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<p>The prevailing discourse comparing Artificial Intelligence (AI) and Natural Intelligence overlooks a critical dimension: culture. Intelligence, whether artificial or human, cannot be fully understood without recognizing its cultural foundations and adaptive contexts. This Perspective introduces two key contributions. First, it defines the Cultural Cognition Gap, the disconnect between AI’s static, pattern-based reasoning and the dynamic, culturally adaptive nature of human cognition, evident in real-world deployment failures. Second, it proposes Culture Driven AI, a conceptual and normative framework advocating for AI systems designed to engage with cultural plurality and fluidity as central features of intelligent behavior. By situating intelligence within its cultural contexts, this work highlights the limitations of current AI design and calls for interdisciplinary collaboration to develop systems that are not only technically aligned but also culturally attuned, ethically grounded, and responsive to the diverse realities of human societies.</p>

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Towards culture driven artificial intelligence to bridge the cultural cognition gap

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

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  • Ammar Younas,
  • Yi Zeng

摘要

The prevailing discourse comparing Artificial Intelligence (AI) and Natural Intelligence overlooks a critical dimension: culture. Intelligence, whether artificial or human, cannot be fully understood without recognizing its cultural foundations and adaptive contexts. This Perspective introduces two key contributions. First, it defines the Cultural Cognition Gap, the disconnect between AI’s static, pattern-based reasoning and the dynamic, culturally adaptive nature of human cognition, evident in real-world deployment failures. Second, it proposes Culture Driven AI, a conceptual and normative framework advocating for AI systems designed to engage with cultural plurality and fluidity as central features of intelligent behavior. By situating intelligence within its cultural contexts, this work highlights the limitations of current AI design and calls for interdisciplinary collaboration to develop systems that are not only technically aligned but also culturally attuned, ethically grounded, and responsive to the diverse realities of human societies.