User acceptance of AI-powered training: extending the technology acceptance model (TAM)

Abstract Despite advancements in technological defenses, human error still remains a crucial role in cyber incidents, highlighting the need for effective cybersecurity awareness (CSA) and training. Artificial intelligence (AI) presents a promising solution for the enhancement of cybersecurity training. Despite their potential, the adoption and efficacy of AI-driven cybersecurity training tools remain insufficiently explored, particularly in relation to user acceptance. Thus, this study seeks to examine the factors that influence user acceptance of AI-driven cybersecurity training tools. This study addresses this gap by extending the technology acceptance model to incorporate CSA, considering trust in AI and perceived risk as critical mediating variables of behavioral intention. A total of 435 individuals, both Saudi and foreign, working in various industries in Saudi Arabia, were surveyed. The data was analyzed using structural equation modeling to examine the variable correlations. The findings reveal that CSA significantly influences key mediating factors, including trust and perceived risk, which in turn drive behavioral intention. However, perceived usefulness and perceived ease of use show weaker mediating roles. The study advances theoretical understanding by challenging traditional assumptions, such as the negative framing of perceived risk, and demonstrates its dual role in encouraging tool adoption. The model explained 64% of the variance in intentions. The findings highlight the need for organizations to design transparent, engaging, and user-focused CSA campaigns that build trust and demonstrate the usability of AI-powered tools. This study contributes to bridging the gap between technological advancements and human behavior, providing a comprehensive framework for promoting AI adoption and strengthening cybersecurity resilience in an increasingly digital world.