Report on the workshop led by Média Animation and the CSEM as part of the 2026 Media Literacy Professional Conference in Brussels, Belgium.
On September 24, Média Animation and the CSEM collaborated to create and lead a workshop during the Media Literacy Professional Conference. It was an opportunity to highlight and bring to life a year’s worth of work centred on the key question: “Is AI redefining the challenges of misinformation?”
Throughout the 2025–2026 academic year, the two organizations organized a series of webinars designed to bring together researchers and media education professionals. Each session resulted in a tool sheet that highlighted the speaker’s contributions from a specific perspective and suggested teaching ideas; these sheets were then used as input for the final workshop.
With concrete examples of content generated by artificial intelligence, the workshop aimed to explore the new challenges these technologies generate for media literacy education. From AI-generated search engine results to AI-generated journalistic content, the professionals in attendance were able to discuss the educational responses needed to address these new uses.
The work was organized into subgroups, each focused on a specific type of content or use. The first step was to explore and discuss these topics to identify the educational issues they raise; the next step was to select one or more of these issues and outline potential educational responses.
Underlying the discussions was a single question: Does AI raise truly new educational challenges, or does it simply give a new form to challenges we already face?
AI and journalistic content
The group examined two examples in which media professionals use AI-generated images: anonymous testimonials illustrated with such images, and documentary footage providing a narrative of the Bois du Cazier disaster.
In the case of anonymous testimonials, the group primarily raises the question of the added value of AI-generated images compared to other techniques (blurring, filmed hands, etc.), which could open the door to representational bias, or even exploitation, at the risk of discriminating.
In the second case, concerning the Bois du Cazier disaster, the group examines the process of fictionalization and the difficulty of distinguishing narrative from the factual role inherent in journalism, a tension that also exists in documentary filmmaking, particularly in reenactments. However, the use of AI exacerbates this confusion by further blurring the line between fact and fiction. Ultimately, the group highlights the risk that these images could be perceived as authentic archival material, thereby reinforcing the confusion between historical reconstruction and reality.
A layer of AI is here added to the document which introduces a certain tension: We’re constantly forced to rely on our own judgment, but shouldn’t the journalistic code of ethics be something obvious, clear, and straightforward? We pile rhetoric upon rhetoric, along with falsehoods, embellishments, and so on. Perhaps journalism should guard against these kinds of elements and avoid blurring the lines between genres? Doesn’t this type of documentary risk create shared but erroneous visual references?
The AI production could provide further insight into the choices that were made. Ideally, this could be done by presenting a “making-of” segment for the documentary to highlight the decisions made such as colour grading, prompts, and so on. In other words, making the use of AI more transparent. The goal, therefore, is to examine the nature of the images (archival footage, reenactments, etc.), the choices made, the documentary framework, and the relationship to visual references.
AI and ambiguous contracts
Parody, satire, activism, fiction… media outlets that borrow journalistic conventions to create other types of content are nothing new. But when AI tools are used to produce content that resembles news without being journalistic, what questions does this raise?
This example sparked an initial wave of questions among participants, focusing primarily on two issues: political and informational concerns. Does this content carry political agendas, or is there a risk that it could be co-opted for such purposes? Should traditional journalism reevaluate itself considering the success of so-called “alternative” news? How can we verify the accuracy of the information and the intentions of its creators? What responsibility do platforms bear for the visibility they give to this content?
Following these discussions, the group developed a methodology for analysing journalistic content—or content that follows journalistic conventions—without limiting itself to questions of truth or fake. The goal here is to develop analytical and fact-checking skills, but also to examine the very concept of information. This tool would take the form of a question tree or a fun investigative process that could be used with different types of content (video, text, etc.) and adapted to audiences of different ages.
When presented with a piece of content, participants would be guided to ask themselves a series of questions: What am I looking at? Who is behind this content? Where is it published? Does it look like journalistic content? What information helps identify its source? Are any sources cited? What seems to be the intention? How can I verify it or compare it to other sources?
