SIGES Alerta Temprana — El Salvador's Self-Audited School Dropout Early-Warning System
El Salvador
El Salvador's SIGES early-warning module flags students at risk of dropout (pregnancy, early union, over-age status) across 96% of the …
Malaysia · Putrajaya · See the Malaysia profile
Malaysia's Ministry of Education added AI risk-screening to its student-tracking system, SiPKPM. In 2025 it helped over 9,000 at-risk students, including 3,287 girls, return to school, while national exam-day absenteeism fell from over 10,000 to about 8,000.
Malaysia's Ministry of Education (KPM) operates the Sistem Pengesanan Murid KPM (SiPKPM), a national student-tracking platform covering primary and secondary schools. In mid-2024 the ministry strengthened SiPKPM with AI, which screens each student against seven risk indicators — attendance, academic achievement, disciplinary record, disability or learning-support needs, household income, distance from school, and parents' marital status — and sends automatic alerts to teachers, counsellors and district officials.
According to UNICEF Malaysia, which supported the system's development, more than 9,000 students were helped to return to school in 2025 after being flagged, including 3,287 girls. Malaysia's national news agency BERNAMA separately reported that AI-enhanced tracking helped cut the number of SPM (national secondary exam) candidates absent on exam day from the tens of thousands to just over 8,000, and Malay Mail independently confirmed a year-on-year drop in SPM no-shows from 10,160 (2023) to 8,164 (2024).
The system relies on sensitive personal data — including household income, disability status and parental marital status — and no published source describes an independent evaluation, a data-protection impact assessment, or a documented consent framework for families. The figures reported are self-reported by the ministry and its partners rather than independently audited.
Read the full analysis: https://www.malaymail.com/news/malaysia/2025/02/05/spm-dropout-issue-sees-improvement-as-moe-plans-secondary-education-reforms/165624
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El Salvador
El Salvador's SIGES early-warning module flags students at risk of dropout (pregnancy, early union, over-age status) across 96% of the …
United Kingdom
The UK's Open University has run OU Analyse, a machine-learning early-warning system flagging at-risk distance learners weekly, in production since …
Guatemala
A World Bank-designed predictive model using only existing administrative data flagged Guatemalan students at risk of dropping out. A 4,000-school …
India
Gujarat's Vidya Samiksha Kendra uses AI analytics on attendance and assessment data to flag dropout risk for 11.5 million students …
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