## 3 Results  
The study selection process followed the PRISMA 2020 guidelines to ensure a systematic and transparent approach. It was conducted in multiple stages, which are visually presented in the PRISMA flowchart (Figure 1). The initial search across Google Scholar and Scopus yielded 37,800 results across both databases. It should be noted that Google Scholar provides only the first 1000 records of the list. After removing duplicates, 200 articles remained for title and abstract screening. At this stage, 120 studies were selected based on the inclusion criteria, focusing on AI applications in computer science education. A full-text assessment was then conducted, leading to the exclusion of 31 studies that did not meet the eligibility criteria. Reasons for exclusion included a lack of empirical research (n = 12), studies with a broad focus on general AI in education rather than computer science (n = 10), and papers that lacked relevant findings on AI's impact on learning outcomes (n = 9). Ultimately, 31 studies were included in this review for synthesis. The 31 studies included in this systematic review were categorized based on their publication year and type (scientific journal or conference proceedings), as presented in Table 3. The analysis revealed that 17 studies (55%) were published in scientific journals, indicating long-term contributions to the field. The remaining 14 studies (45%) were conference papers published in peer-reviewed conference proceedings. This suggests that research on AI in computer science education is actively evolving, with conferences serving as a platform for early dissemination of findings and emerging trends. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Reference** & **Title** & **Type of Publication** & **Journal/Conference Title** \\ \hline
2024 & [7] & Iris: An AI-Driven Virtual Tutor for Computer Science Education & Conference Proceedings & Innovation and Technology in Computer Science Education \\ \hline
2024 & [8] & A Comparative Study of AI-Generated (Gpt-4) And Human-Crafted MCQs in Programming Education & Conference & 26 th Australasian Computing Education Conference \\ \hline
2024 & [9] & Teachers’ And Students’ Perceptions of AI-Generated Concept Explanations: Implications for Integrating Generative AI in 2023 Computer Science Education & Scientific Journal & Computers and Education: Artificial Intelligence \\ \hline
2024 & [10] & Evaluating the Effectiveness of Large Language Models in Introductory Computer Science Education: A Semester-Long Field Study & Conference Proceedings & Eleventh ACM Conference on Learning@ Scale \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of study year and type. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Reference** & **Title** & **Type of Publication** & **Journal/Conference Title** \\ \hline
2024 & [11] & Enhancing Educational Efficiency: & & \\  & Generative AI Chatbots and & & Scientific Journal & Computer Applications in \\  & & DevOps in Education 4.0 & & Engineering Education \\ \hline
2024 & [12] & Assessing AI Detectors in & & 46 th International \\  & Identifying AI-Generated Code: & & Conference & Conference on Software \\  & & Implications for Education & & Engineering Education and \\  & & & Training \\ \hline