PS-13Education & Inclusion
ShikshaSetu
Explainable Student Success and Early-Intervention Platform
SDG 4SDG 10
The challenge
Schools and colleges store attendance, assessment and learning-activity data but often use it only after a student has already fallen behind. Design an AI-assisted early-intervention platform that identifies patterns of academic or engagement risk, explains the main contributing factors and recommends practical interventions for teachers or mentors. The solution must distinguish correlation from cause, avoid labelling students permanently and include fairness checks so that protected or socioeconomic characteristics are not used carelessly.
What a working submission needs
Build a working dashboard using synthetic or open student-performance data.
It should generate risk bands, show feature-level explanations, suggest at least three intervention options, allow mentor feedback and track whether risk changes over time.
Stretch goals
personalised learning-resource recommendations, missing-data handling, intervention-effect analytics or student-facing progress views.
Optional. A working core counts for more than feature count.Suggested stack
Web/mobile application, AI/ML models, a mapping or dashboard interface, and open APIs as appropriate to the problem.
Scope and responsibility
Healthcare, safety and public-sector challenges are decision-support tools, not autonomous clinical, legal, emergency-command or enforcement systems. Name your prototype’s limitations in the demo. Judging weighs a working core, justified use of AI, technical quality, real-world impact and responsible design above feature count.
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