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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.

This research aims to present, identify, and propose the implementation of AI technology in aviation decision-making, as well as examine how AI can affect the transition from multi-crew to eMCO and SiPO, based on the rationale that the single-pilot human operator having accessible data in a timely and naturally interactive fashion could enhance natural decision making (NDM) (Klein, 2008; Orasanu & Fischer, 1997).According to the industrial roadmaps, the first certification of assistance for pilots is anticipated to occur in the year 2025, and this will be followed by a gradual transition to full autonomy sometime around the year 2035. The progression of events in the field of commercial air transport can be broken down into three distinct stages:•First step: crew assistance/ augmentation (2022-2025)•Second step: human/ machine collaboration (2025-2030)•Third step: autonomous commercial air transport (2035+)There have been identified two different operational concepts:Extended Minimum-Crew Operations (eMCOs), formerly known as "Reduced Crew Operations," in which single-pilot operations are permitted during the cruise phase of the flight with a level of safety similar to that of today's two-pilot operations (to be implemented beginning in the year 2025).Single-Pilot Operations (SiPOs), in which, at a later stage, end-to-end single-pilot operations might be allowed, also based on a level of safety equivalent to today's two-pilot operations, to be implemented as of the year 2030. Single-Pilot Operations (SiPOs), in which, at a later stage, end-to-end single-pilot operations might be allowed.The proposed artificial intelligence aviation decision-making research in cockpit design and users' experience was constructed by first surveying the current literature about Artificial Intelligence (AI). The findings point to the difficulties artificial intelligence poses, including its limitations and users' resistance, in shifting from multi-crew operations to e-MCO and SiPO. This resistance to change should be considered when designing any potential upgrades to the AI cockpit design or user interactions. However, the existing commercially available  AI technology may be ready to serve some low-impact or non-time-critical applications (for example, weather in destination and alternate airports update during the cruise phase) in this transitional period to eMCOs and SiPOs, which would postpone the necessity for a complete flight deck redesign at this time (Stanton & Harris, 2015). The utilization of AI for the administration of systems and the retrieval of information has the potential to improve both the perception (Level 1 SA) and comprehension (Level 2 SA) of pilots (Endsley, 1995). Therefore, the single-pilot human operator in the NDM cockpit environment who has data accessible promptly and in a naturally engaging fashion would be able to make judgments that are more fulfilling and closer to optimums in the NDM environment (Klein, 2008; Orasanu & Fischer, 1997).

Aviation and air travel have always been among the businesses at the forefront of technological advancement throughout history. Both the International Air Transportation Authority's (IATA) Technology Roadmap (IATA, 2019) and the European Aviation Safety Agency's (EASA) Artificial Intelligence (AI) roadmap (EASA, 2020) propose an outline and assessment of ongoing technological prospects that change the aviation environment with the implementation of AI from the initial phases. New technology increased the operational capabilities of airplanes in adverse weather. An enhanced flight vision system (EFVS) is a piece of aircraft equipment that captures and displays a scene image for the pilot, allowing for improved scene and object detection. Moreover, an EFVS is a device that enhances the pilot's vision to the point where it is superior to natural sight. An EFVS has a display for the pilot, which can be a head-mounted display or a head-up display, and image sensors such as a color camera, infrared camera, or radar. A combined vision system can be made by combining an EFVS with a synthetic vision system. A forward-looking infrared camera, also known as an enhanced vision system (EVS), and a Head-Up Display (HUD) are used to form the EFVS. Two aircraft types can house an EFVS: fixed-wing (airplane) and rotary-wing (helicopter).Several operators argue that the use of Enhanced Flight Vision Systems (EFVS) may be operated without the prior approval of the competent authority, assuming that the flight procedures, equipment, and pilot safety barriers are sufficiently robust. This research aims to test pilots' readiness levels with no or little exposure to EFVS to use such equipment (EASA, 2020). Moreover, the Purdue simulation center aims to validate this hypothesis. The Purdue human systems integration team is developing a test plan that could be easily incorporated into the systems engineering test plan to implement Artificial Intelligence (AI) in aviation training globally and evaluate the results. Based on guidelines from the International Air Transport Association (IATA), the Purdue University School of Aviation and Transportation Technology (SATT) professional flying program recognizes technical and nontechnical competencies. Furthermore, the Purdue Virtual Reality research roadmap is focused on the certification process (FAA, EASA), implementation of an AI training syllabus following a change management approach, and introduction of AI standardization principles in the global AI aviation ecosystem.

The aviation industry is characterized by innovation, change management, and human factors implementation in flight operations. The aviation industry anticipates the Single Pilot Operations (SiPO) implementation in commercial airliners. Further de-crewing on commercial airline jets would necessitate using artificial intelligence (AI) in the flight deck to support the pilot duties. This paper outlines human factors and ergonomics (HF/E) certification concerns regarding Human System Integration (HSI). The International Air Transportation Authority's (IATA) Technology Roadmap (IATA, 2019) and the European Aviation Safety Agency's (EASA) Artificial Intelligence (AI) roadmap give an overview and evaluation of current technology trends that will change the aviation environment with the use of AI and the introduction of extended Minimum Crew Operations (eMCO) and Single Pilot Operations (SiPO). A review of the existing research on Artificial Intelligence certification challenges in single pilot operations structured the research themes in cockpit design and users' perception-experience. AI certification challenges in future single pilot operations were examined through interviews with Subject Matter Experts (Human Factors analysts, AI analysts, regulators, test pilots, manufacturers, airline managers, examiners, instructors, qualified pilots, and pilots in training) and questionnaires were sent to a group of professional pilots and pilots in training. In the current regulatory environment, the associated risk-based approach for systems, equipment, and components is primarily driven by a requirements-based "development assurance" methodology during the development of their elements. Although system-level assurance may still necessitate a requirements-based approach, it is acknowledged that design-level layers that rely on learning processes – learning assurance cannot be addressed with only 'development assurance' techniques.Moreover, this research focuses on mitigating residual risk in the 'AI black box.' Results were analyzed and evaluated the Artificial Intelligence (AI) certification and learning assurance challenges under the future single pilot operations aspect.