## 4 Discussion
### Instructional Design for AI Integration in Computer Science Education  
Research findings indicate that the integration of AI into CS education has been achieved through a variety of pedagogical approaches. AI applications span across multiple areas of computer science, with a predominant focus being on programming instruction. Research highlights AI's role in teaching specific programming languages (e.g., Java, Python) and general programming concepts [3; 6]. Given the fundamental role of programming in computer science, AI tools play a crucial role in enhancing instruction by providing real-time coding assistance, generating explanations for complex concepts, and offering multiple solution approaches. These capabilities align with existing literature emphasizing the effectiveness of automated tutoring and real-time debugging in coding instruction [44]. Beyond programming, the integration of AI extends to database instruction, where studies [4; 23; 26; 27; 28; 29; 30] have explored the use of AI-driven tools to support both students and instructors and enhance learning and teaching. Chatbot-based platforms have been utilized to facilitate SQL learning and assist students in practicing complex database queries independently [14; 23]. Additionally, large language models (LLMs) such as ChatGPT have been evaluated for their effectiveness in solving SQL assignments, their potential implications for academic integrity in online assessments, and their use as grading tools [26]. Research also emphasizes the role of LLM-driven chatbots in higher education database courses, highlighting their potential to enhance engagement and comprehension [27]. Furthermore, investigations have been conducted into AI-driven microlearning methodologies, which have been shown to offer benefits such as customized instruction and the segmentation of learning material into manageable units [28; 29; 30]. Given the conceptual complexity of databases, expanding research into AI-driven tutoring systems could enhance students' understanding of complex query structures, normalization processes, and database design principles. Several instruction models have been identified in AI-supported computer science education. Problem-based learning is the most frequently applied approach [3], encouraging students to engage in real-world problem-solving. This approach fosters critical thinking and collaboration as students debug code, optimize algorithms, and explore alternative solutions. Similarly, collaborative learning leverages AI as a support tool for discussions, troubleshooting, and knowledge sharing, enhancing both technical and social learning experiences [1; 38]. Another widely implemented approach is experiential learning, which emphasizes hands-on engagement through interactive AI-based platforms that provide real-time feedback and adaptive learning pathways. Additional instructional models include inquiry-based learning, where students pose and investigate their own questions, and competency-based learning, which focuses on skill mastery through AI-driven personalized assessments. Additionally, drill and practice, project-based learning, flipped classroom, and game-based learning have been incorporated to enhance engagement and provide diverse learning experience [4]. AI-powered platforms such as Codex, CodeTutor, and ChatbotSQL have demonstrated effectiveness in supporting autonomous learning. However, the review also indicates that most AI-driven studies focus on higher education settings, with limited research on AI's use in K-12 computer science instruction [5]. This may be due to the fact that computer science courses are mostly taught in higher education, although there are many attempts to teach some courses, such as programming or AI, at earlier educational levels (e.g., elementary or K-12) [5]. The use of AI in earlier educational stages, particularly for teaching fundamental programming and computational thinking concepts, is growing [4].