## 2 Methodology  
This systematic review follows the PRISMA 2020 guidelines to ensure transparency and reproducibility. The review process included the following key steps: defining research questions, establishing inclusion and exclusion criteria, conducting a comprehensive literature search, selecting relevant studies, extracting data, and synthesizing results. A systematic screening process ensured reliability by removing duplicates, screening titles and abstracts, and evaluating full texts. The focus was on the analysis of AI applications in the domain of computer science education, with a particular emphasis on their role in database instruction. Studies published between 2020 and 2025 were included, provided that they focused on AI in computer science and database education and employed empirical research methods such as experimental studies or mixed-method approaches. Inclusion and exclusion criteria are presented in Table 2. Exclusion criteria included studies published before 2020, non-English publications, and those lacking a clear research methodology. The PRISMA flowchart is used to visually represent the selection process, illustrating the number of records identified, screened, and ultimately included in the review. The collected data were then categorized based on three distinct research methods: empirical studies, qualitative analyses, and systematic reviews. Additionally, the AI applications were categorized into three groups: programming, databases, and computational thinking, among others. The educational design was analyzed, identifying AI's function as a tutor, assistant, or automation tool. The reported benefits, challenges, and limitations of AI in computer science and databases education were also extracted. The literature search was conducted in February 2025, with the final data extraction completed in March 2025. All searches were performed using two major academic databases, Google Scholar and Scopus, to ensure a comprehensive collection of relevant studies. The search strategy involved predefined Boolean search queries incorporating key terms related to AI, computer science, database instruction, and education. The exact keyword combinations used for both databases were (\"generated AI\" OR \"Artificial Intelligence\" OR \"chatbots\" or \"AI\" OR \"LLM\" OR \"LLMs\" OR \"AI-driven\" OR \"AI-augmented\" OR \"AI-assisted\") AND (\"database\" OR \"Computer Science\" OR \"Relational Databases\" OR \"Informatics\" or \"Computational Thinking\" OR \"Programming\" OR \"Algorithms\") AND (\"education\" OR \"training\" OR \"Learning\" OR \"Instruction\" OR \"Tutoring\" OR \"Intervention\"). The Boolean search queries were applied explicitly to the title, abstract, and keyword fields when searching the Scopus database. Due to the inherent limitations of \begin{table}
\begin{tabular}{c c} \hline \hline
**Inclusion Criteria** & **Exclusion Criteria** \\ \hline Studies published between 2020 and 2024 & Studies published before 2020 \\ \hline Studies in English language & Studies in a language other than English \\ \hline Studies using AI in education and CS and & \\ database teaching and learning & Studies that examine the use of AI \\ Studies using AI tools in computer & theoretically rather than through \\ engineering or programming & experimental application \\ development & \\ \hline Studies using AI with research methods & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Inclusion and exclusion criteria. the Google Scholar platform, the same Boolean queries were applied broadly across the available metadata (including titles and abstracts). Given that Google Scholar does not allow fine-grained control of search fields, results from this source were limited to the first 1000 records returned by relevance ranking. The initial screening of titles and abstracts, as well as the subsequent full-text review, was performed independently by two authors. Disagreements regarding the inclusion or exclusion of studies were resolved through detailed discussion until consensus was reached.