9/06/2026
How can media literacy strategies help safeguard citizens in an AI-driven information ecosystem? How have social media algorithms transformed the way information spreads? How can we ensure that AI aligns with human values and ethical principles? What does it mean to use AI responsibly? We explored these questions, among others, in the latest series of the EAVI Conversations podcast.
In this article, we share some of the key lessons that emerged from our conversations with experts. From questioning the information we encounter online and reflecting critically on our use of AI, to defending human agency and having the courage to reject systems that do not genuinely benefit our societies, these insights offer practical guidance for navigating the opportunities and challenges of the AI age.
1. Ask Questions About the Information You See
As Artemiza-Tatiana Chisca explains, media and information literacy helps us make sense of the environment we are immersed in. Is the information I see reliable? Where does it come from? Do I need to look elsewhere to know more? Asking these questions helps us approach information with a critical eye, form our own opinions, and participate meaningfully in public life and democratic processes.
2. We All Have a Role to Play: Find Yours
As Julia Haas puts it, we need to think about what we can do from our own perspective, whether as members of an international organisation, academia, civil society, or simply as individuals, consumers, or citizens. We need to ask ourselves: where can I contribute to strengthening information integrity, media freedom, and freedom of expression? We must also encourage others to do the same. This requires collaboration and a broad, coordinated effort across sectors.
3. AI Is Fragmenting Our Shared Informational Space: We Need More Dialogue
As Murielle Popa-Fabre points out, we have moved from a paradigm in which one news source reached a shared public space to one made up of many personalised spaces, where we no longer share the same informational environment. She describes this as an “audience of one” model that fragments society. Think, for example, of Netflix generating a TV series just for you: when you meet your colleagues, you can no longer share your disappointment about a character’s choices because there is no common cultural reference point. Without shared experiences, we lose part of the shared dialogue that democracy is built on.
4. Stay Curious: Question AI and How You Use It
Virginia Dignum argues that the most important thing is to remain inquisitive and keep asking ourselves and others: why are we doing what we are doing? Why are we accepting certain systems or tools as they are? We need to educate ourselves about what AI actually is, recognising both its potential and its limits, and understanding that it is not a solution to all our problems.
5. We Must Have the Courage to Refuse Some Systems, and Demand Alternatives
As Virginia Dignum further observes, we all need the courage to acknowledge that we are using systems that may not be the most suitable or desirable ones. This is not only because their outputs are not always as correct or precise as we would like, but also because, by using them, we contribute to some of the problems AI creates. We need to accept that many of these systems are not transparent or inclusive, and that they are biased, depend on exploitative labour practices, and consume natural resources that might be better reserved for purposes more important than interactions with a chatbot. The challenge, then, is to understand what we are doing and, if we want to be even more courageous, to refuse certain systems or demand alternatives to those we have today.
6. Push Back Against the Narrative That AI Will Replace You
As Lee Hibbard stresses, we need to push back against narratives of replacement. We need to empower people and give them the confidence to say: “that’s not my narrative”.
7. Learn by Doing: Start Experimenting with AI Tools
As Giovanni Spitale and Federico Germani suggest, a fundamental starting point is developing awareness of what these tools actually are and how they function. They advocate learning by doing and working through trial and error as valuable ways to build a practical understanding of these systems, especially given that the cost of a mistake is relatively low, as long as users are aware they are in an experimental process. Crucially, this does not require a full university course. Even basic prompt engineering, for example comparing prompt A with prompt B and observing differences in the output, can provide a meaningful foundation for understanding how these systems operate.
8. Critical Thinking Goes Beyond What AI Predicts for You
As Paolo Celot and Alexandre Le Voci Sayad reflect, we need to break free from purely probabilistic patterns and continue trying new things. As Paolo Celot illustrates, imagine going to buy an ice cream and asking for strawberry and chocolate every day: after a few days, you are handed one without even having to ask, simply because it has become predictable. We have transitioned from systems that consistently produced the same outputs from the same inputs to probabilistic models that generate different outcomes by combining and interpreting data in new ways. As Alexandre Le Voci Sayad adds, if we always receive what is predicted for us, we never have the chance to try vanilla ice cream with blueberry sauce. This matters because, when we delegate precise research to a probabilistic model such as an LLM, we should not expect perfectly precise results.
9. We Should Not Delegate Our Critical Thinking to AI
As Elizabeth Milovidov emphasises, the skills that make us more resilient today will continue to serve us well in the future. Relying on a computer to solve every problem for us is not sufficient: parents and educators must ensure that children understand that learning is not simply about taking shortcuts, and that critical thinking remains essential for participating fully in society.
