Understanding the Framework Landscape

Across education, research, the public sector, and professional practice, a growing number of AI-related frameworks are used to support benchmarking, workforce development, skills planning, and sector readiness. Each of these frameworks plays an important role in helping organisations understand what AI is, where it is being used, and which skills or controls may be required.

The CloudPedagogy AI Capability Framework (2026 Edition) is designed to complement these approaches by focusing on a distinct but often under-addressed question:

How do people and institutions work responsibly, reflectively, and effectively with generative AI in real practice — over time?

Rather than replicating benchmarking models or skills taxonomies, CloudPedagogy focuses on sustained human–AI capability: the ability to think clearly, exercise judgement, design responsible workflows, govern decisions transparently, and adapt practice as AI technologies and contexts evolve.


What This Comparison Shows

The comparison below highlights differences in purpose and design intent across several widely recognised and mainstream AI frameworks. These frameworks represent established approaches to:

  • institutional assessment and benchmarking
  • workforce skills and competency development
  • sector-specific capability and safety readiness


They are referenced here to provide context, not to rank quality or effectiveness.

CloudPedagogy addresses a different layer of the AI challenge — how capability is embedded, governed, reflected upon, and renewed in everyday academic, research, and organisational work.


High-Level Comparison by Design Intent

Dimension QS AI Capability Framework Alan Turing Institute – AI Skills for Business NHS DART-Ed Capability Framework CloudPedagogy AI Capability Framework
Primary purpose External assessment and benchmarking Workforce upskilling and competency development Sector workforce readiness (healthcare) Sustained human–AI capability in practice
Orientation Institutional snapshot Individual competence Role-based capability Human–AI partnership
Primary sector focus Higher education Business (cross-sector) Healthcare Education, research, public service
Generative AI focus Not the primary focus Partially addressed Partially addressed Explicitly designed for GenAI
Human judgement Implicit Explicit at role level Explicit in safety-critical contexts Core design principle
Ethics framing Compliance-oriented Professional values Safety and regulation Ethical practice and human agency
Reflection and renewal Not a core feature Project-level reflection Developmental review Structural and continuous
Governance support Conceptual Conceptual Role-based guidance Operational templates and workflows
Practical artefacts Limited Limited Mapped learning resources Toolkits, prompts, scenarios, governance assets


How CloudPedagogy Extends Existing Approaches

CloudPedagogy does not seek to replace established AI frameworks. Instead, it extends them in ways that support long-term, real-world practice.

From Skills to Capability

Skills and competencies matter — but they are not sufficient on their own. CloudPedagogy focuses on capability: the ability to apply knowledge with judgement, navigate uncertainty, govern AI-supported decisions responsibly, and continue learning as technologies evolve.

From Tool Use to Human–AI Partnership

Rather than treating AI solely as a system to deploy or a tool to master, CloudPedagogy frames AI as a thinking partner that must be intentionally designed, constrained, supervised, and reflected upon. Human expertise and accountability remain central.

From One-Off Training to Continuous Renewal

AI capability is not static. CloudPedagogy embeds reflection, learning, and renewal directly into its structure, enabling individuals and institutions to adapt their practice as models, tools, and expectations change.


Complementary, Not Competitive

Established AI frameworks play a vital role in benchmarking progress, defining workforce skills, and supporting sector-specific readiness. CloudPedagogy complements these efforts by focusing on how AI capability is lived, governed, and sustained in everyday practice.

Together, these approaches form a more complete ecosystem for responsible, effective, and future-ready AI adoption.