The Implementation of Artificial Intelligence (AI) in Aviation Collegiate Education: A Simple to Complex Approach

Abstract

Aviation and air travel have always been among the most innovative industries throughout history. Both the International Air Transportation Authority (IATA) Technology Roadmap (IATA, 2019) and the European Aviation Safety Agency (EASA) Artificial Intelligence (AI) roadmap propose an outline and assessment of ongoing technological prospects which change the aviation environment with the implementation of AI from the initial phases of the collegiate education. Using traditional flight simulators is an essential part of initial and recurrent training for pilots. These simulators help pilots achieve and maintain proficiency in normal and abnormal circumstances that may arise during flight operations (Myers et al., 2018). The upskilling performed through simulators are typically completed at a far cheaper cost than the training completed in the air. However, the capital cost of simulator units can range from USD 10-15 million, which results in an exorbitant cost recovery of approximately USD 1,500 per session (Bent & Chan, 2010). This makes it expensive for air carriers and undergraduate pilot training programs to comply with mandated flight and simulator training requirements. In addition, because the COVID-19 epidemic is so widespread, companies that provide flight training have been entrusted with developing novel ways to instruct their students, such as through remote pilot-to-student education. The Federal Aviation Administration (FAA) (2020) acknowledges the use of non-traditional technologies that can successfully fulfill the requirement for ongoing training in ever-changing regulatory standards. The following four steps follow a simple-to-complex implementation approach that is advocated for using AI in the instruction provided by college aviation programs: 1.) Activities relating to outreach and recruitment 2.) Introducing new students to the PFP (Professional Flight Program). 3.) Additional training in addition to fundamental and advanced jet instruction 4.) Research aimed at mastery of pilot competencies, increasing student self-efficacy, and decreasing the number of crew operations.Alterations to aviation training will affect the performance of humans and decision-making. The research used an AI methodology that accepted "any technology that appears to replicate the performance of a person." The AI approach followed this broad definition. The thematically selected research on AI decision-making in collegiate aviation trainees' perception and experience was structured based on an analysis of the available literature concerning the current uses of AI in aviation. The use of artificial intelligence in pilots' training and operations was investigated through a combination of interviews with Subject Matter Experts (including Human Factors analysts, AI analysts, training managers, examiners, instructors, qualified pilots, and pilots under training) and questionnaires (which were distributed to a group consisting of professional pilots and pilots under training).The findings were reviewed and evaluated concerning the appropriateness of the AI training syllabus and the notable differences between them in terms of the decision-making component.