Branching
sims from real
practice.
A canvas for the messy, conditional, “it depends” conversations that don’t fit a multiple-choice quiz.
SimChoices turns expert practitioners’ war stories into branching scenario simulations, reading what learners actually wrote rather than what they clicked, with path validators that catch dead-ends before launch, and SCORM/LTI 1.3 hand-off into your LMS. Developed with collaborating TAFEs, universities and teaching networks.
A canvas for conditional teaching.
Most digital learning collapses real practice into a multiple-choice quiz. SimChoices keeps the conditional logic intact, what an expert actually does when context, history and consequence all matter at once.
Capture the story.
Sit beside a senior practitioner. Record their case. The canvas guides you to break it into scenes, decisions and consequences, the same shape as the way they tell the story over coffee. Story stays the unit of design; the engine handles the branching.
Type a real response.
No multiple-choice unless the scene calls for it. Learners write what they would say, draft what they would send, plan what they would do next. The engine reads it, works out which approach they took, and routes the simulation to the consequence that follows.
See the field.
Cohort analytics show where the path forks, not just whether learners passed, but which approach they took and how it changed under pressure. Hand off to your LMS via SCORM 1.2 or LTI 1.3 with grades intact.
Three views of one simulation.
The same scenario seen by the learner taking it, the educator who built it, and the program leader watching the cohort. Drop into any tab to see the surface area.
“My mum’s boyfriend says I’m not allowed to tell anyone, but… he keeps coming into my room at night.”
State
Reference
Engine
Path distribution · Scene 02
312 learners · 96% completeReflection delta
pre vs. post · self-rated confidenceNLP heard · sample of learner responses
classified into archetypes · auditable on clickEvery generated line is checked against your source story before it publishes.
Capabilities.
Nine capabilities that turn the canvas from a slide-deck replacement into a research instrument. All available on every plan; pilots get the full surface area at no cost.
Branching canvas
Drag-and-drop graph of scenes, choices and consequences. Up to 7 layers deep, with pattern-locked sub-trees so re-using a sub-flow stays in sync.
Open-text response classifier
Open-text learner replies are sorted into the archetypes the author defined. Auditable confidence scores; manual override always one click away.
Path validator
Catches dead-ends, unreachable scenes, dangling branches and unused choices before publish. Walks every reachable path, surfaces the gaps with a one-click jump.
Stateful timing & pressure
Scenes can be timed, paced or interrupted. Time-pressure variables flow through state and influence which path opens next. Useful for triage and crisis sims.
SCORM 1.2 & LTI 1.3
Hand off into Canvas, Moodle, Blackboard, Bridge, Brightspace and any LTI 1.3-compliant LMS. Grades, completion and time-on-task pass back automatically.
Cohort path analytics
See where a cohort branches, where they get stuck, where confidence climbs. Filter by demographic, repeat attempt, or facilitator. Export to CSV or SPSS.
WCAG 2.2 AA, audio paths
Every scene supports a recorded-audio variant; transcripts, captions, keyboard nav and reduced-motion paths shipped on day one. Independently reviewed by accessibility specialists.
Story-to-scenes assistant
Paste a transcript of an expert telling their case. The assistant proposes scenes, decision points and archetypes, you accept, edit or reject each one before it lands on the canvas.
Built with educators
Every release is shaped with real teachers and trainers, validation cohort size, classifier F1, inter-rater reliability, drop-off curves all reviewed with the people who use it.
Story to cohort in five steps.
How a sim moves from a senior practitioner’s anecdote to 312 learners in a TAFE cohort.
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01 · Capture
Sit, listen, record.
An author runs a 45-minute conversation with a senior practitioner. The story-to-scenes assistant turns the transcript into a draft tree of scenes and decision points.
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02 · Author
Lay out the canvas.
Edit nodes, write archetypes for the open-text choices, set state variables (trust, time, escalation). Validate. Iterate. The graph stays the source of truth.
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03 · Pilot
Run with 30 first.
Soft-launch to a small cohort. The dashboard surfaces unparsed responses; you tighten classifier archetypes until F1 climbs above 0.85.
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04 · Deploy
Hand off to the LMS.
Publish to SCORM 1.2 or LTI 1.3 endpoint. Grades and completion pass back automatically. Scale to a full cohort with a single configuration change.
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05 · Reflect
Read the field.
Cohort analytics show what the field actually did, not just whether they passed. Iterate the canvas based on where the path forks and stalls.