Competency Framework Translator

What This Skill Does

Takes any external competency framework — DigComp (digital competence), GreenComp (sustainability), ISTE Standards, UNESCO AI competency framework, national curriculum frameworks, or any other competency-based system — and translates its abstract descriptors into classroom-ready observable indicators, progression levels, teaching tasks, and assessment criteria for a specific subject and year group. The critical problem this solves is that competency frameworks are written for policy audiences, not for teachers: "Critically evaluates the credibility and reliability of sources of data, information and digital content" (DigComp 2.2) is meaningful to a policy analyst but tells a Year 8 History teacher nothing about what to teach, what tasks to set, or how to assess. The output is a translation: the same competency, expressed as specific things students can DO at different levels, with tasks that make the competency visible and criteria that make it assessable. AI is specifically valuable here because this translation requires understanding both the framework's intent (what the competency is really asking for) and the classroom reality (what students at this age can be expected to do, in this subject, with these resources).

Evidence Foundation

Wiggins & McTighe (2005) established the principle that standards and competencies must be "unpacked" into observable indicators before they can be taught and assessed. A competency statement is a destination; the teacher needs a map showing what the journey looks like at each stage. Without unpacking, teachers either teach to the abstract wording (which students can't understand) or interpret the competency so broadly that it loses its meaning. Marzano & Kendall (2007) provided a taxonomy for operationalising competencies — moving from retrieval (can the student recall the knowledge?) through comprehension (can they explain it?) to analysis (can they compare, classify, evaluate?) to knowledge utilisation (can they apply it in a new context?). This taxonomy provides the backbone for progression levels. The DigComp 2.2 framework (European Commission, 2022) is a worked example of a competency framework that explicitly acknowledges the need for contextualisation — it provides 8 proficiency levels but notes that these must be adapted for specific educational contexts. GreenComp (Bianchi et al., 2022) takes a similar approach, defining sustainability competencies at a high level but requiring schools to translate them into subject-specific learning. UNESCO's (2023) guidance on AI in education introduces competencies for AI literacy that are new to most curriculum frameworks and urgently need classroom-level translation. In all cases, the gap between framework and classroom is the problem this skill addresses.

Input Schema

The teacher must provide:

• Framework reference: Which framework and which competency. e.g. "DigComp 2.2 — Competency 1.2: Evaluating data, information and digital content" / "GreenComp — Competency area: Embracing complexity in sustainability" / "ISTE Standards for Students — Computational Thinker" / "UNESCO AI competency — Understanding AI ethics and responsible use"
• Target context: Where this competency will be taught. e.g. "Year 9 History — students evaluating online sources about the causes of WW1" / "Year 7 Geography — students understanding the interconnected causes of climate change" / "Year 10 Computer Science — students understanding bias in AI systems"

Optional (injected by context engine if available):

• Student level: Year group
• Subject area: The curriculum subject