An NLP-Focused Pilot Training Agent for Safe and Efficient Aviation Communication

Abstract

Aviation communication is vital for safe and efficient flight operations. However, pilots often struggle to adhere to strict phraseology due to diverse backgrounds and language proficiency levels. Traditional training methods involve expensive setups and reliance on human-in-the-loop simulations. To overcome these challenges, we propose an NLP-focused training agent. Our approach leverages natural language capabilities and involves fine-tuning on communication data to generate instructions based on input scenarios (keywords). Given the absence of prior references for this business problem, we explored the feasibility of our proposed solution by 1) generating all instructions at once and 2) generating one instruction while incorporating conversational history in each input. Our findings affirm the feasibility of this approach, emphasizing the effectiveness of fine-tuning pre-trained models and large language models in advancing aviation communication training.