ABOUT THIS PROJECT
Description: This was my original design, envisioned for Storyline: six scenarios, a four-category gate, and a three-part decision sequence per scenario (Initial Response, Exposure Response, Coaching).
Working with the SMEs my design evolved and we ultimately selected a simplified, reduced-scope version for the new-hire audience: five scenarios, three categories, and a single decision per scenario. That reduced scope was simple enough to build natively in Rise, which became the shipped course.
I didn't want the original, fuller design to disappear, so I built it out in Storyline 360 as a showcase of slide design, variables and triggers, layers, and custom button states with an emphasis on building a system that could scale cleanly across any number of scenarios rather than treating each one as a one-off build. This entry covers that original design in full. It's a personal project, and is not part of the deployed course.
Challenge 1: Building a four-category gate that could repeat across six scenarios without rebuilding the logic each time. The gate needed four response categories and several distractor options, and that structure had to reappear identically at the start of every scenario.
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Solution: I designed one reusable variable-and-trigger pattern for the gate rather than a bespoke build per scenario. Variables tied to each answer button check the selection on click, route to the matching feedback layer, and unlock "Continue" only when the correct variable evaluates true. Reapplying it to a new scenario meant reindexing variables and swapping content, not rebuilding the logic.
Result: One variable-and-trigger pattern covers all six gates and one layer pattern covers all five feedback states. Instead of building 48 slides, I built three. Adding a scenario meant extending a system, not duplicating a bunch of slides.
Challenge 2: Scaling three full decisions per scenario across 18 decision slides without rebuilding the variable logic each time. Three decisions per scenario yielded 18 decision slides each needing five additional feedback slides. Including intro and completion slides, this would balloon to 100 slides.
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Solution: I used a single layer-and-variable pattern for every decision slide that closely follows the logic of the gate slide. Layers house each feedback state on the base slide instead of spawning a new slide per outcome. Custom button states (Normal, Visited, Correct, Incorrect) reflect the learner's answer history using the same variable logic each time.
Result: Result: I architected this as a compromise between a very complex variable-and-trigger pattern and a massive number of slides. I built and tested the first three decision slides with custom triggers and five results layers each. From there, duplicating those slides carried the trigger logic forward automatically. The remaining 15 decision slides needed only content and variable-reference edits rather than anything built from scratch. Instead of building 90 decision slides, I built 18.
Tools
Articulate Storyline 360, AI Assistant
Claude
Gemini Notebook (fka NotebookLM)
Twine
Skills
Instructional Design/Adult Learning
Elearning
Gated Scenarios with Layered Decision Points
AI-assisted Workflows
AI Workflow
Claude: I used Claude to plan the trigger and variable logic before building in Storyline. I mapped which variables each gate and decision needed and designed a single pattern that scaled across all six scenarios without rework. I also used it to assemble a feedback-ready SME review document, then later to verify the published HTML export against that approved document. Altogether, this cut architecture and content planning, validation, and troubleshooting time by 50%.
Rapid Prototyping: Claude + Twine. To pressure-test the branching logic, I used Claude to convert the finished scenarios into Twine import files. I created one version for SME review that mirrors the learner experience and another mapping every slide and layer to its variables and navigation for the Storyline build. For a project this size, hand-building both in Twine from scratch wouldn't have been worth it, however, using Claude to generate the TWEE files avoided the manual setup cost.
Gemini Notebook: I used Gemini Notebook to verify every AI-drafted scenario against approved source documentation before sending it for SME review. Because it can be scoped to a specific, controlled set of source files Gemini Notebook is well-suited for RAG and catching inaccuracies. I also used Gemini Notebook
Articulate AI Assistant: I used the built in AI Assistant to generate the images of the dogs featured in the scenarios.