The current Accreditation Council for Occupational Therapy Education (ACOTE) Standards include a provision for the use of experiential learning methods as level I fieldwork experiences by entry-level occupational therapy (OT) education programs (ACOTE, 2018). Included in these experiences are two specific types of simulation: simulated environments and standardized patients. Earlier versions of the ACOTE Standards did not allow for the use of simulation as level I fieldwork experiences. This provision may help mitigate a shortage of level I and level II fieldwork placements and allow academic programs to provide consistent quality level I fieldwork across students (American Occupational Therapy Association [AOTA], 2017). This use of simulation as a fieldwork training method is an emerging area of OT education that has limited research on its use and best practice. This doctoral project sought to contribute to the existing knowledge by conducting a research study which investigated the use of both simulated environments and standardized patients by academic programs, as well as identifying the primary supports and barriers to its implementation. The project included the creation, distribution, and analysis of a national survey of entry-level OT programs. The survey found that the main barrier and support to implementation of simulation was funding and that private institutions are more likely to utilize standardized patients than public institutions. The results of this study will help inform future ACOTE Standards, provide both the American Occupational Therapy Association and ACOTE with additional information to help determine how to best provide resources for academic programs that facilitate successful implementation of the simulation methods, and help identify programs that can participate in the dissemination of best practice in the use of simulation as fieldwork experiences. The author recommends that ACOTE should also consider mandating the use of simulation, along with other experiential learning activities, as partial fulfillment of level I fieldwork requirements, to allow for better access to funding, decrease the fieldwork burden on traditional fieldwork sites, and allow for more consistent level I fieldwork experiences.

Implementing object detection methods for runway detection during landing approaches is limited in the safety-critical aircraft domain. This limitation is due to the difficulty that comes with verification of the design and the ability to understand how the object detection behaves during operation. During operation, object detection needs to consider the aircraft's position, environmental factors, different runways and aircraft attitudes. Training such an object detection model requires a comprehensive dataset that defines the features mentioned above. The feature's impact on the detection capabilities needs to be analysed to ensure the correct distribution of images in the dataset. Gathering images for these scenarios would be costly and needed due to the aviation industry's safety standards. Synthetic data can be used to limit the cost and time required to create a dataset where all features occur. By using synthesised data in the form of generating datasets in a simulated environment, these features could be applied to the dataset directly. The features could also be implemented separately in different datasets and compared to each other to analyse their impact on the object detections capabilities. By utilising this method for the features mentioned above, the following results could be determined. For object detection to consider most landing cases and different runways, the dataset needs to replicate real flight data and generate additional extreme landing cases. The dataset also needs to consider landings at different altitudes, which can differ at a different airport. Environmental conditions such as clouds and time of day reduce detection capabilities far from the runway, while attitude and runway appearance reduce it at close range. Runway appearance did also affect the runway at long ranges but only for darker runways.