Recent Advances in Artificial Intelligence for Computer Science Applications

Abstract

Artificial Intelligence (AI) has become a cornerstone of modern computer science, driving transformative changes across domains such as software engineering, cybersecurity, data analytics, and human–computer interaction. The rapid progression from rule-based systems to advanced neural architectures, generative models, and hybrid learning techniques has expanded the scope and impact of AI applications. This paper reviews recent advances in AI technologies, including deep learning, reinforcement learning, generative models, and multimodal systems, highlighting their practical applications and benefits. It further examines challenges such as ethical considerations, data privacy, computational demands, and model interpretability. By synthesizing findings from contemporary literature and case studies, the paper outlines emerging opportunities in AI–computer science integration, particularly in quantum computing, sustainable AI, and human–AI collaboration. The study concludes that while AI offers unprecedented potential to solve complex problems, its responsible and sustainable deployment requires addressing societal concerns, enhancing transparency, and fostering interdisciplinary research.