Storyline 360 · Branching scenario
The Fair Hire: Responsible AI in Recruiting
A branching scenario where HR managers audit an AI screening tool, spot bias, and make decisions with real consequences.
The challenge
A company adopted an AI résumé-screening tool. Recruiters trusted its rankings without question, which risked unfair hiring outcomes and legal exposure.
My approach
- A realistic three-act story where the learner is the hiring manager on a live vacancy.
- Every choice changes the candidate pool, team trust and a compliance meter.
- Custom triggers and variables track decisions and give personalised feedback at the end.
- A reflective debrief links each consequence back to responsible-AI principles.
Objectives and how each is assessed
Every assessment item was written before the content, and maps to exactly one objective.
| Learning objective | Assessment |
|---|---|
| Recognise signs of bias in an AI screening tool's output. | Hotspot task: flag the biased patterns in a ranked shortlist |
| Apply a human-in-the-loop review before accepting AI recommendations. | Branching decision points scored by variables |
| Evaluate whether an AI hiring decision can be explained to a candidate. | Scenario question with consequence-based feedback |
| Choose the correct escalation path when bias is found. | Final decision, must reach the 'fair outcome' ending |
Results and reflection
Add pilot results, reviewer quotes or what you would do differently next time.
Design artefacts
- Storyboard (PDF) — coming soon
- Action map — coming soon