Transforming Aviation Technical Authoring with Generative Artificial Intelligence: Toward Automation and Efficiency

Abstract

Over the coming decades, the aviation sector is expected to witness substantial growth driven by increasing global demand for air travel, necessitating efficient and precise technical documentation to manage the growing complexity of maintenance. As technical authoring processes remain labor-intensive and prone to inconsistencies, this study investigates the potential of Generative artificial intelligence (GenAI) to automate the creation of Engineering Orders (EOs), which are derived from Airworthiness Directives (ADs) and Service Bulletins (SBs). A three-phase approach is adopted to generate EOs from ADs and SBs, enabling a structured evaluation of GenAI’s performance in technical authoring. Expert reviews are integral to refining AI outputs, emphasizing the importance of integrating AI capabilities with human expertise. This study validates the effectiveness of GenAI in aviation technical authoring and introduces a scoring tool to evaluate the quality of AI-generated documentation across several dimensions: (1) technical knowledge; (2) accuracy; (3) comprehensiveness; and (4) usability and flexibility. The findings highlight that the synergy between AI-generated content and expert review significantly improves documentation quality by mitigating AI limitations, reducing the time required to produce technical documentation and ensuring practical applicability. The proposed approach provides a scalable framework that can be adapted for use in various industries requiring precise technical documentation.

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