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AI-based Early Warning System spots 1.68 lakh students at risk of dropping out from Gujarat govt’s primary schools

by India News Online Team
June 23, 2025
in Technology
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AI-based Early Warning System spots 1.68 lakh students at risk of dropping out from Gujarat govt’s primary schools
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Now an AI-based Early Warning System (EWS) for identifying students at risk of dropping out from schools, is in place in Gujarat. 

Currently, around one crore students are studying in government primary schools (Grades 1 to 8) in Gujarat. Among these, the EWS has so far identified nearly 1.68 lakh students — less than two percent — who are at potential risk of dropping out, Gujarat government stated in an official release, Monday. This EWS system identifies potential dropout students in advance, preventing them from leaving school and ensuring they complete their education.

The Government of Gujarat aims to reduce the school dropout ratio to nearly zero at the primary, secondary and higher education levels. In the year 2001-02, the dropout ratio of students from classes 1 to 8 in Gujarat stood at 37.22 per cent. This declined to 2.42 per cent by the year 2023-24. To ensure that these 1.68 lakh students identified by EWS continue their schooling, the concerned students and their parents will be invited to the Shala Praveshotsav — a state government initiative — where they will be sensitised about the importance of completing school education for the child’s development and progress.

The AI-based EWS built on a fully scientific approach, uses data such as age, gender, disability status, school performance, attendance and assessment records of every student studying in primary schools (Classes 1 to 8) to predict potential dropout cases in advance and issues alerts to take preventive actions. The system uses algorithms to identify patterns in the data to detect students at risk of dropping out. Major contributing factors for student dropouts include frequent absenteeism, poor academic performance, health issues or physical disabilities and behavioural problems. In addition, school-related information such as school type (government, aided, private, etc.), multigrade classrooms, school infrastructure and family-related data such as economic status, migration, family beliefs about education, number of children in the family and demographic factors are also used to identify potential dropout students.

After identifying the factors related to the potential dropout of students, discussions will be held with their parents to prevent the child from leaving school. School Management Committee meetings will address how to ensure the child completes their education and remains enrolled. When the new academic year begins, the School Management Committee will ensure that students identified as at-risk definitely get enrolled and attend school regularly.

Published on June 23, 2025



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