Sustained use of generative artificial intelligence (GenAI) in programming education: a systematic review of empirical evidence on adoption, learning outcomes, and continuance intention

This systematic review explores how Generative Artificial Intelligence (GenAI) technologies have been adopted and sustained within programming education from 2023 to 2025, uncovering their applications, educational benefits, and challenges. This review draws on 45 empirical studies identified through five major academic databases, including Scopus, Web of Science, IEEE Xplore, SpringerLink, and Google Scholar. The analysis reveals that GenAI could enhance programming education by supporting code generation, debugging assistance, personalised explanations, feedback provision, and learner engagement. However, key challenges remain concerning inaccurate outputs, academic integrity, learner dependency, trust, and unequal access to AI-supported learning resources. This study suggests that educators should strategically employ GenAI as a complementary learning resource, maximising its educational benefits while addressing potential limitations. The findings highlight the importance of educator preparation, institutional guidance, and responsible AI integration, while future research should further investigate long-term continuance intention behaviour, comparative effectiveness of emerging GenAI programming tools, and sustainable implementation strategies across diverse educational contexts.