## 3 Results
### The Use of AI-Assisted Programming in Educational Approaches  
The integration of AI in programming education is leading to significant changes in teaching methodologies and learning experiences. Large language models and AI-driven code generation tools have been shown to provide personalized support, automate routine tasks, and generate educational resources, thereby fundamentally altering the landscape of programming education [10; 11; 12; 13; 15; 16; 17; 18; 19; 20; 24; 25; 26; 27; 29; 30; 31; 32; 33; 34; 35; 36; 37]. AI-assisted tools have been shown to enhance learner autonomy by offering tailored learning experiences, reducing negative emotions such as frustration and insecurity, and fostering engagement. Educators can leverage these tools to provide personalized feedback and resources, allowing them to focus on social learning opportunities that further motivate students [31]. The capacity of AI to automatically generate programming exercises, explanations, and feedback contributes to a reduction in instructors' workload while expanding the variety of available learning materials [32; 34]. Moreover, AI tools improve the clarity and actionability of programming error messages, facilitate multiple solution approaches, and enable students to concentrate on higher-level problem-solving by automating lower-level coding tasks. These features enable students to engage with specification-focused learning, emphasizing problem definition over manual coding [32]. Additionally, studies have demonstrated that AI-assisted programming can enhance intrinsic motivation, reduce programming anxiety, and improve performance, particularly in collaborative settings such as AI-assisted pair programming [35]. Recent empirical studies provide further evidence of AI's impact on programming education. For instance, a semester-long study revealed that students utilizing CodeTutor, an LLM-powered assistant, demonstrated notable enhancements in their final scores compared to a control group [10]. The study also found that students with no prior experience with AI tools benefited the most, suggesting that AI-assisted programming can help bridge gaps in foundational programming skills [10]. Similarly, another research study revealed that while educators recognize AI's potential in enhancing instruction and automating grading, concerns remain about student reliance on AI tools and academic integrity [20]. The use of AI chatbots, particularly ChatGPT, has been explored as a means to support programming education in various ways. Studies have shown that AI tools can generate code snippets, debug errors, and provide structured explanations, which have been beneficial for both novice and experienced programmers [11; 13]. Research indicates that AI-generated learning materials can enhance comprehension by offering adaptive learning tasks and real-time feedback [17; 19]. Despite these advantages, challenges remain regarding the reliability of AI-generated content and the potential for misuse. A primary concern is that students may become overly dependent on AI-generated solutions, potentially undermining their ability to develop critical problem-solving skills. The quality and reliability of AI-generated content vary, particularly in complex problem scenarios, raising concerns about the accuracy of solutions provided by LLMs [15; 16]. Additionally, the current state of AI-generated content detectors often fails to effectively differentiate between human-written code and AI-generated code, underscoring existing limitations in enforcing academic integrity [12]. Studies also show that while tools like ChatGPT or Codex can produce syntactically correct code, their outputs may contain conceptual errors or security vulnerabilities that students may fail to identify without proper training [24]. Furthermore, the variability and occasional inconsistency in AI responses require developers to be equipped with the skills to assess, validate, and refine AI-generated code.