Designing AI-Supported Performance Feedback and Goal Systems

An AI-supported performance system that actually helps managers and employees requires careful design. The data you draw on, the way you summarize it, the bias controls you build in, and the outputs you offer to managers all shape whether the system becomes a useful tool or another source of friction. This course walks through those decisions in the order they typically arise. You will work through written design exercises that mirror how an HR technology team would plan an AI-supported performance system. The emphasis is on the practical tradeoffs that matter when employee trust, manager time, and legal context are all under pressure. What you'll learn: - Identify data sources that genuinely reflect performance without sliding into surveillance - Design feedback aggregation routines that summarize multiple inputs without losing nuance - Apply natural language processing for surfacing themes while respecting privacy and consent - Build goal-tracking workflows that adapt as priorities shift across quarters and years - Plan bias detection and audit routines as a first-class part of the system - Design outputs that support managers in coaching conversations rather than replacing them The course progresses from data inputs through feedback aggregation, goal tracking, bias controls, and manager-facing outputs. A capstone written exercise asks you to draft a one-page design for an AI-supported performance system in a specific organizational context. This course is designed for HR technology professionals, people analytics specialists, and HR leaders evaluating new systems. No deep AI experience is required. The course treats the system as a design problem and respects the human stakes throughout.