Pilot performance is a critical factor in aviation safety, as modern cockpits require operators to process large amounts of information while managing multiple parallel tasks. Under high workload, distraction, or stress, this reliance on human vigilance can lead to lapses that compromise operational reliability. This motivates the development of intelligent assistance systems capable of monitoring pilot actions in real time and providing continuous, objective support. In this thesis, we present and evaluate an integrated pipeline that combines object detection with gaze tracking to assess pilot interaction with cockpit instruments. While the system is applied to a checklist verification task in a general aviation cockpit simulator, the approach is designed to be generalizable to more complex environments such as airliner cockpits, where automated monitoring could provide substantial benefits, particularly in single-pilot operations.