The_Use_of_Generative_AI_to_Advance_Cybersecurity_Education_Taxonomy

After searching on Google Scholar, the papers that did not match the criteria and papers with titles irrelevant to the study were discarded, and the remaining papers moved to the next stage. In this stage, the abstracts and conclusions were reviewed to assess each paper’s relevance to the study. Papers that did not align with the research goals were excluded. The remaining papers were then read in full to confirm their final relevancy to the research goals and the topic of this thesis. The selection process was first performed by the master student and then evaluated by the supervisor to ensure reliability of the findings.<br>Using the search string mentioned above on Google Scholar results in a total of 386 search results at the time of conducting the paper selection. After eliminating irrelevant papers, a total of 14 papers were found relevant to the research questions. Although the number of papers may appear limited, the research topic is relatively new, and there is a lack of studies available on this subject. As many as seven papers were published in 2024 and six were published in 2023. This recent surge in publications underscores both the growing academic attention of the use of AI to advance cybersecurity education and the need for further research to deepen understanding in this area. The selected papers and their related data, such as title, author(s), publication source, publisher, publication year, findings, and identified themes were extracted in an Excel file which can be accessed on Figshare public repository