Instructor & Learner Insights Automation
Built a privacy-first analytics and AI pipeline that converts learner survey data into actionable insights, enabling instructors to plan classes based on evidence rather than intuition.
Technologies & Tools
📊 Impact: Privacy-first AI insights for data-driven instruction
Problem
Instructors at Tech Goes Home rely on learner feedback and outcomes data to adapt their teaching, but historically this data was either too raw, too delayed, or too difficult to interpret. As a result, instructional adjustments were often based on intuition rather than evidence. Learner survey data lived in Salesforce but was underutilized, instructors lacked time and tools to analyze raw data, reports focused on metrics not interpretation or action, and instructional decisions were often based on experience rather than evidence.
My Role
I owned the solution end-to-end, including designing a privacy-safe analytics architecture, defining PII-stripping and data minimization rules, building the Salesforce → Python analytics pipeline, integrating LLMs to generate human-readable insights, and designing instructor-facing reports aligned to teaching workflows. This was not a reporting enhancement — it was a decision-support system.
Solution
Designed an automated insights pipeline that transforms learner survey data into structured, actionable insights for instructors. The system securely analyzes de-identified learner data using Python-based analytics and LLM-driven insight generation, then delivers concise, instructor-ready reports that directly inform lesson planning and instructional strategy.
Architecture
High-Level Data Flow
High-Level Data Flow: (1) Learner survey data extracted from Salesforce
Personally identifiable information (PII) is stripped
De-identified data securely sent to Python analytics service
Statistical patterns, trends, and deltas computed
Aggregated results passed to LLM for insight generation
Structured, instructor-ready report generated and delivered. Used Salesforce as source system, privacy layer with PII removal and field-level filtering, Python analytics engine, and LLM-based insight generation.
Key Design Decisions
Results
- ✓Enabled instructors to adjust class plans based on real learner needs
- ✓Increased adoption of survey data in instructional planning
- ✓Reduced manual analysis and interpretation effort
- ✓Improved alignment between learner feedback and course delivery
- ✓Established scalable framework for AI-assisted educational insights