Reform Practice of an AI-Enabled Integrated Model for "Flight Principles" Experimental Teaching
DOI:
https://doi.org/10.61360/BoniCETR262020881001Keywords:
artificial intelligence, flight principles, experimental teaching, intelligent simulation, teaching reformAbstract
Traditional experimental teaching in Flight Principles is constrained by site, equipment, airspace, and safety conditions. It often suffers from single experimental scenarios, poor parameter controllability, limited data acquisition, and superficial understanding of principles, making it difficult to meet the needs of high-precision, inquiry-based, and innovative experimental teaching in aviation-related majors. Artificial intelligence offers advantages such as simulation, data collection, intelligent analysis, and personalized tutoring, which can effectively compensate for the shortcomings of traditional experiments. Focusing on AI-enabled teaching reform, this paper clarifies practical teaching objectives, analyzes factors affecting reform, constructs a systematic implementation method for AI-integrated experimental teaching, and builds a new experimental teaching model that combines virtual and real environments, is data-driven, and intelligently empowered. The study aims to improve students' ability to inquire into flight principles, their aviation engineering thinking, and their innovative practical ability, thereby providing a reference for intelligent teaching reform in aviation majors.
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Copyright (c) 2026 Liang Wang
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