Fake news is no longer only a problem of false claims. In today’s digital public sphere, misinformation spreads because social media platforms and AI-driven systems decide what people see, how often they see it, and which messages are rewarded with visibility. For students, young people, educators and professionals working with the public, this changes the meaning of media literacy. It is no longer enough to ask whether a piece of content is true or false. The more urgent question is how that content reached the user, why it was recommended, and what kind of emotional or political reaction it is designed to trigger.

This challenge is at the center of current debates on AI in the media and the algorithmic amplification of fake news and misinformation. Recommender systems are built to maximize engagement, not necessarily accuracy, democratic participation or public understanding. In public discussions on digital rights and youth, online platforms are private environments governed by questionable rules, business models and algorithmic choices. If users are not paying for the service, their attention and data become part of the product. That makes young people especially vulnerable to targeted narratives, polarizing content and harmful messages that may not clearly break the law but still damage trust, well-being and democratic debate.

The most difficult content to address is often not the most obviously illegal. Hate speech, explicit manipulation or clearly false claims can sometimes be reported, fact-checked or removed. But much of the content shaping political attitudes exists in a grey zone: memes, jokes, emotionally charged opinions, misleading framings, AI-generated visuals or short videos that are offensive, divisive or conspiratorial without being easy to classify as illegal misinformation. This is why the public debate on media literacy must move beyond traditional fact-checking and towards system literacy: an understanding of platforms, algorithms, targeting, synthetic media and the incentives that make some narratives travel faster than others.

A concrete good-practice example of this shift can be found in Europeans Against Fake News (EAF), a European initiative coordinated by Connect International. EAF should not be seen simply as another awareness campaign about disinformation. Its value lies in the method it tested: Youth Community Hackathons, a participatory learning format that brings young people together with media, policy and digital-rights experts to investigate real misinformation cases and transform their analysis into public-facing outputs.

The practice is concrete and verifiable. EAF worked with selected disinformation cases related to fundamental rights and COVID-19, climate change, migration and euroscepticism. Across five events, including in-person and online formats, participants examined how false or misleading narratives are built, why they appeal to audiences, and how they can be challenged. Instead of ending the learning process with a lecture or a quiz, the hackathon model asked young people to create their own responses: escape-room games, podcasts, flash mobs, memes and a virtual exhibition. In this way, participants were not treated only as consumers of media literacy education, but as active civic communicators.

AI and recommender systems do not only produce or distribute false information; they shape the conditions under which public attention is organized. A pedagogical response therefore must help learners understand both content and infrastructure. EAF’s approach gives educators and youth workers a model for doing this in practice. Participants can start with a real case of misinformation, identify the narrative techniques used, discuss how algorithmic ranking or personalization may increase its reach, and then design an intervention that speaks to their peers in a language they understand.

For higher-education teachers, EAF offers a transferable classroom structure. Students could work in teams to analyze a misinformation case, map the role of AI-generated or algorithmically amplified content, consult expert sources, and produce a communication output targeting a defined audience. Assessment could focus not only on whether students identify misinformation, but also on whether they understand amplification, emotional framing, platform incentives and ethical communication.

For young people, this method is relevant because it recognizes the reality of their online lives. Many young users do not encounter information’s through long articles or official statements, but through short videos, memes, influencer commentary and platform recommendations. EAF’s participatory format meets them in that environment without blaming them for it. It helps them ask practical questions: Why am I seeing this? Who benefits from this message spreading? Is this content trying to inform me, provoke me, frighten me or mobilize me? Could it be AI-generated? Is it legal but still harmful? What would a responsible response look like? These are all the questions that we should ask ourselves to make sure that algorithms don’t influence way we see the world.

For professionals and civil-society organizations, the lesson is equally important. We in Connect International believe that digital literacy alone cannot solve the problem if platforms continue to be designed around addictive engagement and opaque algorithmic systems. Young people need skills, but institutions also need transparency, accountability, human oversight and stronger attention to mental well-being. A media literacy workshop that ignores platform design risks placing too much responsibility on individual users and too little on the systems that shape the information environment.

The knowledge-transfer value of EAF is therefore not limited to its specific events. What can be learned from it is a repeatable approach: choose real and relevant misinformation cases; analyze both the message and the system that spreads it; bring together young people, educators and experts; use collaborative production instead of passive instruction; and create outputs that can be shared with the wider community. This model can be adapted by universities, NGOs, youth centers, schools, public libraries, journalism programs and digital-competence initiatives.

The broader topic is urgent because AI will make misinformation cheaper, faster and more persuasive. Synthetic images, automated text, deepfakes and personalized political messages will increasingly test the limits of traditional education. The answer is not panic, but a more ambitious form of media literacy: one that combines critical thinking, AI literacy, civic engagement, creativity and democratic responsibility.

EAF provides a useful example of how this can be done. It shows that young people can move from being targets of algorithmic amplification to becoming analysts, creators and defenders of healthier public debate. The best practice is not only the project itself, but the principle behind it: media literacy education must become active, collaborative and system-aware if it is to respond to the realities of AI-driven misinformation.

Find out more at: https://europeansagainstfake.news/ 

Author: Jelena Spasovic, General Manager, Connect International