Computing education using generative artificial intelligence tools: A systematic literature review - Friday Joseph Agbo; Chris Olivia; Godsalvation Oguibe; Ismaila Temitayo Sanusi; Godwin Sani (Computers and Education Open)
Recent advances in generative artificial intelligence (GenAI) are revolutionizing computing education, causing paradigm shifts from the traditional teaching and learning technique. Studies are exploring GenAI tools in computing classes from intro to advanced topics with the aim to showcase how to reshape computing education in this new era of GenAI. This study examined the computing education research landscape to unravel how GenAI tools have been used in that domain, what are the characteristics of those studies in terms of computing topics, context, and tools, offering insights into the pros and cons for integrating GenAI in computing education based on the performance indicators reported in the literature. This study employed a systematic literature review approach to identify and analyze 78 relevant articles. The findings of this study show that educators are exploring GenAI tools in computer sciences classes from K-12 through graduate levels. Beyond programming education, GenAI has also been explored in college upper-level computer science courses such as Computer Graphics and Human-Computer Interaction. The performance analysis of these tools are presented in this study, indicating a progressive advancement from when the technology was introduced. This study also discusses learning outcomes, good practices, and potential risks to avoid when exploring GenAI, as reported in the studies, which could guide how computing educators design their instructional strategies using GenAI. This study contributes to broadening the understanding of exploring, adapting, or using GenAI in computer science education and may spark interest among educators who are unwilling to explore GenAI or may not understand how or what strategies to adopt.