"We'll Fix It Later": Education, AI, and the Deferral of Privacy in EdTech

arXiv:2609.28137v1 Announce Type: new Abstract: 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 requir

EdTech platforms prioritize functionality over privacy, lacking transparency in AI governance.

The study shows that EdTech platforms collect sensitive student data with privacy often deferred or inadequately addressed, increasing risks to student privacy and data governance. Poor AI disclosure and weak accountability measures raise concerns about privacy protection and institutional responsibility. The implications raise serious privacy concerns but lack immediate catastrophic effects.

A well-documented empirical study (interviews plus 48-platform audit) shows systemic under-disclosure of AI use and weak data governance in education technology affecting children. This is a concerning but not extreme institutional failure, with the study itself pointing toward feasible regulatory remedies.

EdTech platforms collect sensitive student data, including behavioral and disability records, yet privacy considerations are routinely deferred in favor of other priorities. A significant portion of these platforms do not disclose their use of AI despite visible AI features, nor do they adequately explain data governance, posing substantial risks to student privacy and autonomy.

The study shows that EdTech platforms prioritize functionality over privacy. This leads to poor AI governance and accountability. Meaningful improvement requires enforceable mechanisms.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   c1663caa-2ccf-4a07-bb75-a482d781440c
           https://arxiv.org/abs/2609.28137
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EVIDENCE   3 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      -57  (Adverse)
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CONFIDENCE 84%