• Exploring core theories, practical methodologies, and industry applications to enhance the reliability, durability, and operational efficiency of engineering systems.

• Investigating how artificial intelligence and big data analytics optimize reliability assessment, fault diagnosis, and predictive maintenance in complex systems.

• Focusing on advanced maintenance strategies, condition monitoring techniques, and prognostic models to minimize downtime and extend asset lifecycle.

• Addressing quality assurance frameworks, risk identification and mitigation, and safety management systems to ensure engineering project integrity and compliance.

• Evaluating the ethical dimensions of deploying AI tools in education, including concerns about data protection, algorithmic bias, and equitable access.

• Examining curriculum design to ensure students acquire the necessary AI literacy and transferable skills for future employment.

• Discussing how AI may reshape teaching roles, redefine pedagogical approaches, and support faculty development in the digital age.