Machine Teaching Interface for Adaptive Human-AI Vehicle Co-Pilots

The patent introduces a human operator as an intermediary between the AI system and the final decision execution. The human operator receives incomprehensible AI decisions, interprets them, and provides feedback or overrides when necessary. This mediator role allows the AI to operate at high speed while human comprehension and common sense alignment are maintained through the translation layer of the human operator.

The patent transforms static checklists and flowcharts into dynamic, adaptive interfaces that respond to the pilot's skill level, preferences, and situational context. The system dynamically adjusts the complexity, detail, and presentation format of procedural information based on the individual pilot's characteristics, thereby maintaining standardization while enabling personalization and adaptability.

The patent applies partial transparency by providing AI process information selectively rather than fully. The system reveals AI decision-making processes, reasoning, and confidence levels to the extent necessary for pilot learning and trust-building, while maintaining overall interface simplicity. This partial disclosure allows pilots to learn from AI processes without being overwhelmed by excessive information.

The patent implements bidirectional teaching capabilities by designing the AI system to perform multiple functions: it not only provides decisions and information to the pilot but also actively learns from pilot feedback, overrides, and interactions. The same communication interface serves both as an output channel for AI decisions and an input channel for human teaching, thereby enabling bidirectional learning without proportionally increasing system complexity.