Practical, inspectable applications for designing, assessing and governing human–AI systems across education, research and professional practice.
These interactive applications demonstrate how AI-supported systems and workflows can be defined, analysed, governed and improved in practice. For curriculum design, Moodle analysis, educational publishing and visualisation, explore the separate Digital Learning Systems portfolio.
Not sure where to start or which tools to use?
The AI System Navigator guides you through your goal and recommends the right sequence of tools — from system definition through design, governance, and evidence.
Apply these tools in real academic and research work through guided courses, structured workflows, and practical system design.
→ Start learning AI system design and governance
If you are new to CloudPedagogy, start with the guided route:
→ Start with the AI System Navigator (recommended)
Or follow a manual pathway below based on your task.
Diagnose capability, explore patterns, and identify risks:
→ AI Capability Self-Assessment
→ AI Capability Dashboard
→ AI Capability Gaps & Risk
Design workflows, assess risk, and record decisions:
→ AI Workflow Governance Designer
→ AI Governance Risk Scanner
→ Human–AI Decision Record Tool
Understanding → Design → Assess → Simulate → Operate → Govern → Evidence → Evolve
Most workflows move through this sequence — you can enter at any stage depending on your needs.
Together, these applications provide full lifecycle coverage of AI-enabled academic and research systems — from understanding and design to governance, evidence, and continuous improvement.
All applications are open, inspectable, and available via GitHub:
https://github.com/cloudpedagogy
Each tool includes guidance on when to use it — select based on your task or context.
You can explore tools directly below — or use the AI System Navigator to be guided to the right ones.
This is the core system object that underpins all other tools.
These tools define the AI system being designed, assessed, and governed across the CloudPedagogy ecosystem.
→ When to use: Define the structure, risks, and governance of an AI system before or alongside using other tools.
[Launch tool] · [View source]
Start here if you want to understand, diagnose, or govern AI capability before building or deploying systems.
These tools support understanding, developing, and governing AI capability across individuals, teams, and institutions.
Tools for diagnosing, mapping, and developing AI capability across educational and organisational contexts.
AI Capability Self-Assessment
→ When to use: Understand your current AI capability and identify where to start.
[Launch tool] · [View source]
AI Capability Dashboard
→ When to use: Explore and reflect on AI capability patterns across individuals or teams.
[Launch tool] · [View source]
Human–AI Thinking Studio
→ When to use: Apply structured reasoning patterns, challenge assumptions, evaluate alternatives, assess risks, consider stakeholder perspectives, and improve decision quality when working with AI-supported systems.
[Launch tool] · [View source]
AI Capability Gaps & Risk
→ When to use: Identify capability gaps and associated risks in your organisation or practice.
[Launch tool] · [View source]
AI Capability Scenario Stress-Test
→ When to use: Test how your systems or organisation respond under future or uncertain AI scenarios.
[Launch tool] · [View source]
AI Capability Programme Mapping
→ When to use: Map how AI capability is developed across a curriculum or organisational structure.
[Launch tool] · [View source]
Tools for designing, analysing, and documenting human–AI decision-making and governance structures.
AI Workflow Governance Designer
→ When to use: Design and document AI-supported workflows with clear human oversight.
[Launch tool] · [View source]
AI Governance Risk Scanner
→ When to use: Analyse risk, fragility, or oversight gaps in an AI-supported workflow.
[Launch tool] · [View source]
AI Ethics Review System
→ When to use: Conduct structured ethical and governance reviews of AI systems before deployment, adoption, procurement, or institutional approval.
[Launch tool] · [View source]
AI Sustainability Impact Review System
→ When to use: Assess the long-term sustainability, maintainability, resilience, and wider organisational impact of AI-supported systems before deployment, scaling, or ongoing institutional use.
[Launch tool] · [View source]
AI Assurance & Review System
→ When to use: Review AI outputs, governance controls, grounding quality, operational risk, and oversight readiness before deployment or institutional use.
[Launch tool] · [View source]
Human–AI Decision Record Tool
→ When to use: Record and audit decisions where AI has influenced outcomes.
[Launch tool] · [View source]
AI Incident Register
→ When to use: Record, monitor, and review AI-related incidents, near misses, governance concerns, and operational failures to support accountability and continuous improvement.[Launch tool] · [View source]
AI Governance Maturity Assessment
→ When to use: Assess organisational readiness to govern AI systems effectively.
[Launch tool] · [View source]
These applications apply CloudPedagogy’s capability and governance approach to AI-aware assessment, curriculum capability mapping and governed research workflows.
AI Integrity Design Tool
→ When to use: Define acceptable AI use in assessment and generate clear student guidance.
[Launch tool] · [View source]
AI-Assisted Curriculum Refactoring Tool
→ When to use: Analyse, harmonise and improve curriculum content through privacy-preserving workflows and AI-assisted suggestions, with recommendations remaining subject to professional review.
[Launch tool] · [View source]
Research Workflow Engine
→ When to use: Design and document AI-enabled research workflows with governance and oversight.
[Launch tool] · [View source]
For curriculum and assessment design, Moodle course analysis, educational publishing and interactive visualisation, explore Digital Learning Systems.
Use these tools to design, structure, and operationalise AI-enabled workflows in practice.
These tools support the practical application of AI-enabled workflows and capability development in academic environments.
AI Capability Studio
→ When to use: Create structured workflow records aligned to the AI Capability Framework.
[Launch tool] · [View source]
These tools connect system design to institutional processes such as quality assurance, review, accreditation, and continuous improvement.
Evidence Pack Generator
→ When to use: Generate structured evidence for quality assurance, accreditation, or review processes.
[Launch tool] · [View source]
These tools support data exchange, interoperability, and structured outputs across the CloudPedagogy ecosystem.
These are supporting infrastructure components rather than end-user tools.
Integration Tool
[Launch tool] · [View source]
Integration SDK
[View source]
These applications are part of a broader capability development approach, combining practical tools with structured learning to support individuals and institutions in building AI-ready practices.
👉 Explore courses: https://www.cloudpedagogy.com/pages/ai-courses
A complete guide to the CloudPedagogy AI Governance System, including its architecture, workflows, applications, and real-world use cases.
👉 CloudPedagogy AI Governance System Handbook.pdf
Most institutions are currently experimenting with AI in fragmented ways — isolated tools, disconnected pilots, and unclear governance.
CloudPedagogy provides a structured alternative:
👉 a system for making AI use visible, accountable, and aligned with institutional goals
👉 a way to move from experimentation to governable, scalable practice
👉 a platform for designing human–AI systems that can be understood, reviewed, and improved over time
Unless otherwise stated, CloudPedagogy applications, documentation, and associated materials are independently maintained and published through CloudPedagogy.
They are not affiliated with, endorsed by, or representative of any employer, university, institution, or organisation.
All tools are provided for educational, experimental, and illustrative purposes only and do not constitute professional, legal, academic, technical, or institutional advice.
These tools are intended to support structured thinking, analysis, and decision-making but do not replace human judgement. Responsibility for the interpretation and use of outputs remains entirely with the user.
Functionality, assumptions, methodologies, and outputs may evolve over time as applications are updated and refined.
Open-source repositories, where applicable, remain subject to their respective licences and terms of use.