Artificial Intelligence for Predictive Change Management in Information Systems Projects: Cognitive Load Analysis of Cybersecurity Interfaces for Autonomous Vehicle Operators
Published 03-03-2024
Keywords
- Cybersecurity Interfaces
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Abstract
The integration of cybersecurity interfaces within autonomous vehicle systems presents significant challenges, particularly in understanding the cognitive load on operators. This study investigates the impact of command, control, and monitoring interfaces (CCMI) on the cognitive load of both expert and novice operators within an AI-driven Predictive Change Management framework. While prior research has largely overlooked this aspect, our work evaluates and compares the performance of various cybersecurity interfaces by utilizing actors from different user groups. By analyzing cognitive load data from experimental mock-ups, this paper offers valuable insights into how AI can optimize interface design to enhance user performance in dynamic, change-driven environments. The findings also support the certification of interfaces for human factors engineering, ensuring that both novice and expert operators can manage cybersecurity effectively during organizational transitions in autonomous vehicle projects.[1]
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