Generative AI is currently reshaping how writing and authorship function across educational and scientific settings. Recent studies (2023-2026) show that large language models (LLMs) enable the rapid production of syntactically polished yet conceptually shallow text, disrupting the traditional relationship between effort and output, understanding and written performance. In higher education, essays no longer reliably reflect learning, while in scientific publishing, editors report rising submission volumes and AI generated manuscripts and peer reviews. Drawing on Ernst Cassirer’s philosophy of symbolic forms and Günther Anders’s critique of technical idolatry, the chapter argues that LLMs function as technical symbolic forms that produce idolatric simulations - outputs that imitate the surface of knowledge while bypassing the formative labour of writing and thinking. Cassirer’s notion of symbolic consciousness clarifies how GenAI reorganizes the conditions under which meaning is recognized, while Anders’s concept of inimputability illuminates the diffusion of responsibility in hybrid human–machine writing environments. The chapter offers an illustrative synthesis of recent empirical work in education and scientific publishing to show how LLMs reorganize the symbolic conditions under which writing and assessment take place. It advances a pedagogy of symbolic responsibility, by proposing educational practices (reasoning visibility, verification based learning, dialogical copresence, authorship transparency and formation oriented curricular design) grounded in progressive induction and resonance. These practices aim to sustain curiosity and responsibility in hybrid human-machine ecologies, and to support educators and institutions in confronting the crisis of authorship under generative AI.

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LLMs,Artificial Intelligence,Simulation,Idolatry,Ernst Cassirer,Günther Anders

• Economics and Business - Social Sciences
• Educational Sciences - Social Sciences
• Philosophy, Ethics and Religion - Humanities