicative only and should be interpreted in context using professional judgement.
All applications are designed to run locally in the browser. No user data is collected, stored, or transmitted.
All example data and structures are synthetic and do not represent any real institution, programme, or curriculum.
This repository contains open-source software released under the MIT License.
CloudPedagogy frameworks and related materials are licensed separately and are not embedded or enforced within this software.
CloudPedagogy develops open, governance-credible resources for building confident, responsible AI capability across education, research, and public service.