Automating Teacher Performance Reviews with AI

The Challenge

A tuition centre chain needed to evaluate their teachers monthly. The data lived in their LMS: student grades, attendance records, and qualitative feedback from guardians. Manually exporting this data and synthesising it into a fair performance review was a massive administrative burden, prone to human error and bias.

The Solution

We built an automated workflow using n8n that acts as a bridge between the LMS and the management team.

1. Data Extraction: A custom JavaScript script extracts raw data from the LMS API/Database (Student Performance, Attendance, Feedback).
2. Aggregation: n8n aggregates this data by teacher profile.
3. AI Analysis: The aggregated packet is sent to an LLM with a specific prompt to analyze trends, flag issues (e.g., dropping attendance), and summarize guardian sentiment.
4. Reporting: A PDF/Email report is generated and sent to the HR manager automatically.

// Extracting specific teacher metrics
const teacherMetrics = data.map(record => ({
    id: record.teacherId,
    attendanceRate: (record.present / record.totalClasses) * 100,
    sentiment: record.guardianFeedback.length > 0 ? 'analysis_required' : 'neutral'
}));

return teacherMetrics;

Why It Matters

This isn't just "automation"; it's Operational Intelligence. The client moved from "guessing" which teachers needed support to having hard data every month, allowing them to provide targeted training and improve student outcomes.