The ability to understand and purposefully leverage AI tools to boost personal and organizational productivity. A core competency for the age of human-AI collaboration, going far beyond simple usage.
AI Utilization is the competency of understanding diverse AI tools and services and effectively applying them to your work and goals. It spans prompt crafting, workflow automation, and AI-driven decision-making, with a focus on maximizing productivity and creativity while recognizing the limitations and ethical dimensions of AI. It also includes the capacity to continuously learn and adapt in a rapidly evolving AI landscape.
Driven by curiosity, you try chatbots and image generation tools for the first time. You understand the basic concepts of AI and can obtain outputs through simple questions and requests. You recognize that AI is not always accurate, and you explore various AI services to get a sense of what is possible.
What Comes Next
If you've checked off most of this list, you're ready for the Acquire, Apply stage, using AI tools regularly, specifying role, context, and format in your prompts, and learning foundational prompt techniques. Bandura(1977)'s Social Learning theory suggests watching AI tool demonstrations and others' success cases builds the confidence to experiment on your own.
Structures AI competency into Acquire-Deepen-Create stages with 5 core domains (human-centered mindset, AI ethics, technical knowledge, pedagogical integration, professional development), providing rationale for level boundary design.
Categorizes generative AI proficiency into Base-Simple-Complex-Custom-Task Specific stages with practice-oriented behavioral criteria, providing measurable action indicators for checklist item design.
Industry-standard competency framework structuring AI/ML skills across 7 responsibility levels (Follow to Set strategy), providing authoritative grounding for autonomy, influence, and complexity criteria in level design.
EU digital competence framework providing 250+ examples of knowledge, skills, and attitudes for AI interaction, serving as public evidence base for checklist items on fact-checking, privacy, and ethical judgment in AI tool usage.
