Intelligent predictions and actionable insights powered by AI
Students at Risk
47
Students predicted to underperform this term
+8 from last weekAttendance Alerts
12 Classes
Classes below 90% attendance threshold
-3 from last weekFees Collection Forecast
UGX 2.1B
Expected collection by term end
+12% above targetDiscipline Trends
↓ 18%
Predicted decrease in incidents
Improvement trend detectedCurrent
95.2%
AI Prediction
96.1%
Current
94.8%
AI Prediction
95.5%
Current
88.5%
AI Prediction
87.2%
Current
96.3%
AI Prediction
97%
| Student | Class | Risk Score | At-Risk Subjects | Attendance | Fees Status | Action |
|---|---|---|---|---|---|---|
SN Sarah Nakato | P5A | 87 | MathScience | 78% | Paid | |
JK John Kamau | S2B | 82 | EnglishHistory | 65% | Partial | |
GA Grace Atim | P7C | 79 | Math | 88% | Paid | |
DO David Okello | S3A | 76 | ScienceMath | 72% | Outstanding | |
AN Alice Nambi | P6B | 73 | English | 81% | Paid |
Based on comprehensive data analysis, the AI Copilot has identified 47 students requiring immediate attention. Primary concerns include declining attendance in S1-S3 classes and performance gaps in Mathematics and Science. However, fees collection is trending 12% above target, and overall discipline incidents are projected to decrease by 18% this term.