Showing 313–324 of 441 articles
Inequality Often Hides Inside the Average — the home as the first laboratory of digital citizenship: Families in a connected world
A household can appear well connected in statistics while distributing devices, time, privacy, support, and decision-making power very unequally. Looking inside the average reveals those gaps and helps make the home a fairer place for digital citizenship.
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The Invisible Question That Changes Everything — the home as the first laboratory of digital citizenship: Families in a connected world
Digital citizenship begins in the household's small decisions: who may speak, which data are shared, how harm is repaired, and which question comes before every click. The invisible question—who benefits, who is exposed, and what relationship is being built—turns screen use into a daily practice of judgement, care, and participation.
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Trust Begins When Reporting a Failure Is Not Frightening — a school capable of learning from its own decisions: The school that learns to change
A school learns when it can discover failures before they become routine. Doing so requires psychological safety, clear reporting routes, fair responses, and disciplined inquiry that turns every signal into shared improvement.
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Time Turns a Temporary Solution into Something Else — a school capable of learning from its own decisions: The school that learns to change
Every temporary measure contains a promise of review. When that promise disappears, an exception becomes a system without debate or learning. This article proposes expiry dates, records, cases, and responsibilities so that a school can decide what to retain, correct, or withdraw.
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Power appears where someone can say enough — a school capable of learning from its own decisions: The school that learns to change
A school truly learns when it distributes authority to stop, review, and repair decisions. This article offers criteria, cases, and a protocol for evidence-informed change without turning innovation into obedience.
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The exception does not break the rule: it explains it — a school capable of learning from its own decisions: The school that learns to change
An exception is not a nuisance to hide in order to save a rule. It is information about the rule’s limits, assumptions, and effects. This guide shows how to hear singular cases, protect rights, and turn them into institutional learning without abandoning consistency.
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Trust begins when reporting a mistake is not frightening — a generation that talks with machines: Artificial intelligence and adolescence
When a teenager can say without fear that an artificial intelligence system was wrong, manipulated them, or received intimate data, genuine protection can begin: listening, checking, repairing, and learning without turning the mistake into blame.
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Inequality often hides inside the average — a generation that talks with machines: Artificial intelligence and adolescence
The average results of an artificial intelligence tool may look positive while hiding who receives worse answers, who gives up more data, and who lacks a human alternative.
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Time turns a temporary solution into something else — a generation that talks with machines: Artificial intelligence and adolescence
An artificial intelligence tool can begin as temporary support and end up organising habits, assessment, and relationships; protecting adolescents requires reviewing that change before routine replaces decision.
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Power appears where someone can say stop — a generation that talks with machines: Artificial intelligence and adolescence
Adolescent autonomy around artificial intelligence is measured not only by the ability to enter a conversation, but by the real right to stop it, question it, and find a human way out.
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The exception does not break the rule: it explains it — a generation that talks with machines: Artificial intelligence and adolescence
When an adolescent treats artificial intelligence as a tutor, confidant, or opponent, the exception reveals something important: governing the tool is not enough; the relationship itself must be educated.
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When measuring better requires looking beyond a single number — a generation that talks with machines: Artificial intelligence and adolescence
A single number may look objective while concealing learning, inequality, well-being, and artificial-intelligence errors. Better measurement requires combining evidence, listening to adolescents, and always preserving the possibility of reviewing a decision.
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