Storyline 360 · Branching scenario
The Fair Hire: Responsible AI in Recruiting
A Storyline branching scenario where a recruiter audits an AI screening tool, spots bias, and makes six decisions that lead to one of three endings.
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 story at the fictional company Harbourline: the learner is the recruiter filling a Data Analyst role by Friday, with the AI tool SiftIQ doing the first sort.
- Six decisions, each with a best, a partial and a poor option. Feedback layers show the character's reaction and a one-line coaching point.
- Variables track every choice and a running score. The score picks one of three endings (Fair and fast, Fair but bumpy, Under audit), and a path review shows each choice next to the best one.
- Earlier choices carry forward: if Daniel was never reviewed in decision 3, decision 5 tells you he is still ranked 140th.
- The learner then writes a short explanation to the candidate and compares it with a model answer and a four-point self-check.
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. | Decisions 1 and 2: respond to a skewed shortlist and its training data (scored 3/1/0) |
| Apply a human-in-the-loop review before accepting AI recommendations. | Decisions 3 and 5: review an under-ranked candidate and choose job-related filters |
| Hold the line on a fair process under time pressure. | Decision 4: respond to a hiring manager who wants the AI's top 10 now |
| Explain an AI-assisted hiring decision to a candidate. | Decision 6 plus a written explanation, self-checked against a model answer and rubric |
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