Abstract In safety-critical domains such as air traffic control (ATC), appropriate trust in AI is essential for operational effectiveness and safety. This study proposes a phase-specific framework for recognizing automation trust using behavioral and eye-tracking features across monitoring, decision-making, and whole-task phases. Data were collected through a trust-probe experiment and analyzed using linear mixed models to identify phase-specific effects of trust.