When the Scaffold Stays On: AI, Practice Style, and Screening in Elite Skill Formation
当脚手架保留:人工智能、实践风格与精英技能形成中的筛选
Song Yao
AI总结 通过分析编程竞赛数据,研究AI使用对精英技能形成的影响,发现AI辅助实践在受监控环境中提升非AI辅助表现,而在开放环境中则可能侵蚀技能,表明筛选机制可区分替代型与互补型用户。
Comments 58 pages, 4 figures
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生成式AI通过完成学习者原本会自行练习的任务来提高短期生产力。这种替代是否会侵蚀前沿技能(即顶级非AI辅助表现背后的技能)是一个日益重要的开放问题。更尖锐的问题是,选择机制能否区分两种共存类型:替代型用户(用AI代替刻意练习)和互补型用户(用AI加速技能发展)。在精英编程领域,国际大学生程序设计竞赛(ICPC)和国际信息学奥林匹克(IOI)在监考下禁止AI,并通过资格赛选拔参赛者,而在线Codeforces(CF)竞赛则无监考且向所有人开放。从CF历史记录中,我们构建了一个AI提示特征(更多首次尝试接受、更少尝试和重试),与AI辅助实践一致。三种模式三角验证了制度筛选。第一,在两次AI推广中,CF实践跨队列向此特征转变。第二,在开放的CF竞赛中,更强的特征预测无ICPC/IOI关联用户的评级增益更小,但对有资格参加AI禁止竞赛的用户则不然。第三,在AI禁止的ICPC环境中,向AI风格实践的转变预测AI时代参赛者的非AI辅助得分更高。相同的实践输入根据环境是否筛选而具有相反的符号。这种对比指向两个杠杆:AI如何融入训练(因为在筛选池内,AI风格实践与非AI辅助更强表现一致),以及AI禁止评估门作为类型分离制度的设计。两者都超越了编程,延伸到认证系统(医学和法律委员会、专业认证),这些系统在日益受AI影响的劳动力中认证技能。
Generative AI raises short-term productivity by completing tasks that learners would otherwise practice on their own. Whether this substitution erodes frontier skill, the skill behind top-tail non-AI-aided performance, is an open question of rising stakes. The sharper question is whether selection mechanisms can screen apart two coexisting types: substitute-users, who use AI in place of deliberate practice, and complement-users, who use it to accelerate skill development. In elite programming, the International Collegiate Programming Contest (ICPC) and the International Olympiad in Informatics (IOI) prohibit AI under proctoring and admit entrants through qualification rounds, whereas online Codeforces (CF) contests are unproctored and open to all. From CF histories we build an AI-prompt signature (more first-attempt acceptances, fewer attempts and retries) consistent with AI-assisted practice. Three patterns triangulate institutional screening. First, CF practice shifted toward this signature across cohorts over two AI rollouts. Second, in open CF contests a stronger signature predicts smaller rating gains for users with no ICPC/IOI affiliation, but not for those who qualified for the AI-prohibited contests. Third, inside the AI-prohibited ICPC environment, a shift toward AI-style practice predicts higher non-AI-aided scores for AI-era entrants. The same practice input carries opposite signs depending on whether the environment screens for it. The contrast points to two levers: how AI is integrated into training, since within the screened pool AI-style practice coincides with stronger non-AI-aided performance; and the design of AI-prohibited evaluation gates as a type-separating institution. Both extend beyond programming to credentialing systems (medical and legal boards, professional certification) that certify skill in a workforce increasingly shaped by AI.