In addition, the group identifies two possible approaches. One focuses on reception, placing participants in the role of investigators: observing before drawing conclusions, identifying the author or sender, researching the context, comparing content, and verifying certain information. The other focuses on the production side to understand that producing content presented as informative also entails certain responsibilities: identifying its source, providing context, citing sources, and giving the audience enough information to understand what they are watching.
The example analysed here ultimately shows that the emergence of AI-generated audiovisual content, which borrows the conventions of journalistic reporting, does not fundamentally alter the skills that need to be developed in media and information literacy. In this case, the primary goal is to foster a critical approach to any content that presents itself as or resembles news.
AI and historic representations
Seeing the world through the eyes of a soldier during a war, Cleopatra on the day of her death, or even a person enslaved in ancient times—these are the possibilities offered by AI-generated images. Between reality, fiction, and entertainment, do these posts shared on social media shed light on new issues?
The participants were not familiar with this type of content: they were able to freely browse all videos of this type directly on TikTok. Their initial reaction was to dismiss this content based on its perceived lack of quality: Do people really watch this? Why? They first drew a clear distinction between this content—which they deemed to be of lower quality—and traditional documentary or literary formats.
Once this initial exploration is complete, participants examine various video clips and reflect on their intentions, particularly their entertainment, historical, or economic aspects. They highlight the speed and volume of their content production, which they attribute primarily to economic motivations. Finally, an analysis of the comments shows that internet users are primarily concerned with the accuracy of the historical facts presented, rather than with how the content is produced, specifically, whether or not AI is used.
Rather than analysing AI-generated content on its own merits, the participants decided to use it as a tool to explore historical content and issues related to the representation of history to young people, their shared public. The group has created a three-part activity: first, to analyse the content and its portrayal of history through a “spot the seven historical errors” game. Next, to put this content into context and show that this way of interpreting history is not unique to AI. Finally, work with the young people to create a POV video, drawing on other sources to develop the script. This final step would also help build a sense of community around a shared goal and raise awareness among the young people about the work and time required to create a video.
AI and online research
Search engines very quickly began incorporating AI-generated answers into their operations. Often without realizing it, users found themselves presented with information in a different format.
These tools have become an integral part of our daily lives and, according to the group, raise numerous issues: the relationship with authority, lack of transparency in how they operate, their impact on research practices, their business model, and their relationship with sources…
Although the group’s initial reaction was to assume that these issues primarily affect young people, it quickly realized that they concern all users, regardless of their age or level of digital literacy. The discussions highlighted the opacity of these tools, whose inner workings are unclear to all audiences, including media literacy professionals.
To develop its educational tool, the group therefore chose to target all search engine users. It identified the issue of authority in the generated results as a priority, which would allow for a broader exploration of issues such as affordances, confirmation bias, plurality, and personalization.
After considering several possibilities, the group realized that the thought process they had just gone through (testing, asking questions, gradually discovering the issues at stake…) is an activity structure in and of itself. The concept chosen for a media literacy activity would therefore be to have the audience experience this same type of guided exploration: letting them experiment, grapple with their own questions, and discover the technology as they ask those questions, rather than explaining how it works from the outset.
Conclusion
Ultimately, does AI require us to develop a new form of critical information literacy, or rather to strengthen and adapt the one we already practice? In the activities suggested above, groups generally return to the fundamentals of media literacy: identifying sources, contextualizing content, questioning their intentions, and understanding the mechanisms by which information gains credibility and circulates. Artificial intelligence tools, while they raise new societal, political, and environmental challenges, do not appear to fundamentally change the skills needed to combat misinformation.
What AI tools are changing today may therefore be less about the emergence of entirely new mechanisms and more about the amplification of mechanisms already present in the information ecosystem. AI accelerates and facilitates the production, transformation, and circulation of content, while at the same time making issues of source, intent, form, and context more complex. The challenge for critical information literacy is therefore not necessarily to develop new skills, but to enable people to apply their existing skills in environments where traditional points of reference are less stable. The debate, then, centres more on the new conditions surrounding the production, access, and circulation of information.