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Artículo diario

Archivo de artículos

Análisis diarios sobre tecnología, inteligencia artificial y educación, elaborados con fuentes académicas verificadas y publicados en español, inglés y francés.

Artificial intelligence and mark-making / early drawing-writing in early childhood education

A CNN/CV that scores strokes/shapes/DAP or fine-motor/prewriting/drawing-age/VMI scores, a GenAI of copy-the-shape worksheets, a drawing-minutes/stroke-accuracy dashboard, a correctness app, or a developmental ranking without mediation do not constitute support for mark-making / early drawing-writing in early childhood: it is real materials, graphic agency, and adult co-presence that interprets meaning and process.

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Artificial intelligence and dialogic reading in early childhood education

A chatbot/LLM agent that “does dialogic reading” with the child, a comprehension/vocabulary/dialogic quality score, a GenAI of CROWD/PEER scripts, a shared-reading-minutes dashboard, or an app that replaces adult–child shared reading do not constitute support for dialogic reading in early childhood: it is a shared book with contingent PEER/CROWD and adult co-presence.

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Artificial intelligence and water play in early childhood education

A “pouring accuracy” / “STEM water score” sensor, a CV/water-table camera with “mess / off-task splashing” alerts, a GenAI of “water experiments” / sink-or-float lesson plans, a minutes-at-the-water-table dashboard, or an AR layer that guides pouring or predicts sink/float do not constitute support for water play in early childhood: it is exploration of the liquid with flow, capacity, sink-float and adult co-presence.

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Artificial intelligence and sand play in early childhood education

A “sandcastle accuracy” / “STEM sand score” / “molding accuracy” sensor, a CV/sandbox camera with “mess / off-task sand spilling” alerts, a GenAI of “sandcastle plans” / sand play lesson plans, a minutes-in-the-sandbox dashboard, or an AR layer that guides molding or predicts castle stability do not constitute support for sand play in early childhood: it is exploration of the granular with flow, compaction, moldability and adult co-presence.

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Artificial intelligence and playdough / clay play in early childhood education

A “playdough molding accuracy” / “fine-motor dough score” / “shape compliance” sensor, a CV/table camera with “mess / off-task dough spilling” alerts, a GenAI of “figure plans” / playdough lesson plans / STEM dough activities, a minutes-with-playdough dashboard, or an AR layer that guides kneading or predicts the correct shape do not constitute support for playdough / clay play in early childhood: it is exploration of the deformable with tactile properties and adult co-presence.

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Artificial intelligence and risky play in early childhood education

A wearable or “fall-risk score” sensor, a CV/playground camera with “unsafe play” alerts, a GenAI of “safe activities” / risk-free alternatives, a “risk exposure score” dashboard, or an AR layer that avoids height/speed do not constitute support for risky play in early childhood: it is child calibration with Sandseter categories, risk≠hazard and adult co-presence.

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Artificial intelligence and loose parts play / open-ended materials in early childhood education

A chatbot that “suggests constructions” or “loose parts ideas,” a GenAI generator of provocations by prompt, a CV system that scores “creativity,” an AR layer that guides arrangement, or an “open-endedness score” dashboard do not constitute support for loose parts play in early childhood: it is situated practice with open-ended physical materials, child agency, extended time, peers and adult co-presence.

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Artificial intelligence and environmental education / sustainability in early childhood education

An “eco-tips” chatbot or “green classroom” plans, an app scoring “green footprint,” computer vision of “sustainable acts,” or a prompt-based environmental-unit generator do not constitute early childhood environmental education: it is situated care of place, sensory nature exploration, everyday habits and adult mediation (ECEfS / nature pedagogy).

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Artificial intelligence and symbolic play / pretend play in early childhood education

A role-playing chatbot, a pretend-play script app, an autonomous voice agent directing roleplay, computer vision scoring “symbolic-play quality,” or a prompt-based dramatic-play unit generator do not constitute support for symbolic play in early childhood: it is situated peer practice with open-ended objects, metacommunication and adult scaffolding (teleoperated StoryCarnival = peripheral contrast).

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Artificial intelligence and executive functions in early childhood education

A cognitive training app, an adaptive game that scores attention or a model that classifies impulsivity do not develop executive functions in a 3–6 kindergarten: EFs are inhibitory control, working memory and cognitive flexibility built through shared play, conversation, mediated waiting, movement and adult scaffolding on real everyday conflicts; not digital drill or algorithmic attention scoring.

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Artificial intelligence, multilingualism, and early childhood education: linguistic bias, minoritized languages, and the risk of English-only AI literacy

Artificial intelligence is not linguistically neutral. A system trained mainly in English can widen language inequality in early childhood and basic education: Spanish scores below English, Indigenous languages operate with high-error ASR, and AI literacy that only works in English is not literacy.

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Academic integrity and generative artificial intelligence in basic education: authorship, evidence of learning, and the limits of detection

The arrival of generative text models has shifted academic integrity toward a problem of evidence: if a system can produce the artifact the school grades, what counts as proof that a child learned? In basic education the problem is not hunting cheats with a detector, but not evaluating a text without a subject.

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Generative artificial intelligence in early childhood education: scaffolding play, oral language and ethical limits of algorithmic mediation

A doctoral synthesis of how play and oral language can be scaffolded when generative artificial intelligence enters early childhood education. Drawing on seventeen studies from 1995–2021, PopBots (n=80, ages 4–7), AI robotic toys in the 2020 Australian lockdown (n=5) and child-agent interaction (n=26, ages 3–10), it argues that algorithmic mediation is defensible only when subordinated to the adult, to relational pedagogy and to the age limit on independent conversations with generative platforms.

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