Crew Reign: CRM Judgment Training

What this is

Crew Reign is a browser-based survival game built in the style of Reigns (2016) — one card at a time, binary swipe decisions, four resource meters, and death at the extremes. The player assumes the role of Purser / Lead Cabin Crew on a single commercial flight, navigating 15 procedurally drawn cabin scenarios from pre-boarding to landing. There are no “correct” answers — only trade-offs that shift four critical operational meters: Safety, Service, Crew Morale, and Schedule.

The game is designed as a digital-game-based learning (DGBL) prototype that applies constructivist learning theory to crew resource management (CRM). It is not a certified airline training system; it is a research-grounded, AI-assisted learning tool that demonstrates how game mechanics can embed competency development in vocational contexts.

Live Demo

Game mechanics and learning alignment

Core loop: survive the flight

• One card, two choices: Each card presents a single cabin scenario with two narrative paths (swipe left or right). The player sees a consequence preview only after the decision is locked — forcing intuitive, time-pressured judgment.
• Four meters: Safety, Service, Crew Morale, Schedule — all start at 50%. If any meter drops to 5% or below, or rises to 95% or above, the flight ends in failure.
• No points: The only metric is survival duration (flight years survived) and meter balance at landing. This eliminates extrinsic motivation artifacts and focuses on intrinsic decision quality.
• Failure states: Each meter has a unique death narrative (e.g., Crew Morale collapse → union strike; Schedule obsession → regulator ground stop). Failure is designed to be instructive, not punitive — the player learns why the imbalance occurred.
• Success states: Surviving all 15 cards triggers a competency radar based on meter balance. The closer all four meters are to 50%, the higher the CRM competency scores.

Why this maps to CRM competency models

The four meters are deliberately mapped to ICAO and EASA CRM competency frameworks:

This alignment reflects research showing that well-designed serious games support constructivist learning by embedding learners in authentic contexts where knowledge is situated and decisions have visible consequences (Gee, 2003; Shaffer, 2006). The binary choice structure forces cognitive dissonance — both options are plausible, and the player must reconcile competing priorities rather than identify a single “right” answer.

Scenarios: technical depth and authenticity

The 15-card deck covers scenarios drawn from actual cabin safety reports and CRM training syllabi:

• Pre-boarding: Chemical odour discovery, toxic fume detection from bleed air, PBE readiness verification
• Boarding: Passenger non-compliance (oversized baggage, seat refusal), service animal conflicts, contamination events
• Taxi/Take-off: Ground delay decisions, passenger medical deterioration before take-off, cabin securing under time pressure
• Cruise: Smoke / fire in galley or lavatory, lithium battery thermal runaway in overhead bin, pilot incapacitation, decompression, medical diversion decisions, fume events from engine oil seal leaks, crew fatigue recognition
• Disruption/Recovery: Unruly passenger escalation, decompression response, final approach cabin securing under turbulence, contaminated cabin air events

Each scenario includes:

• Realistic trigger conditions: Chemical odour without smoke detector alarm; crew fatigue after 11-hour duty; bleed-air fume concentration in specific rows
• Consequence chains: Immediate operational outcome + cascading effect on other meters
• No feedback text on choice: The player sees the narrative consequence, not an educational annotation. Learning happens through pattern recognition across repeated plays, not explicit instruction

Academic foundation: game-based learning in aviation

The design draws on systematic reviews of digital game-based learning in vocational education (Wouters et al., 2013; de Freitas, 2018) which show that games increase motivation, engagement, and often produce more sustainable learning outcomes than traditional methods. In aviation specifically, serious games have been studied for pilot cognitive skills training (Chandra et al., 2016) and crew resource management simulation (O’Connor & Flin, 2003).

The key DGBL design principles applied here:

1. Alignment of mechanics with learning outcomes: The four-meter survival model directly teaches resource balancing — the core of CRM. If the game mechanic doesn’t map to a competency, it is removed.
2. Failure as pedagogy: Research on productive failure (Kapur, 2016) shows that learners who struggle before receiving instruction develop deeper conceptual understanding. The “death” screen surfaces which meter failed and the last three decisions — enabling reflective analysis.
3. Repetition with variation: Each play draws 15 cards from a shuffled deck. The player encounters the same scenario types in different orders, building pattern recognition and adaptive expertise (Hatano & Inagaki, 1986).
4. Debrief scaffolding: The instructor analytics overlay (` key) shows decision heatmaps, meter trajectory, and time-to-decision — enabling facilitators to run structured debriefs grounded in actual player data.

Visual and interaction design

• Light cream and pastel surfaces: No dark mode, no dark accents — deliberately soft and non-clinical to reduce anxiety around failure
• Character-driven cards: Each scenario shows a character portrait, name, and situation — narrative immersion reduces cognitive load (Mayer, 2009)
• Swipe animation: Cards physically swipe left/right with rotation and opacity fade — provides tactile feedback and reinforces the binary commitment
• Phase indicator: Subtle dot bar showing current flight phase (Pre-Boarding through Disruption/Recovery)
• Danger zone visualization: Meter tracks show red-tinted zones at 0–5% and 95–100% — pre-attentive processing of risk (Ware, 2004)

Technical stack

• SvelteKit with Svelte store for reactive game state
• 15-card JSON scenario deck with shuffled draw
• CSS-driven card swipe animations (transform, opacity, keyframes)
• Responsive layout for tablet and desktop
• Instructor analytics overlay with heatmap and meter trajectory

References (embedded in design)

• Gee, J. P. (2003). What Video Games Have to Teach Us About Learning and Literacy
• Shaffer, D. W. (2006). How Computer Games Help Children Learn
• Wouters, P., et al. (2013). A meta-analysis of the cognitive and motivational effects of serious games. Journal of Educational Psychology
• Kapur, M. (2016). Examining productive failure. Journal of the Learning Sciences
• Hatano, G., & Inagaki, K. (1986). Two courses of expertise. Child Development and Education in Japan
• Mayer, R. E. (2009). Multimedia Learning (2nd ed.)
• Ware, C. (2004). Information Visualization (2nd ed.)
• Chandra, S., et al. (2016). Game-based training for aircrew cognitive skills. International Journal of Aviation Psychology
• O’Connor, P., & Flin, R. (2003). Crew resource management training for offshore oil production teams. Safety Science