AI 中文总结
本文针对AI在教育领域的自我强化伤害风险，提出“支架，而非替代”的设计原则，其诉求与学习科学原则契合，该原则或可推广至其他领域，旨在开发培养人类能力的AI系统。
AI 中文摘要
人工智能在教育场景中的应用速度远超人们对其后果的理解。我们认为核心风险是对齐问题：消除人类努力的AI会侵蚀教育本应培养的能力。我们将该风险组织为包含四个相互关联维度的整合框架——认知、能动性、情绪健康与伦理，这些维度由一个自我强化循环关联：认知卸载减少努力，削弱能动性，并加剧情绪与伦理伤害。我们以一小群学生的视角为该框架提供依据：对49篇关于AI影响的国际学士学位议论文的探索性分析显示，学习者感知到这些风险，其中80%的论文指出依赖AI会减少思考。同时，这些论文明确表达了学生想要的AI的一致愿景：通过不直接给出答案、提示回忆、用问题而非解决方案鼓励反思来支持而非替代学习的系统。这些诉求与学习科学的既定原则高度一致。基于这些见解，我们提出单一设计原则：支架，而非替代。我们认为该原则不仅适用于教育，它代表了AI生态系统面临的更广泛挑战：任何中介人类思考的系统要么通过替代削弱人类能力，要么通过支架强化人类能力。最后，我们概述了开发可培养持久人类能力的AI系统的研究议程，这不仅对学习者至关重要，最终也对民主社会至关重要。
英文摘要
Artificial intelligence is being adopted in educational settings faster than its consequences are understood. We argue that the central risk is misalignment: AI that eliminates human effort erodes the very capacities education is meant to build. We organize this risk into an integrative framework of four interrelated dimensions -cognition, agency, emotional well-being, and ethics- linked by a self-reinforcing cycle where cognitive offloading reduces effort, weakens agency, and compounds emotional and ethical harm. We ground the framework in the perspective of a small cohort of students: an exploratory analysis of 49 International Baccalaureate argumentative essays about the impact of AI reveals that learners perceive these risks, with $80\%$ of essays reporting that AI reliance reduces thinking. At the same time, the essays articulate a consistent vision of the AI the students want: systems that support rather than replace learning by withholding immediate answers, prompting recall, and encouraging reflection through questions instead of solutions. These desiderata closely align with established principles from the learning sciences. Building on these insights, we propose a single design principle, scaffold, do not substitute. We argue that this principle extends beyond education. It represents a broader challenge for the AI ecosystem: any system that mediates human thinking can either weaken human capabilities through substitution or strengthen them through scaffolding. We conclude by outlining a research agenda for developing AI systems that foster enduring human capacity, an imperative not only for learners but, ultimately, for democratic societies.
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