Continual Learning in Artificial Intelligence: A Review of Techniques, Metrics, and Real-World Applications

Continual learning (CL) is a critical paradigm in artificial intelligence that enables models to learn sequentially from a stream of tasks while retaining previously acquired knowledge. Unlike traditional machine learning approaches that assume static datasets, CL aims to address real-world scenarios where data distributions evolve over time. However, CL models face significant challenges, including catastrophic forgetting, scalability, task interference, and the trade-off between stability and plasticity. This survey provides a comprehensive review of continual learning, covering key learning paradigms such as regularization-based methods, memory replay techniques, and dynamic architectural approaches. We discuss widely used evaluation metrics, benchmark datasets, and experimental protocols that facilitate fair comparisons among CL methods. Additionally, we explore real-world applications of CL in domains such as robotics, healthcare, natural language processing, cybersecurity, and recommender systems. Despite recent advances, several open challenges remain, including efficient memory management, task-free learning, privacy concerns, and improving forward and backward transfer. We highlight emerging research directions, including neuroscience-inspired learning mechanisms, self-supervised continual learning, meta-learning, and multi-modal CL. Finally, we discuss the integration of CL into large-scale foundation models and human-AI collaborative systems. By presenting an in-depth analysis of continual learning methodologies, challenges, and future prospects, this survey aims to provide researchers and practitioners with a structured understanding of the field and inspire further innovations in building adaptive, lifelong learning AI systems.