Keystroke-level and full-screen wellbeing corpus from student devices

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Keystroke-level and full-screen wellbeing corpus from student devices — and uniquely, captured offline. Monitor risk-assesses everything a student types across Google Workspace, Microsoft 365, offline documents, web chat and social media, whether the device is online or not, buffering on device and uploading when the device reconnects. Each detected behaviour becomes an alert rated 1-5 on frequency and severity, and every high-level alert is reviewed by a trained human moderator who confirms or rejects it for tone, context and pattern-over-time before it reaches school staff. Coverage spans seven wellbeing categories including cyberbullying, offensive language and problematic gaming during school hours, with an opt-in 24/7 mode for named students of concern and boarding students, and school-calendar-aligned schedules. The result is a dual-labelled corpus — machine flag plus human adjudication — on drafted text rather than on search queries, which is a materially different and richer artefact than filter-log data.