Related Posts
9/06/2026
How can media literacy strategies help safeguard citizens in an AI-driven information ecosystem? How have social media algorithms transformed the way information spreads? How can we ensure that AI aligns with human values and ethical principles? What does it mean to use AI responsibly? We explored these questions, among others, in the latest series of the EAVI Conversations podcast.
In this article, we share some of the key lessons that emerged from our conversations with experts. From questioning the information we encounter online and reflecting critically on our use of AI, to defending human agency and having the courage to reject systems that do not genuinely benefit our societies, these insights offer practical guidance for navigating the opportunities and challenges of the AI age.
1. Ask Questions About the Information You See
As Artemiza-Tatiana Chisca explains, media and information literacy helps us make sense of the environment we are immersed in. Is the information I see reliable? Where does it come from? Do I need to look elsewhere to know more? Asking these questions helps us approach information with a critical eye, form our own opinions, and participate meaningfully in public life and democratic processes.
2. We All Have a Role to Play: Find Yours
As Julia Haas puts it, we need to think about what we can do from our own perspective, whether as members of an international organisation, academia, civil society, or simply as individuals, consumers, or citizens. We need to ask ourselves: where can I contribute to strengthening information integrity, media freedom, and freedom of expression? We must also encourage others to do the same. This requires collaboration and a broad, coordinated effort across sectors.
3. AI Is Fragmenting Our Shared Informational Space: We Need More Dialogue
As Murielle Popa-Fabre points out, we have moved from a paradigm in which one news source reached a shared public space to one made up of many personalised spaces, where we no longer share the same informational environment. She describes this as an “audience of one” model that fragments society. Think, for example, of Netflix generating a TV series just for you: when you meet your colleagues, you can no longer share your disappointment about a character’s choices because there is no common cultural reference point. Without shared experiences, we lose part of the shared dialogue that democracy is built on.
4. Stay Curious: Question AI and How You Use It
Virginia Dignum argues that the most important thing is to remain inquisitive and keep asking ourselves and others: why are we doing what we are doing? Why are we accepting certain systems or tools as they are? We need to educate ourselves about what AI actually is, recognising both its potential and its limits, and understanding that it is not a solution to all our problems.
5. We Must Have the Courage to Refuse Some Systems, and Demand Alternatives
As Virginia Dignum further observes, we all need the courage to acknowledge that we are using systems that may not be the most suitable or desirable ones. This is not only because their outputs are not always as correct or precise as we would like, but also because, by using them, we contribute to some of the problems AI creates. We need to accept that many of these systems are not transparent or inclusive, and that they are biased, depend on exploitative labour practices, and consume natural resources that might be better reserved for purposes more important than interactions with a chatbot. The challenge, then, is to understand what we are doing and, if we want to be even more courageous, to refuse certain systems or demand alternatives to those we have today.
6. Push Back Against the Narrative That AI Will Replace You
As Lee Hibbard stresses, we need to push back against narratives of replacement. We need to empower people and give them the confidence to say: “that’s not my narrative”.
7. Learn by Doing: Start Experimenting with AI Tools
As Giovanni Spitale and Federico Germani suggest, a fundamental starting point is developing awareness of what these tools actually are and how they function. They advocate learning by doing and working through trial and error as valuable ways to build a practical understanding of these systems, especially given that the cost of a mistake is relatively low, as long as users are aware they are in an experimental process. Crucially, this does not require a full university course. Even basic prompt engineering, for example comparing prompt A with prompt B and observing differences in the output, can provide a meaningful foundation for understanding how these systems operate.
8. Critical Thinking Goes Beyond What AI Predicts for You
As Paolo Celot and Alexandre Le Voci Sayad reflect, we need to break free from purely probabilistic patterns and continue trying new things. As Paolo Celot illustrates, imagine going to buy an ice cream and asking for strawberry and chocolate every day: after a few days, you are handed one without even having to ask, simply because it has become predictable. We have transitioned from systems that consistently produced the same outputs from the same inputs to probabilistic models that generate different outcomes by combining and interpreting data in new ways. As Alexandre Le Voci Sayad adds, if we always receive what is predicted for us, we never have the chance to try vanilla ice cream with blueberry sauce. This matters because, when we delegate precise research to a probabilistic model such as an LLM, we should not expect perfectly precise results.
9. We Should Not Delegate Our Critical Thinking to AI
As Elizabeth Milovidov emphasises, the skills that make us more resilient today will continue to serve us well in the future. Relying on a computer to solve every problem for us is not sufficient: parents and educators must ensure that children understand that learning is not simply about taking shortcuts, and that critical thinking remains essential for participating fully in society.
