Intelligent Code Analysis and Feedback Generation
Abstract
As digital literacy grows vital in today's job market, programming education faces challenges due to its abstract nature, syntax sensitivity, and high error-proneness. This chapter explores how Artificial Intelligence (AI) can enhance programming education by detecting common coding errors, offering instant, personalized feedback, and supporting adaptive learning paths. Leveraging machine learning, NLP, and code analysis, Intelligent Tutoring Systems (ITS) help students grasp complex coding concepts by analyzing code patterns, syntax trees, and embeddings across languages like Python and Java. The chapter presents case studies using hybrid ML and rule-based feedback engines, reducing instructor workload and boosting student outcomes. It also discusses challenges like model transparency, unseen errors, and over-reliance. Future directions include LLMs for code explanation and federated learning for secure, scalable error prediction. This chapter offers practical insights for educators to build more effective, engaging, and supportive programming classrooms.