Design rationale: framework, structure, focus, and tool selection
Role: AI Activation Learning Experience Designer (contract)
Topic: AI-ready data — awareness microlearning for a broad business audience
Runtime: Approx. 2.5–3 minutes · 5 screens
Role: AI Activation Learning Experience Designer (contract)
Topic: AI-ready data — awareness microlearning for a broad business audience
Runtime: Approx. 2.5–3 minutes · 5 screens
COURSE FRAMEWORK OPTIONS & RECOMMENDATION
Given the topic (AI-ready data) and the outcome (a broad business audience understands what AI-ready data is and adopts small habits that improve it), three framework approaches were viable. Each is summarized below with its core tradeoff.
A. Definition-first (explainer)
How it works: Open by defining AI-ready data, then list risks, then list habits. Classic informational structure.
Tradeoff: Clear but low tension; learner is told the answer before they care about the question. Easy to forget.
How it works: Open by defining AI-ready data, then list risks, then list habits. Classic informational structure.
Tradeoff: Clear but low tension; learner is told the answer before they care about the question. Easy to forget.
B. Story-first (cold-open scenario)
How it works: Open on a relatable failure — an employee gets a confident but wrong AI recommendation — then reveal the data as the cause, define the concept, diagnose the failure, and resolve it.
Tradeoff: Requires tight pacing to fit runtime, but creates a question the learner wants answered. Highest retention.
How it works: Open on a relatable failure — an employee gets a confident but wrong AI recommendation — then reveal the data as the cause, define the concept, diagnose the failure, and resolve it.
Tradeoff: Requires tight pacing to fit runtime, but creates a question the learner wants answered. Highest retention.
C. Branching scenario
How it works: Learner makes data-handling choices and sees consequences play out across multiple paths.
Tradeoff: Strong behavior practice, but overbuilt for a 2.5–3 minute awareness piece. Scope risk.
How it works: Learner makes data-handling choices and sees consequences play out across multiple paths.
Tradeoff: Strong behavior practice, but overbuilt for a 2.5–3 minute awareness piece. Scope risk.
STRUCTURE (END-TO-END FLOW)
The five screens form a single narrative arc rather than a list of topics. One character (“Maya”) carries the thread, and each screen advances a question opened by the one before it. Internal scene names follow the arc: Wrong → Depends → Breaks → Fix → Forward.
Screen 1 — Wrong (the hook)
Maya gets a confident, wrong AI recommendation. Plants the question: how did this happen?
Maya gets a confident, wrong AI recommendation. Plants the question: how did this happen?
Screen 2 — Depends (the answer)
AI depends on the data we give it. Defines AI-ready data: clear, complete, consistent, current, usable.
AI depends on the data we give it. Defines AI-ready data: clear, complete, consistent, current, usable.
Screen 3 — Breaks (the diagnosis)
Missing, inconsistent, and outdated data; why Maya’s result failed. “Confident but unreliable output.”
Missing, inconsistent, and outdated data; why Maya’s result failed. “Confident but unreliable output.”
Screen 4 — Fix (the turn)
Small everyday habits; a before/after record transforms from vague to usable.
Small everyday habits; a before/after record transforms from vague to usable.
Screen 5 — Forward (the payoff)
Recap + knowledge check. “Better data today, better AI-supported decisions tomorrow.”
Recap + knowledge check. “Better data today, better AI-supported decisions tomorrow.”
Logic of the build: the definition (Screen 2) is deliberately withheld from the opener so the hook isn’t spoiled; the risk types (Screen 3) are framed as the explanation for Maya’s specific failure rather than an abstract list; and the resolution (Screen 4) shows the behavior in concrete before/after terms so the learner can picture doing it.
