Ingishts

The UAICS knowledge map contains a computational thinking family; at the same time, the standard has never assessed programming. The two decisions look contradictory. They come from a single judgement, worth setting out in full. First, why computational thinking is in. Decomposition, pattern recognition, abstraction — these are not coding; they are methods for making a problem explicit. And that is precisely the substrate of working with AI: breaking a task into ordered steps

It is the most common question parents ask, and the most legitimate. Essays polished by AI, maths explained by AI, first drafts generated by AI — over the years, does a child’s own thinking atrophy? Cognitive science gives an answer more honest than yes or no: it depends on how. Cognitive load theory holds that working memory is strictly limited, and that learning happens when that limited capacity is spent on generative processing — organising for oneself, inferring for ones

Claiming to be "based on international frameworks" is easy. The hard part is answering: based on what, exactly? What was adopted, what redesigned, what added? This article publishes the full methodology, open to examination. Begin with each framework’s coverage and gaps. UNESCO’s AI Competency Framework for Students (2024) — four dimensions by three levels — goes deepest on the human-centred and ethical, but deliberately avoids age banding, so it cannot say what a nine-year-o

Competency-based education carries a long-standing dilemma: the most important capacities are the hardest to examine. Reflection, values, self-awareness — every curriculum declares them essential, and so they become rating scales, reflection essays, checklists, and marks. What students usually learn is not reflection, but how to produce writing that resembles it. Measurement theory explains why. Examinations reliably assess knowledge with right answers and performance with qu

On AI teacher training, the market’s default answer is: teach teachers to use AI tools. Run a workshop, issue an attendance certificate, expect the classroom to change. The answer fails quietly — because it trains at right angles to the actual gap. The real gap spans two professions. On one side, technical understanding: how models are trained, how data shapes output, why generative AI errs so fluently. On the other, cognitive development: what a ten-year-old’s working memory

This Board did not begin with a document. It began with a scene that kept repeating: teachers, parents and children standing before the same wave — knowing AI matters, not knowing where to begin. The teachers’ helplessness is concrete. They teach every day, yet are asked to teach a subject that did not exist when they studied; those fluent in the technology lack grounding in child development, those grounded in children lack the technology — and the students have already fini