## 2 Methodology
Although inter-rater reliability was not calculated, this consensus-driven approach helped ensure consistency and accuracy in the selection process. Once relevant studies were selected, data extraction followed a standardized process. The extracted data included study details (authors, year, publication type), research methods, AI applications in education, instructional approaches, reported benefits and challenges, and study outcomes. The reviewers worked separately during the initial extraction phase to ensure accuracy and reduce bias. Any discrepancies in extracted data were discussed and resolved through consensus. No automation tools were used in this process. Where necessary, additional clarifications were obtained by cross-referencing details from the original study reports. The review focused on extracting data related to AI applications in programming and database instruction, specifically examining instructional approaches, AI roles, learning benefits, and challenges. Outcomes sought included improvements in student engagement, personalized learning, and skill development, as well as challenges like AI accuracy, biases, and academic integrity concerns. All relevant results reported in the selected studies were considered, prioritizing those with quantitative evidence of AI effectiveness or direct pedagogical comparisons. Studies lacking explicit educational outcomes or focusing solely on AI technology development without instructional application were excluded from synthesis. In addition to educational outcomes, data were extracted on study characteristics, including participant demographics (e.g., student level, sample size), AI intervention types, and instructional settings. If key details were missing or unclear, assumptions were made based on the study context, or information was inferred from related sections. Studies lacking essential methodological details were excluded from the synthesis to maintain data reliability. A risk of bias assessment was performed on criteria such as sample size, research design, data collection methods, and potential conflicts of interest, and each study was classified by the two reviewers independently assigning a low, moderate, or high risk of bias classification. Due to variability in study designs and outcome reporting, a meta-analysis was not conducted and findings were synthesized narratively. Studies were deemed eligible for synthesis based on their relevance to AI applications in computer science education, particularly in programming and database instruction. Each study's intervention characteristics, such as AI tool type, instructional approach, and target learning outcomes, were tabulated and compared against the predefined inclusion criteria. Studies that focused solely on AI development without an educational application were excluded. Eligible studies were then grouped by AI roles, instructional strategies, and reported learning outcomes, allowing for a structured synthesis of findings. Synthesis of the data was then used to address the research questions. Results were organized into tables and figures, summarizing study characteristics, AI applications, instructional approaches, and reported outcomes. Studies were grouped based on AI roles, benefits, challenges, and educational contexts to facilitate comparison. Visual representations, such as charts, were used to highlight trends and distributions across the included studies. The risk of bias due to missing results was assessed by examining publication sources and the completeness of reported outcomes. Studies lacking sufficient methodological details or clear reporting of findings were noted as having a potential reporting bias. No formal statistical methods were applied to detect publication bias, but the narrative synthesis accounted for possible underreporting by considering study limitations.