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<p>This article examines why education science should expand its theories and fields of practice to include AI. Studying media innovations genealogically reveals that media have consistently been a&#xa0;source of new opportunities for individuals and societies. Yet historically, these new opportunities have proven to be contingent upon certain essential preconditions: for instance, the printing press granted the masses improved access to education, but only if the population was broadly <i>literate</i>. Social media platforms are no different: they offer individuals a&#xa0;public voice, yet they are only trustworthy tools of social communication in the presence of <i>critical</i> users. Many in the contemporary public debates are therefore calling for ‘AI literacy’ in curricula. However, such concepts often reduce the subject to a&#xa0;mere (tool) <i>user</i>. They also fail to show how individuals can develop competencies if there is no clearly determinable agent involved whose actions are subject to our evaluation and criticism? It is therefore no longer sufficient to focus on subjective user skills alone, and even new specific literacy models are of limited help. Instead, what is needed is an <i>augmented theoretical</i> understanding of media education that encompasses the individual and social dimension of AI. This is because societies shaped by mediatization and digitalization require an education system that enables individuals to engage with media <i>and</i> technology in a&#xa0;critical, empowered, and socially responsible way. This in turn will allow individuals to participate in society in a&#xa0;self-determined and critical manner, and to actively and creatively shape media, technology and society themselves. To understand why this is crucial, this article examines the underlying functions of AI-based media as interdisciplinary “translation” and elaborates on their significance for an expanded theoretical understanding of educational science. The relevance of this conceptional expansion is illustrated in seven arguments that explain why education should engage with AI in theory and practice.</p>

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Why AI matters for education—an exploration in seven arguments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月23日

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  • Thomas Knaus

摘要

This article examines why education science should expand its theories and fields of practice to include AI. Studying media innovations genealogically reveals that media have consistently been a source of new opportunities for individuals and societies. Yet historically, these new opportunities have proven to be contingent upon certain essential preconditions: for instance, the printing press granted the masses improved access to education, but only if the population was broadly literate. Social media platforms are no different: they offer individuals a public voice, yet they are only trustworthy tools of social communication in the presence of critical users. Many in the contemporary public debates are therefore calling for ‘AI literacy’ in curricula. However, such concepts often reduce the subject to a mere (tool) user. They also fail to show how individuals can develop competencies if there is no clearly determinable agent involved whose actions are subject to our evaluation and criticism? It is therefore no longer sufficient to focus on subjective user skills alone, and even new specific literacy models are of limited help. Instead, what is needed is an augmented theoretical understanding of media education that encompasses the individual and social dimension of AI. This is because societies shaped by mediatization and digitalization require an education system that enables individuals to engage with media and technology in a critical, empowered, and socially responsible way. This in turn will allow individuals to participate in society in a self-determined and critical manner, and to actively and creatively shape media, technology and society themselves. To understand why this is crucial, this article examines the underlying functions of AI-based media as interdisciplinary “translation” and elaborates on their significance for an expanded theoretical understanding of educational science. The relevance of this conceptional expansion is illustrated in seven arguments that explain why education should engage with AI in theory and practice.