Related Posts
9/06/2026
How can media literacy strategies help safeguard citizens in an AI-driven information ecosystem? How have social media algorithms transformed the way information spreads? How can we ensure that AI aligns with human values and ethical principles? What does it mean to use AI responsibly? We explored these questions, among others, in the latest series of the EAVI Conversations podcast.
In this article, we share some of the key lessons that emerged from our conversations with experts. From questioning the information we encounter online and reflecting critically on our use of AI, to defending human agency and having the courage to reject systems that do not genuinely benefit our societies, these insights offer practical guidance for navigating the opportunities and challenges of the AI age.
1. Ask Questions About the Information You See
As Artemiza-Tatiana Chisca explains, media and information literacy helps us make sense of the environment we are immersed in. Is the information I see reliable? Where does it come from? Do I need to look elsewhere to know more? Asking these questions helps us approach information with a critical eye, form our own opinions, and participate meaningfully in public life and democratic processes.
2. We All Have a Role to Play: Find Yours
As Julia Haas puts it, we need to think about what we can do from our own perspective, whether as members of an international organisation, academia, civil society, or simply as individuals, consumers, or citizens. We need to ask ourselves: where can I contribute to strengthening information integrity, media freedom, and freedom of expression? We must also encourage others to do the same. This requires collaboration and a broad, coordinated effort across sectors.
3. AI Is Fragmenting Our Shared Informational Space: We Need More Dialogue
As Murielle Popa-Fabre points out, we have moved from a paradigm in which one news source reached a shared public space to one made up of many personalised spaces, where we no longer share the same informational environment. She describes this as an “audience of one” model that fragments society. Think, for example, of Netflix generating a TV series just for you: when you meet your colleagues, you can no longer share your disappointment about a character’s choices because there is no common cultural reference point. Without shared experiences, we lose part of the shared dialogue that democracy is built on.
4. Stay Curious: Question AI and How You Use It
Virginia Dignum argues that the most important thing is to remain inquisitive and keep asking ourselves and others: why are we doing what we are doing? Why are we accepting certain systems or tools as they are? We need to educate ourselves about what AI actually is, recognising both its potential and its limits, and understanding that it is not a solution to all our problems.
5. We Must Have the Courage to Refuse Some Systems, and Demand Alternatives
As Virginia Dignum further observes, we all need the courage to acknowledge that we are using systems that may not be the most suitable or desirable ones. This is not only because their outputs are not always as correct or precise as we would like, but also because, by using them, we contribute to some of the problems AI creates. We need to accept that many of these systems are not transparent or inclusive, and that they are biased, depend on exploitative labour practices, and consume natural resources that might be better reserved for purposes more important than interactions with a chatbot. The challenge, then, is to understand what we are doing and, if we want to be even more courageous, to refuse certain systems or demand alternatives to those we have today.
6. Push Back Against the Narrative That AI Will Replace You
As Lee Hibbard stresses, we need to push back against narratives of replacement. We need to empower people and give them the confidence to say: “that’s not my narrative”.
7. Learn by Doing: Start Experimenting with AI Tools
As Giovanni Spitale and Federico Germani suggest, a fundamental starting point is developing awareness of what these tools actually are and how they function. They advocate learning by doing and working through trial and error as valuable ways to build a practical understanding of these systems, especially given that the cost of a mistake is relatively low, as long as users are aware they are in an experimental process. Crucially, this does not require a full university course. Even basic prompt engineering, for example comparing prompt A with prompt B and observing differences in the output, can provide a meaningful foundation for understanding how these systems operate.
8. Critical Thinking Goes Beyond What AI Predicts for You
As Paolo Celot and Alexandre Le Voci Sayad reflect, we need to break free from purely probabilistic patterns and continue trying new things. As Paolo Celot illustrates, imagine going to buy an ice cream and asking for strawberry and chocolate every day: after a few days, you are handed one without even having to ask, simply because it has become predictable. We have transitioned from systems that consistently produced the same outputs from the same inputs to probabilistic models that generate different outcomes by combining and interpreting data in new ways. As Alexandre Le Voci Sayad adds, if we always receive what is predicted for us, we never have the chance to try vanilla ice cream with blueberry sauce. This matters because, when we delegate precise research to a probabilistic model such as an LLM, we should not expect perfectly precise results.
9. We Should Not Delegate Our Critical Thinking to AI
As Elizabeth Milovidov emphasises, the skills that make us more resilient today will continue to serve us well in the future. Relying on a computer to solve every problem for us is not sufficient: parents and educators must ensure that children understand that learning is not simply about taking shortcuts, and that critical thinking remains essential for participating fully in society.
