Digital News Report 2026 — research report chapter, not a peer-reviewed journal article
Published
2026-06-16
Original Digital News Report 2026 chapter by Amy Ross Arguedas analyzing who uses AI chatbots for news, what they ask for and whether they follow links to publishers. Survey-based research; the chapter does not claim that chatbot use causes changes in news trust or traffic.
#ai-chatbots#journalism#news
reutersinstitute.politics.ox.ac.ukAdded Nov 13, 20260 opens
Public experiment testing whether language models appear to recognize a name without web search. Its methodology clusters generated descriptions and combines model confidence into a strength score. The creators warn that confidence is uncalibrated and biographies can be hallucinated; the score is not a verified measure of fame or evidence of a specific training record.
Research project at Radboud University in the Netherlands that studies AI in science and society from critical expert perspectives, with project publications and outputs on AI literacy.
An NCDA Career Convergence article by Safaa Amer (Oct. 2026) on how AI tools are reshaping career coaching — expanding access and automating tasks — and the AI literacy and ethical practice coaches need to use them responsibly.
A learning site aimed at helping non-technical people understand and use AI. The site blocked an automated check, so this description is based on the site name and focus; confirm details on the page.
An open-access paper on Zenodo describing Critical AI Literacies (CAILs): ways of thinking about AI that reject framings from the technology industry, naive computationalism, and dehumanizing ideologies, and that center human cognition and the integrity of research and education. Co-authored by Olivia Guest and Iris van Rooij, among others.
An April 2026 U.S. Department of Labor announcement of a national contracting opportunity to integrate artificial intelligence skills into Registered Apprenticeship programs. The Employment and Training Administration initiative seeks to expand AI-related training, modernize apprenticeship programs, and build AI literacy and technical skills across industries, aligning with the department's AI Literacy Framework and the Make America AI-Ready initiative.
Why I recommend it: If you want to move into AI-adjacent work without a four-year degree, Registered Apprenticeships are one of the few earn-while-you-learn paths, and this initiative is the signal that AI skills are now part of that pipeline. Watch your state's apprenticeship sponsor list for AI-related openings.
A communal blog where working mathematicians — professors, PhD students, sceptics and enthusiasts alike — write about what AI is doing to their field: authorship, what counts as understanding, where papers will go, and whether the job changes. Recent pieces include Martin Hairer on why he joined the AGMAI advisory group and Jonny Evans on the choices ahead. Submissions are open to anyone in the field, any length.
Why I recommend it: Free to read and free to write for. Worth reading even if you never touch mathematics: it is one of the few places where a whole profession is arguing in public about what AI does to its craft, in its own words rather than a journalist's. These are individual opinions, not findings — the value is the range of them, and the disagreement is the point.
A Wharton working paper by Steven D Shaw and Gideon Nave, written 11 January 2026 and posted to SSRN on 2 February 2026. It proposes "Tri-System Theory" — adding a "System 3" (thinking done outside your head by a machine) to Kahneman's fast/slow account — and names "cognitive surrender": taking an AI's answer with barely a glance. Across three preregistered experiments (1,372 people, 9,593 trials) the researchers secretly varied whether the AI was right. People consulted it on more than half of questions; accuracy rose about 25 percentage points when the AI was right and fell about 15 when it was wrong, and confidence went up either way — even after errors. Time pressure, cash incentives and feedback all moved baseline scores but never removed the pattern.
Why I recommend it: Free to read and free to download the full 58-page PDF — no account needed. Two honest flags. It is a working paper: posted by the authors, and the SSRN version has not been through journal peer review, so treat the numbers as a strong first result rather than settled fact. And the copyright line says all rights reserved — read and cite it, do not republish the text. The finding worth carrying around is the one about confidence: people felt surer of themselves after the AI led them wrong. That is exactly why the checks on our AI Basics page are worth doing out loud.
The free research library and blog of Snorkel AI, the company spun out of Stanford's Snorkel project on programmatic labelling. The papers and posts explain how training data for AI models is actually built — labelling, evaluation sets, and the 'environments' used to train agents. Useful if you want to understand the unglamorous data work behind model quality, which is where a lot of the real jobs are.
Why I recommend it: Research and blog posts are free to read with no signup. Read it for the how, not the verdict: Snorkel sells data services to frontier AI labs, so posts arguing that better data beats bigger models are also a sales case. Everything else on the site is a paid enterprise product — 'request dataset samples' means a sales call.
A satirical site built around a real habit: the person who pastes a question into an AI and pastes the answer back, adding nothing but delay. It has a thirty-second six-statement self-test, a list of the tells, four free explainer graphics under CC BY 4.0, and a free 44-icon Slack and Discord emoji pack.
Why I recommend it: Funny, free, and the fastest way to make a point in a team that has started forwarding chatbot answers as work. The four downloadable graphics are CC BY 4.0, so you can put them in a deck or Slack thread if you credit meatproxy.me with a link. Two things to know before you share it: the underlying idea belongs to Niklas Gruhn's short essay, which is the better read if you want the argument rather than the joke; and this site promotes a Solana crypto token and merchandise, so send people the test, not the wallet.
A three-page printable reference for Google Gemini: the PART prompt formula (Persona, Action, Reference, Tone), follow-up prompting, when to use Flash, Pro and Thinking models, saving your own context in settings, and worked sample prompts for Gemini inside Chrome, Gmail, Docs, Sheets, Slides and Drive. Includes a plain warning about Gemini being confidently wrong.
Why I recommend it: Print this and keep it next to you for a week. Most people get weak AI output because they skip the persona and the reference, and this one page fixes that faster than any course will. The limitations box at the bottom is the part to take seriously.
Free, self-paced hands-on lab using ChatGPT, Claude, Gemini, NotebookLM, and Perplexity for real work tasks.
Why I recommend it: A recognizable university name on a free AI literacy course. Finish it with one work problem you solved using the tools so you have a story to tell.
Experis/ManpowerGroup research summary on how employers and employees are actually using AI at work, plus a five-step action plan for building AI career durability.
In plain terms: A research write-up from staffing firm Experis showing most employers now use AI in hiring and are fine with candidates using it too, while very few companies have AI fully rolled out. It argues AI mostly augments jobs rather than replacing them, and lists five practical steps — build durable skills, learn your company's AI tools, research use cases for your role, take free training, and propose a small pilot.
From the site: Exploring the key findings of our new report: Building and Sustaining a Meaningful Career in the AI Age.
Why I recommend it: The headline stat matters for job seekers: 85% of employers say it is fine for candidates to use AI during hiring, and 53% already use AI in hiring and onboarding. Use the five-step durability plan as a checklist — learn what AI your employer is deploying, find use cases for your role, take free training, then pitch one small pilot you can measure. That pilot becomes a resume bullet.
Anthropic's free course teaching a practical framework for working with AI: delegation, description, discernment, and diligence. Good grounding before you use AI in job search, school, or client work.
In plain terms: This free online course teaches a practical framework for using artificial intelligence tools effectively and responsibly. You can practice prompting techniques, learn how to evaluate AI results, and earn a certificate of completion.
From the site: A free course from Anthropic on the 4D framework for working effectively and responsibly with AI: Delegation, Description, Discernment, and Diligence.
Why I recommend it: This is the fastest way to sound credible about AI in an interview. Take the course, then describe one task you redesigned with AI and what you checked before trusting the output.
