Artificial Intelligence-Assisted Microlearning for Food Quality and Hazard Control: A Review of Digital Risk Communication and Traceability Systems

Otto Ketney

Volume 31, Issue 4
Pages: 392-416, 2025
ISSN: 2069-0053 (print), Agroprint
ISSN (online): 2068-9551

Food safety management increasingly depends on rapid information transfer and workforce awareness. This review examines how artificial intelligence (AI)-assisted microlearning supports hazard identification, quality assurance, and risk communication in the food industry. Recent evidence shows that short, adaptive learning modules integrated with AI analytics can enhance employee compliance with Hazard Analysis and Critical Control Points (HACCP) standards and improve traceability performance. By linking data capture, automated reasoning, and personalized feedback, AI microlearning creates a continuous improvement loop across production, inspection, and recall operations. The review also highlights how explainable AI (xAI) and human-in-the-loop (HIL) systems foster trust and accountability in digital training environments. Practical implications include faster hazard reporting, reduced training costs, and measurable gains in consumer safety indicators. Overall, AI-driven microlearning represents a scalable approach to strengthening food quality control and hazard prevention through data-driven communication and verifiable traceability.