The Implementation of Artificial Intelligence (AI) in Aviation Collegiate
Education: A Simple to Complex Approach
Authors: Ziakkas Dimitrios , Pechlivanis Konstantinos , Keller Julius 
Published: February 2023

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.