Education · Live POC

Student Retention Intelligence

Identifies students at risk of dropout 30–60 days before the event using a composite signal model spanning attendance trends, grade trajectory, assignment submission rates, library usage, and financial stress indicators. Risk scores every enrolled student, segments them by intervention urgency, and recommends specific actions per student (counselling, fee waiver, peer tutoring). Tracks intervention outcomes and measures semester-on-semester retention improvement.

Student Retention Intelligence — Pragyan Ai

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Adaptive Learning Engine

Builds a real-time knowledge graph for each student by analysing assessment scores, time-on-task, error patterns, and content interaction signals. Dynamically adjusts content difficulty, format (video vs text vs practice), and pacing per learner. Shows mastery heatmaps by subject and topic, predicts assessment readiness scores, and surfaces learning path recommendations — enabling teachers to intervene on students falling below mastery thresholds before exams.

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