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Working paper argues that AI may lower quality standards without strong prompts, suggesting a need for better evaluation measures.
This working paper argues that unguided artificial intelligence can unintentionally promote mediocrity by smoothing over qualitative differences, replacing evaluation with balanced contextualization, and translating original contributions into familiar categories. It proposes that strong theoretical prompts function as epistemic control instruments that help AI recognize quality, originality, coherence, long-term impact, and the expansion of human agency. The article situates this thesis within the semantic field of Jean-Pol Martin and complements the existing Zenodo corpus on prompt architecture, New Human Rights, Learning by Teaching, and human-AI collaboration.
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Martin et al. (2026) studied this question.
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