Generative AI as a Learning Tool in Aviation: A Review of Pedagogical Approaches and Outcomes

Abstract

This paper examines the integration of Generative Artificial Intelligence (GenAI) into aviation training, focusing on instructional strategies, learning objectives, and key challenges. Using a systematic literature review approach, relevant studies were gathered from academic sources such as Google Scholar, IEEE Xplore, and PubMed, emphasizing the educational applications of GenAI. Some research suggests that GenAI is beginning to transform aviation training by giving instructors the ability to customize lessons based on how each student learns best and how quickly they progress. This adaptability often leads to higher student engagement and better retention of material. This flexibility tends to keep learners more involved and helps them absorb information more effectively. GenAI also plays a role in developing realistic practice environments, allowing trainees to safely build critical skills. Still, its growing presence in education raises important concerns. Questions around fairness, the potential for biased content, and the risk that students or educators may rely too heavily on these tools are still unresolved. This review highlights these issues and suggests that further research is necessary to better understand how GenAI affects aviation training in the long run. It also underlines the importance of building well-informed teaching methods and ethical safeguards to guide its integration. As the aviation sector adopts new technologies, careful planning will be essential to ensure that innovation supports not compromises safety, learning quality, and professional standards.