Artificial Intelligence in Safety Education and Training: A Conceptual Review of Applications, Learning Processes and Outcomes
Abstract
This study examines the role of artificial intelligence (AI) in enhancing safety education and training through a conceptual and structured narrative review of recent literature. Safety education remains a critical component of risk prevention and professional competence across sectors such as healthcare, engineering, and industrial operations. However, traditional instructional approaches often lack the adaptability and interactivity required to address complex and evolving safety challenges. Drawing on peer-reviewed studies published between 2021 and 2026, this paper synthesises current evidence on the application of AI technologies, including adaptive learning systems, intelligent tutoring systems, and simulation-based environments, in safety training contexts. The review identifies key mechanisms through which AI enhances learning, particularly through personalised instruction, real-time feedback, and experiential learning. These processes contribute to improved safety knowledge, risk awareness, and compliance behaviour. The study also highlights moderating factors such as AI literacy, educator readiness, infrastructure, and ethical considerations, which influence the effectiveness of AI integration. While AI presents significant opportunities for improving safety education, challenges related to data privacy, technological access, and institutional capacity remain critical. The study concludes that effective implementation of AI in safety education requires a balanced approach that integrates technological innovation with pedagogical, ethical, and contextual considerations.
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