{"id":80350,"date":"2026-05-22T00:47:11","date_gmt":"2026-05-22T04:47:11","guid":{"rendered":"https:\/\/world.edu\/?p=80350"},"modified":"2026-05-22T00:47:11","modified_gmt":"2026-05-22T04:47:11","slug":"why-understanding-ai-will-be-as-important-as-learning-to-read","status":"publish","type":"post","link":"https:\/\/world.edu\/why-understanding-ai-will-be-as-important-as-learning-to-read\/","title":{"rendered":"Why understanding AI will be as important as learning to read"},"content":{"rendered":"

\"\" \"\"<\/span><\/p>\n

For years, media literacy has been considered\u00a0<\/span>an essential skill<\/span><\/a>\u00a0for navigating digital environments. It involves learning to identify reliable sources, verify information, and detect misleading content.<\/span><\/p>\n

However, in a context marked by the expansion of artificial intelligence in the processes of access, production and circulation of knowledge, the information ecosystem has been transformed and the aforementioned skills are not sufficient.<\/span><\/p>\n

Traditionally, users interacted with relatively identifiable content, produced by recognizable sources and under more or less transparent editorial guidelines. Today, that relationship has changed substantially. Increasingly, users do not access information that they must interpret, but rather interact with systems that synthesize, reorganize, and generate it in real time.<\/span><\/p>\n

Why does ChatGPT respond the way it does?<\/span><\/strong><\/h4>\n

For example, a few years ago, someone who wanted information about vaccines, mental health, or healthy eating could read news on various digital platforms, consult scientific articles, or compare expert opinions. Critical thinking involved evaluating who produced the information, through which medium it was disseminated, and with what intention.<\/span><\/p>\n

Today, that same user can directly ask ChatGPT or another artificial intelligence system: \u201cAre vaccines safe?\u201d, \u201cHow do I know if I have anxiety?\u201d, or \u201cWhat diet is best for me?\u201d. In a few seconds, they receive a clear, structured, and seemingly reliable answer. However, they often don’t know what sources the system used, what information it prioritized, what data it omitted, or what biases might influence the generated response.<\/span><\/p>\n

The difference<\/strong> is profound: previously, critical thinking focused primarily on content; now it must also address the system that produces and organizes knowledge. In fact,\u00a0<\/span>recent research<\/span><\/a>\u00a0warns that the growing reliance on artificial intelligence systems may alter how people evaluate information and make decisions, especially in sensitive areas such as health and well-being.<\/span><\/p>\n

An uncritical use of AI<\/span><\/h4>\n

We know that the use of AI\u00a0<\/span>improves efficiency in content production<\/span><\/a>\u00a0, but it also tends to shift critical judgment towards trust in the system, especially when the results are presented in a coherent and plausible way.<\/span><\/p>\n

This phenomenon extends beyond the educational sphere: the rapid adoption of these technologies is\u00a0<\/span>transforming the dynamics of access to information<\/span><\/a>\u00a0,\u00a0<\/span>generating new challenges<\/span><\/a>\u00a0in terms of transparency, equity and knowledge governance.<\/span><\/p>\n

Understanding algorithmic mediation<\/span><\/strong><\/h4>\n

Artificial intelligence not only facilitates access to information, but also actively intervenes in its construction. This is no small change. It represents a shift from a model based on content interpretation to one in which algorithmic mediation plays a central role. In this context, the source can become more diffuse, authorship less visible, and the logic of knowledge production more opaque to users.<\/span><\/p>\n

Therefore, understanding algorithmic systems is as relevant\u00a0<\/span>as evaluating content<\/span><\/a>\u00a0, and digital and media literacy must include artificial intelligence literacy, an emerging field that integrates technical, critical, and ethical dimensions.<\/span><\/p>\n

What is artificial intelligence literacy?<\/span><\/strong><\/h4>\n

This literacy goes beyond simply knowing how to use tools like ChatGPT, Gemini, or Copilot. It’s not just about learning to write better instructions or getting faster responses, but about understanding how these systems produce information, what their limitations are, and what social, ethical, and cognitive implications they can generate.<\/span><\/p>\n

In practical terms, a person literate in AI should be able to understand, at least in a basic way, how algorithmic systems work, what role data plays in generating answers, why biases or errors may appear, and how automation influences the way we interpret reality and make decisions.<\/span><\/p>\n

New critical competencies<\/span><\/strong><\/h4>\n

This involves developing new critical skills: questioning the apparent neutrality of AI-generated responses, identifying when a response requires further verification, recognizing the risks of over-delegating thinking to automated systems, and understanding that these technologies do not “think,” but rather produce results based on patterns and probabilities.<\/span><\/p>\n

Currently, there is a gap between these transformations and educational practices. While the goal is for students to be able to analyze content and develop media and information literacy skills, as promoted by organizations such as\u00a0<\/span>UNESCO<\/span><\/a>\u00a0and educational frameworks linked to digital competence, they are not always being provided with the tools to understand\u00a0<\/span>the processes by which that content is generated<\/span><\/a>\u00a0.<\/span><\/p>\n

How knowledge is built<\/span><\/strong><\/h4>\n

What is at stake is no longer just digital competence, but the ability of societies to understand who organizes, prioritizes, and legitimizes knowledge in increasingly automated environments.<\/span><\/p>\n

Individuals may believe they are making fully informed decisions when, in reality, they are relying on systems whose internal logic they do not understand. This not only affects how we consume information but also societies’ ability to participate critically in areas such as health, politics, and public communication<\/span><\/p>\n

For citizens to achieve this AI literacy, learning should begin progressively from school and be integrated across different educational stages and civic training programs, not only from technological areas, but also from subjects related to communication, ethics, social sciences and digital citizenship.<\/span><\/p>\n

The adult population also needs training and outreach spaces that allow them to critically understand how these technologies work, especially in sensitive areas such as health, political information, or education.<\/span><\/p>\n

The responsibility doesn’t lie solely with education systems. Governments, universities, media outlets, technology platforms, and international organizations also have a key role to play in developing a citizenry capable of critically engaging with artificial intelligence. In a context where algorithms are increasingly involved in shaping what we see, think, and believe we know, understanding how they work is no longer a specialized skill but a democratic imperative.<\/span><\/p>\n

Author Bio:<\/strong> Barbara Castillo Abdul is Lecturer and Researcher at UDIT – University of Design, Innovation and Technology<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

For years, media literacy has been considered\u00a0an essential skill\u00a0for navigating digital environments. It involves learning to identify reliable sources, verify information, and detect misleading content. 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