AI教技能越快，学生未来越贬值，教育规划需警惕技术误导。
Training for Obsolescence? The AI-Driven Education Trap
- 用理论模型分析教育者只看AI现时教学效率，忽略未来技能贬值风险。
- 技能越易被AI教学，越可能被自动化取代，导致教育资源错配。
- 适合关注教育政策、AI影响与长期人力资本的学生和研究者。
人工智能同时改变学校中人力资本的生产函数与劳动力市场对技能的回报。我们构建理论模型分析当这两个因素被孤立看待时可能引发的资源配置扭曲。研究一位教育规划者：他看到AI在教授特定技能上的即时生产力提升，却未能充分内化该技术对未来工资的抑制作用。受一项预注册试点研究启发，该研究显示技能的“可教性”与被自动化风险正相关，我们发现信息摩擦导致系统性技能错配。规划者过度投资于注定过时的技能，且这种扭曲随AI普及度增加而单调上升。扩展分析表明，忽视未定价的非认知技能（如毅力）以及教育技术的内生过量采用会加剧这一错配。研究警示：若不配合前瞻性的劳动力市场信号，推动AI进教育的政策反而可能削弱学生的长期人力资本，例如挤占通过智力挑战培养出的毅力等能力。
原文摘要 · Abstract (English)
Artificial intelligence is simultaneously transforming the production function of human capital in schools and the return to skills in the labor market. We develop a theoretical model to analyze the potential for misallocation when these two forces are considered in isolation. We study an educational planner who observes AI's immediate productivity benefits in teaching specific skills but fails to fully internalize the technology's future wage-suppressing effects on those same skills. Motivated by a pre-registered pilot study suggesting a positive correlation between a skill's "teachability" by AI and its vulnerability to automation, we show that this information friction leads to a systematic skill mismatch. The planner over-invests in skills destined for obsolescence, a distortion that increases monotonically with AI prevalence. Extensions demonstrate that this mismatch is exacerbated by the neglect of unpriced non-cognitive skills and by the endogenous over-adoption of educational technology. Our findings caution that policies promoting AI in education, if not paired with forward-looking labor market signals, may paradoxically undermine students' long-term human capital, such as by crowding out skills like persistence that are forged through intellectual struggle.
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