Practical Application is one of the five components of AI literacy in the ALiF Standard. Every conforming implementation addresses all five, with emphasis that varies by professional role.
Key elements
- Domain-specific implementations
- Workflow optimization
- Problem-solving approaches
- Innovation development
Example competencies
- Applies AI tools to domain-specific tasks
- Develops efficient AI-enhanced workflows
- Solves complex problems using AI capabilities
- Creates innovative approaches to professional challenges
How it relates to the other components
Practical Application (PRA) as integrative. Applying AI effectively in professional contexts requires drawing on all other components simultaneously. PRA is where TEC, EVA, ETH, and DAT converge in action. This integrative nature means that PRA competencies are difficult to develop without corresponding development in the other four components.
Sub-competencies (3)
PRA.1 Implementation
The ability to apply AI tools to real professional tasks and integrate them into existing work contexts. This sub-competency encompasses selecting appropriate AI tools for specific problems, deploying AI solutions within professional settings, adapting implementations to local constraints, and managing the practical challenges of introducing AI into established practices. It is the bridge between knowing what AI can do (TEC) and making it work in practice.
| Foundation (L1) | Intermediate (L2) | Advanced (L3) |
|---|---|---|
| Applies AI tools to basic professional tasks. Selects appropriate tools for straightforward problems. Follows established implementation procedures. Documents AI use in work context. | Integrates AI tools across multiple aspects of professional work. Adapts implementations to local constraints. Manages practical challenges of AI deployment. Develops implementation procedures for common use cases. | Designs implementation strategies that account for organizational context and change management. Creates scalable deployment approaches with built-in evaluation. Leads implementation projects across teams. |
PRA.2 Workflow Enhancement
The ability to redesign and optimize professional processes through strategic AI integration. This sub-competency encompasses mapping existing workflows to identify AI-enhancement opportunities, creating effective human-AI collaboration patterns, measuring process improvements, and iterating on AI-enhanced workflows based on experience. Where PRA.1 introduces AI into existing work, PRA.2 transforms the work itself.
Proposed in ALiF 2.0: Adds identifying tasks that serve as learning opportunities and preserving them within real work or, where that is not possible, providing supervised alternatives.
| Foundation (L1) | Intermediate (L2) | Advanced (L3) |
|---|---|---|
| Identifies opportunities where AI can improve existing processes. Implements simple AI-enhanced workflows. Documents changes to working processes. Recognizes bottlenecks AI might address. | Redesigns workflows to optimize human-AI collaboration. Creates process maps showing AI integration points. Measures productivity and quality improvements. Develops standardized workflow templates. | Develops workflow approaches that fundamentally restructure professional practice. Creates human-AI collaboration models that preserve professional judgment while leveraging automation. Leads strategic workflow transformation across organizational units. |
PRA.3 Problem Solving
The ability to use AI as a tool for addressing complex professional challenges that resist conventional approaches. This sub-competency encompasses framing problems in ways that leverage AI capabilities, combining AI outputs with professional judgment to develop solutions, and adapting AI approaches when initial strategies prove insufficient. It represents the creative, judgment-intensive dimension of practical AI use.
| Foundation (L1) | Intermediate (L2) | Advanced (L3) |
|---|---|---|
| Uses AI to address straightforward professional challenges. Applies AI outputs with professional judgment. Recognizes when AI is and is not helpful for a given problem. | Frames complex problems to leverage AI capabilities effectively. Combines multiple AI approaches when single tools prove insufficient. Iterates on solutions using AI feedback and professional expertise. | Addresses novel, high-stakes problems through creative integration of AI capabilities with deep professional judgment. Develops transferable problem-solving methodologies. Mentors others in AI-augmented reasoning. |
Definitions and level descriptors are quoted from ALiF Standard v1.0r1. The level descriptors are informative; role-specific descriptors are in the Competency Framework.