Adaptive scenario generation: introduce training scenarios targeting weaknesses identified from real-time performance metrics¶

Strategy · All strategies
Evidence · no claims cited

Description¶

Rather than relying solely on fixed scenarios, ASTRA proposes automatically generating training situations from trainee performance. "By analyzing real- time interactions and RT performance metrics, the system identifies performance gaps and dy- namically introduces scenarios that target specific weaknesses.", creating a closed-loop training environment that continuously adapts to trainee competency without direct instructor intervention. For example, a trainee struggling with altitude management may be presented with scenarios of increased altitude-control complexity.

Design Implications¶

Context¶

Requirements¶

• Real-time capture of trainee interactions and radiotelephony performance metrics to detect performance gaps

Constraints¶

• Presented as future work rather than an implemented and evaluated capability

Target Learners¶

• Air traffic control operator trainees

Target Learning Goals¶

• Closing individual performance gaps such as altitude management

Related Strategies¶

• Scenario Based Training
• Use AI-assisted hybrid evaluation (deterministic rules plus LLM analysis) to scale standardized radiotelephony assessment
• Use ANN-based adaptive feedback to provide scalable, process-oriented real-time feedback at scale in higher education

Examples¶

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Key Sources¶

• Ethan Chew, Enjia Wu, Iruss Eng, Ian Lim, Ranen Sim, Brandon Koh, Kaleb Nim, Caden Toh, Wei Dong Soin, Darius Koh, Galen Tay, Prannaya Gupta, Jonathan Koong, Yong Zhi Lim. (2026). ASTRA: A Scalable Next-Generation ATCO Training Simulator with Autonomous Simpilots. https://arxiv.org/abs/2606.18319