How Saval Supports the UNESCO AI Competency Frameworks for Teachers and Students

In 2024, UNESCO published two companion documents that are quickly becoming the reference point for AI in education worldwide: the AI Competency Framework for Teachers and the AI Competency Framework for Students. Together, they answer a question institutions have been circling for two years — not "should we use AI in the classroom," but "what does responsible, effective AI competency actually look like, for educators and learners alike?"

The frameworks are built around a shared set of pillars: a human-centred mindset, ethical awareness, technical understanding, pedagogical application (for teachers) or foundational literacy (for students), and the ability to use AI to support collaboration and critical thinking rather than replace it. What's notable is that these aren't abstract values — they're meant to be assessed, tracked, and developed over time. That's where most institutions hit a wall: frameworks are easy to endorse and hard to operationalize.

Where assessment meets the framework

Saval was built around a simple premise: if AI competency matters, it has to show up in how students are assessed, not just in a policy document. Here's how that plays out against the framework's core pillars:

• Ethical and human-centred use — Saval's scenario-based tasks are designed so students must justify how and why they used AI in their reasoning, surfacing ethical judgment as part of the assessment itself, not a separate module.
• Critical thinking over answer-copying — Because Saval's gamified formative assessment rewards the reasoning path, not just the final answer, it naturally pushes students toward the kind of AI-assisted-but-not-AI-replaced thinking the framework calls for.
• Teacher enablement — On the educator side, Saval's dashboards give teachers the real-time visibility they need to model and teach AI competency directly, aligning with the framework's emphasis on teachers as active guides rather than gatekeepers.
• Continuous, formative development — The frameworks describe competency as a progression, not a checkbox. Saval's formative, ongoing assessment model mirrors that same philosophy: competency is built incrementally, with feedback at every step.

Why this alignment matters

Frameworks like UNESCO's give institutions a shared language for AI competency — but adoption stalls without tools that make the framework practicable at the classroom level. Platforms that embed these principles directly into assessment design, rather than bolting them on as a policy layer, are what will actually move the needle on AI literacy at scale.

As more institutions look to align their teaching and assessment practices with UNESCO's frameworks, the platforms that succeed won't be the ones that mention AI competency in a mission statement — they'll be the ones where every assessment task is quietly reinforcing it.