The Program for International Student Assessment (PISA), coordinated by the OECD, is preparing to introduce the competency known as Media and Artificial Intelligence Literacy (MAIL).

This represents a significant conceptual shift: contemporary literacy can no longer be understood solely as mastery of traditional reading, writing, and mathematics. In societies structured around digital platforms, personalized information flows, and systems that operate through probabilistic inference, understanding how information is organized, filtered, and recommended has become an integral part of basic education.

This article provides an analytical overview of what is currently known about the skills typically assessed by the MAIL, linking the media and algorithmic dimensions of this competency. I have also developed a chatbot to answer questions on this topic. Click here to access it.

What does “assessed skills” mean in the context of PISA?

PISA does not assess the memorization of subject-specific content. According to the OECD, competence involves applying knowledge in complex situations, using contextualized reasoning, and justifying decisions.

In the case of MAIL, the following is not evaluated:

• Technical proficiency in software;
• Ability to program;
• Professional training in artificial intelligence.

The following observable cognitive processes are assessed:

• Interpret a personalized feed and explain why certain content is featured;
• Analyze the reliability of a digital news story by identifying its authorship, evidence, and argumentative coherence;
• Recognize that automated recommendations are the result of modeling based on behavioral data;
• Explain how engagement criteria or interaction history influence content visibility.

The focus is on a well-reasoned explanation and a structural understanding of the informational environment.

The Media Dimension: Interpreting, Analyzing, and Evaluating Information

The media dimension interacts with the UNESCO Media and Information Literacy Framework , which emphasizes access, analysis, critical evaluation, and responsible production of information.

In the context of MAIL, this dimension involves skills such as:

• Identify authorship and intent;
• Recognize narrative frameworks and persuasion strategies;
• Assess the credibility of sources;
• Compare differing versions of the same event.

Example: When presented with two news reports on a political event, the student must identify differences in framing, examine the evidence, and explain which report demonstrates greater informational consistency. In another scenario, the student may be asked to distinguish a news report from disguised native advertising by identifying signs of sponsorship.

The innovation lies in the fact that these messages do not circulate in isolation, but within digital ecosystems that have been structured in advance by algorithms.

The Algorithmic Axis: Understanding Systems That Infer

The algorithmic dimension, as outlined by the OECD in *Empowering Learners for the Age of AI* (AILit Framework), involves understanding how computational systems make inferences based on data.

Core skills include:

• Understanding automated personalization;
• Recognize ranking and recommendation criteria;
• Understand which models operate based on probabilistic inference;
• Identify potential biases arising from training data.

Example: After searching for a specific topic, the student begins to receive related content. The expected skill is to explain this pattern as the result of predictive modeling based on past behavior. Another example: when analyzing a system that classifies individuals as “high risk,” the student must recognize that this is a statistical estimate, not an absolute deterministic decision.

Here, the focus is on a conceptual understanding of inferential logic, not technical proficiency.

The Synergy Between Media and Algorithms

The core of MAIL lies in the intersection between discourse analysis and an understanding of infrastructure. UNESCO had already indicated, in the Global MIL Assessment Framework, that literacy should be understood in a systemic way. MAIL builds on this approach by explicitly incorporating the inferential dimension of algorithmic systems described by the OECD.

Synergy occurs when the student simultaneously draws upon two levels of analysis:

• The discursive layer:examining arguments, identifying intent, and evaluating evidence.
• The infrastructure layer:understanding that the visibility of information is influenced by data-driven ranking, personalization, and optimization processes.

Integrated example: Upon realizing that their feed is dominated by content with a certain ideological orientation, students critically analyze the arguments presented (media dimension), but also recognize that this recurrence may result from previous interactions, algorithmic targeting, and engagement metrics (algorithmic dimension).

When conducting an online search, he assesses the reliability of sources and, at the same time, questions the order in which results are presented, understanding that commercial criteria or calculated relevance may influence the ranking.

This integration constitutes a broader form of sociotechnical literacy.

What's Not at Stake

The introduction of MAIL does not require technical training in programming or software engineering. PISA does not assess technological expertise.

What's at stake is:

• Cognitive autonomy in the face of automated information environments;
• Critical understanding of algorithmic mediation;
• Ability to discuss the social implications of these systems.

Educational Implications

The incorporation of MAIL does not require a new course. It requires curricular integration.

• In Languages: A Critical Analysis of Personalized Digital Environments.
• In Mathematics: a basic understanding of probability and inference.
• In the Humanities: a discussion of the social and political impacts of algorithmic mediation.

Teacher training is central to this effort to update critical literacy.

MAIL and BNCC

The BNCC recognizes digital literacy as a general competency in Basic Education. However, it operates at the normative level. MAIL operates at the assessment level.

While the BNCC emphasizes the need for digital critical thinking, the MAIL outlines the cognitive processes that make it observable, including:

• Understanding algorithmic personalization;
• Probabilistic inference recognition;
• Analysis of visibility and recommendation criteria;
• Explanation of the social implications of these mechanisms.

There is no break, but rather an operational deepening.

Conclusion

The introduction of MAIL in PISA 2029 redefines literacy for complex digital societies. Information does not circulate in a neutral manner; it is structured by data-driven automated systems.

Assessing these skills means evaluating the student's ability to critically understand both the content and the infrastructure that organizes its visibility.

It is less about technical proficiency and more about cognitive autonomy in socio-technical environments.

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References:

OECD. PISA 2022 Assessment and Analytical Framework. Paris: OECD Publishing, 2023.

OECD. PISA 2018 Global Competence Framework. Paris: OECD Publishing, 2018.

OECD. Empowering Learners for the Age of Artificial Intelligence. Paris: OECD Publishing, 2023.

OECD. OECD Digital Education Outlook 2023: Toward an Effective Digital Education Ecosystem. Paris: OECD Publishing, 2023.

OECD. Artificial Intelligence in Society. Paris: OECD Publishing, 2019.

UNESCO. Media and Information Literacy: Policy and Strategy Guidelines. Paris: UNESCO, 2013.

UNESCO. Global Media and Information Literacy Assessment Framework: Country Readiness and Competencies. Paris: UNESCO, 2013.

UNESCO. Media and Information Literacy Curriculum for Teachers. Paris: UNESCO, 2011.

BRAZIL. Ministry of Education. National Common Core Curriculum (BNCC). Brasília: MEC, 2018.

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