“我们稍后修复”：教育、人工智能与教育科技中隐私的延迟处理

中文摘要 AI 辅助

教育科技（EdTech）平台收集高度敏感的学生数据，包括行为日志、残疾记录和学业历史。然而，隐私考量往往被推迟处理，而非作为基础性设计需求。我们开展了一项混合方法研究，结合了对教育科技专业人士的12次半结构化访谈，以及对48个平台进行的隐私政策审计，该审计按五个维度进行编码，具有较高的评分者间信度（平均Cohen's Kappa = 0.781）。访谈揭示了一种反复出现的组织模式：隐私虽被认可为重要，但在产品生命周期中被延迟处理，因为组织优先考虑产品功能、增长、资金和即时教育成果。责任往往被委托给云服务提供商、政策文件或下游机构，而有限的隐私相关反馈使组织几乎没有压力去改变这些做法。政策分析反映了这些模式：平台对其收集的数据描述相对较好，但对这些数据后续如何被治理提供的信息则明显不足。33%的平台尽管具有可见的人工智能（AI）功能，却未进行任何有意义的AI披露，73%的平台仅提供通用的问责和泄露应对措辞。K-12平台在儿童同意方面表现更好，因为法规对此有明确要求，但这一优势并未延伸至AI治理或问责。这些发现表明，有意义的改进需要可执行的制度和监管机制，而非仅靠自愿的隐私承诺。

英文摘要

Educational technology (EdTech) platforms collect highly sensitive student data, including behavioral logs, disability records, and academic histories. However, privacy considerations are often postponed rather than treated as a foundational design requirement. We present a mixed-methods study combining 12 semi-structured interviews with EdTech professionals and a privacy policy audit of 48 platforms coded across five dimensions, with strong inter-rater reliability (mean Cohen's Kappa = 0.781). Our interviews reveal a recurring organizational pattern in which privacy is recognized as important but deferred across the product lifecycle as organizations prioritize product functionality, growth, funding, and immediate educational outcomes. Responsibility is often delegated to cloud providers, policy documents, or downstream institutions, while limited privacy-related feedback gives organizations little pressure to change these practices. The policy analysis reflects these patterns: platforms describe what data they collect relatively well but provide substantially less information about how that data is subsequently governed. Thirty-three percent make no meaningful Artificial Intelligence (AI) disclosure despite visible AI features, and 73% provide only generic accountability and breach-response language. K-12 platforms perform better on children's consent where regulation creates explicit requirements, but this advantage does not extend to AI governance or accountability. These findings suggest that meaningful improvement requires enforceable institutional and regulatory mechanisms rather than voluntary privacy commitments alone.

发表机构

• New York University（纽约大学）

机构由 AI 辅助整理，请以论文原文为准。