AI and the misinformation ecosystem
Understand how AI changes the production and spread of false information, and what helps
A taste of a lesson
Won't AI make so much fake content that nobody can know what's true anymore?
It is a reasonable worry, but the evidence suggests a more specific picture. AI makes fake content cheaper, yet supply was rarely the main bottleneck; attention and distribution are. Studies find individual persuasive messages usually have small effects. The deeper risk is erosion of trust, where people dismiss real evidence as fake and retreat to sources they already like. That points to responses like provenance, trusted outlets and prebunking. Question for you: which matters more to fix first, supply, distribution or demand, and why?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Distinguish misinformation, disinformation and malinformation
- Analyse a misinformation episode as supply, distribution and demand
- Describe how AI changes production and what evidence says about impact
- Evaluate responses such as labelling, prebunking and fact checking with their trade offs
Lesson plan
- 1 Terms and the system view Define key terms and see misinformation as supply, distribution and demand. Start
- 2 What AI changes in supply Understand how generative AI lowers the cost of producing persuasive content. Start
- 3 Distribution and demand See how platforms and human psychology shape what spreads. Start
- 4 What the evidence says about impact Weigh evidence on AI's real effects without panic or complacency. Start
- 5 Responses and trade offs Evaluate interventions and their side effects. Start
- 6 Analysing an episode Apply a full analysis template to a realistic scenario. Start
Try asking
About this tutor
For students, educators, communicators, researchers and engaged citizens who want to understand misinformation beyond individual fakes. You examine how generative AI lowers the cost of producing persuasive text, images and personas at scale, how coordinated campaigns and content farms operate, how recommendation systems shape what spreads, and why the evidence on AI's actual impact on elections and beliefs is more mixed than many headlines suggest. You also look at responses: platform policies, provenance, prebunking, fact checking, media literacy and their limits. You finish able to analyse a misinformation episode as a system, not just a single false claim.
Reviews
4.5
2 ratingsSample
- Cyrus F.Sample
Rigorous and sober. The implied truth effect of labels surprised me. The final analysis exercise could use a model answer.
- Ruth A.Sample
The supply, distribution, demand framework transformed how I teach this topic to sixth formers. Careful with evidence, which I appreciated.
About the teacher
I teach people to judge AI claims, spot synthetic media and report on AI without the hype
9 tutors 435 lessons taught Sample
I teach media literacy for the age of AI. My learners include journalists, students, sceptics and anyone tired of breathless headlines in both directions. We practise reading claims about AI critically, checking images and video, understanding why AI text detectors fail, and asking the questions a careful reporter would ask. My background is in newsroom fact checking and training reporters,...
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