| Role | Instructional Designer · Scenario Writer · Learning Experience Designer |
|---|---|
| Scope | Scenario-based micro-simulation placing managers at a high-stakes AI-assisted decision point in a performance management context |
| Audience | People managers, HR business partners, and L&D leaders working in organizations deploying AI-powered performance tools |
| Format | Interactive dashboard simulation · Branching logic · Consequence-based feedback · Verification framework · Reflective synthesis |
| Tools & Standards | Adobe Captivate 13 · SCORM 1.2 / xAPI · WCAG 2.1 AA |
| Deliverables | Scenario-based micro-simulation · Branching storyboard · Verification framework · Measurement framework · Pre/post reflection instrument |
This simulation was built on a specific premise: that the most consequential moment in AI-assisted decision-making is not when the system generates a recommendation, but when a human decides whether to act on it. Most AI training teaches people what a tool does. This module teaches people what to do when the tool is confident, the data is accurate, but the context that the system cannot see changes everything.
Organizations are deploying AI-powered performance tools faster than they are preparing managers to use them with judgment. A dashboard flags an employee as a performance risk. The recommendation is clear. The confidence score is high. But the AI cannot see that the employee's manager left six months ago without a handoff, that there is a confidential HR note on file, or that the colleagues they relied on transferred out of the team in Q1. The gap between what the system surfaces and what only a human can know is the real decision point.
A scenario-based micro-simulation placing managers inside a live AI performance dashboard. The system has flagged Jordan Mills — attendance down, output declining, engagement at 44 out of 100, and a confidence score of 78%. The AI has generated three recommended actions: escalate to HR, initiate a coaching conversation, or monitor for 30 days. Before the manager commits, the simulation reveals what the data cannot see. Then they decide. Then they see what happens.
Data before context. The learner sees the AI dashboard and forms an initial judgment before the human context is revealed. That sequence is a deliberate design choice, as it mirrors how AI recommendations actually enter a manager's workflow, and it creates the cognitive disruption that makes the learning feel relevant.
Consequence at the human level. Feedback does not score the decision. It shows what happened to Jordan, and connects that outcome directly to the manager's choice.
The verification framework is a transferable tool. After the consequence, the simulation introduces a four-step verification framework — confirm the source, check data completeness, assess policy alignment, record the rationale — designed to be used in any AI-assisted decision context, not just this one scenario.
Reflection is the closing act. The module ends by asking the learner to examine their own judgment — what drove the decision, what the AI shaped, and what they would do differently. That metacognitive step is where the learning consolidates.