Application of Big Data and Artificial Intelligence in Pilot Training: A Systematic Literature Review

Abstract

In the aviation industry, large volumes of data are collected daily, varying from engine maintenance to flight monitoring information. The industry also gathers data from each flight that can indicate the level of pilot performance and allow for a thorough analysis to be presented, including tailored training programs to enhance pilot performance. This paper presents a systematic literature review of 10 related works, extracted and analyzed, to describe the current application of Big Data and AI in commercial airline pilots’ training, and to identify areas where the research is limited or missing. Based on this review’s descriptive qualitative content analysis method, the themes identified ranged from civilian to military training across different stages of the pilot training program. Findings reveal that research gaps exist in the current literature, where there is currently limited knowledge of how Big Data and AI can be systematically and comprehensively applied to pilot training that provides real-time feedback and span across the various stages of training. The review also shows that there is an absence of Micro-Adaptive Learning approaches to training programs that are tailored to individual pilots with varying learning styles and capabilities.