Date
5-20-2026
Department
School of Aeronautics
Degree
Doctor of Philosophy in Aviation (PhD)
Chair
Karl Winters
Keywords
artificial intelligence, AI, flight student, training, instructor, instruction, simulator, technology acceptance model, structural equation modeling
Disciplines
Aviation
Recommended Citation
Lamothe, Zachary L., "A Predictive-Correlational Study Investigating Flight Students' Perceptions of the Use of an Artificial Intelligence Instructor in the Simulated Flight Training Environment" (2026). Doctoral Dissertations and Projects. 8407.
https://digitalcommons.liberty.edu/doctoral/8407
Abstract
The purpose of this quantitative predictive-correlational study is to investigate how perceived ease of use, perceived usefulness, performance expectancy, and perceived enjoyment impact attitude towards use and the behavioral intention to use an artificial intelligence flight instructor in simulated flight training among aviation students earning an aeronautical degree at a collegiate flight training school in the Mid-Atlantic region. Aviation has benefited from different technological advances, and using artificial intelligence technology for flight training could be another improvement as it becomes increasingly sophisticated. The study adds to the literature by addressing the problem of limited information on the perception of artificial intelligence and whether flight students' perceptions indicate their attitude and behavioral intention to use an artificial intelligence instructor in their flight training. The sample is taken from collegiate students enrolled in an aviation degree program that includes flight training. Data was collected using an extended version of the technology acceptance model, which uses a survey design, and was distributed via email. Data was analyzed using the path model of the structural equation modeling method through SPSS Statistics AMOS. The results indicated that perceived usefulness and perceived enjoyment had a significant positive predictive relationship on flight students' attitude towards the use of artificial intelligence instructors, and attitude towards use significantly positively predicted their behavioral intention. Meanwhile, perceived enjoyment and perceived ease of use did not significantly predict attitude towards use. The findings can help inform educators and instructors about artificial intelligence training perceptions and areas of focus for implementation.
