Generative AI in Higher Education: A Systematic Review with Emphasis on Programming and Computer Science Education

Generative Artificial Intelligence (GenAI) is rapidly reshaping teaching and learning in higher education, particularly in pro-gramming and computer science education, where tools such as ChatGPT and GitHub Copilot are increasingly used for code generation, debugging, conceptual explanation, and personalised learning support. Despite this growing use, the literature re-mains fragmented, with limited synthesis across educational outcomes, adoption contexts, user perceptions, and factors in-fluencing sustained use. To address this gap, this study conducts a systematic literature review following PRISMA 2020 guide-lines. The review draws on a dataset of 60 empirical studies published between 2022 and 2025 and retrieved from major academic databases. The selected studies were analysed using a mixed synthesis approach that combines descriptive mapping with thematic analysis. The synthesis shows that GenAI can improve coding support, debugging efficiency, conceptual un-derstanding, and student engagement, especially in program-ming-related learning contexts; however, adoption remains une-ven across regions, institutions, and course settings, while con-cerns related to academic integrity, over-reliance, reliability, and unequal access persist. By integrating findings across learn-ing outcomes, adoption patterns, user perceptions, and continu-ance-related factors, this review provides a more structured understanding of GenAI use in higher education, with particular emphasis on programming and computer science education. The study highlights the need for AI literacy, ethical governance, and inclusive institutional support to enable more responsible and sustainable GenAI integration.

@article{Yalwa2026,
  title     = {Generative AI in Higher Education: A Systematic Review with Emphasis on Programming and Computer Science Education},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Abdulaziz Saidu Yalwa and Mohd Shahizan Othman and Lizawati Mi Yusuf and Muteb Sinhat Almarshadi},
  doi       = {10.14569/IJACSA.2026.0170531},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170531}
}