Why managing AI risk presents new challenges
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The difficult of using AI to improve risk management
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How to bring AI into managing risk
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Pros and cons of using AI to manage risks
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Benefits and opportunities for risk managers applying AI
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Facing a shared global shortage of STEM talent, countries are takingdistinct strategic paths in K-12 education. This presentation explores thecontrasting STEM education ecosystems of China and Germany, comparing China'semphasis on "Technological Pragmatism" with Germany's focus on"Digital Sovereignty." The analysis examines four key dimensions:governance logic, pedagogical paradigms, teacher professionalism, and digitalpathways/AI praxes. Specifically, China's model is driven by nationalstrategic guidance, comprehensive policy support, and industrial ecosystemsynergy.This dynamic coordination enables the rapid, large-scaledeployment of AI infrastructures and leverages Teaching Research Groups (TRGs)to ensure standardized efficiency and distributive equity. In contrast, Germanyemphasizes multi-level subsidiarity and decentralized autonomy, transformingtheoretical knowledge into action competence, and prioritizing AI ethics andcritical reflection ("establish norms first, apply later"). Whileboth systems demonstrate immense vitality, they face unique practical dilemmas.For instance, China is exploring ways to balance highly structured knowledgeacquisition with holistic student development, whereas Germany seeks tomitigate performance anxiety and resource fragmentation arising from highlyautonomous learning. Ultimately, this talk highlights the potential forreciprocal learning between the two nations. By moving beyond binaryoppositions and embracing structural complementarities, we can better achieveour shared mandate: nurturing innovative and ethically mature digital citizens
