The five components
- Technical Understanding (TEC)Knowledge and skills required to understand AI systems and their capabilities.
- Critical Evaluation (EVA)Systematic assessment of AI outputs, processes, and impacts.
- Practical Application (PRA)Effective implementation of AI tools and processes in real-world contexts.
- Ethical Considerations (ETH)Responsible and principled use of AI in professional contexts.
- Data Literacy (DAT)Understanding data as the foundation for AI systems.
The sixteen sub-competencies
| Code | Sub-competency | Core focus |
|---|---|---|
| TEC.1 | Conceptual Awareness | Understanding what AI is and what it can do |
| TEC.2 | Tool Proficiency | Using AI tools effectively |
| TEC.3 | Process Knowledge | Integrating AI into workflows |
| EVA.1 | Output Assessment | Verifying AI-generated content |
| EVA.2 | Quality Control | Assessing AI tool performance and biases |
| EVA.3 | Impact Analysis | Measuring effects of AI use |
| PRA.1 | Implementation | Applying AI to real professional tasks |
| PRA.2 | Workflow Enhancement | Redesigning processes with AI |
| PRA.3 | Problem Solving | Addressing complex challenges with AI |
| ETH.1 | Responsibility | Transparent, accountable AI use |
| ETH.2 | Compliance | Operating within policies and guidelines |
| ETH.3 | Risk Management | Identifying and mitigating AI risks |
| DAT.1 | Data Understanding | Comprehending data that underlies AI |
| DAT.2 | Data Collection & Management | Handling data responsibly |
| DAT.3 | Data Analysis | Analyzing and interpreting data |
| DAT.4 | Data Visualization & Communication | Representing and communicating data insights |
The components are interdependent. Data Literacy provides a foundation for the others, Technical Understanding and Critical Evaluation develop together, Ethical Considerations cuts across all components, and Practical Application draws on all of them at once.