## 3 Results
### Benefits, Challenges, and Limitations
#### 3.4.2 Challenges  
The integration of generative AI tools, such as ChatGPT, presents challenges related to content accuracy, problem complexity, and evaluation reliability. AI systems often struggle with diverse topics, especially with erroneous data inputs or complex queries [22; 25; 28]. In coding tasks, ChatGPT frequently generates incorrectly formatted outputs, causing syntactical and logical errors in code [9; 24]. Despite advancements, it occasionally fails to evaluate programming solutions correctly, causing misinterpretations of expected outputs and flawed debugging assistance [17; 24].  
A significant concern is the inability of AI content detectors (AIGC) to distinguish between human-written and AI-generated code due to varying syntax structures in programming languages. Tools fail to detect AI submissions, raising concerns about academic integrity [12]. This highlights the risk of academic dishonstly, as students may rely on AI to generate SQL code, undermining the authenticity of their work and the integrity of assessments [26]. Additionally, concerns about biases and overreliance on AI hinder the development of critical thinking and problem-solving skills in students [18; 19; 22]. AI tools face technical constraints, including high computational requirements, slow response times, and integration challenges, which limit their scalability in education [11; 13].  
In the context of database education, AI tools encounter notable limitations, particularly in terms of providing contextually appropriate explanations. This deficiency can lead  
\begin{table}
\begin{tabular}{c c} \hline
**Benefits** & **Reference** \\ \hline Student support and guidance & [10; 16; 17; 19; 23; 26; 27; 28; 29; 30] \\ Enhanced learning experience & [7; 12; 16; 25; 26; 27; 28; 30] \\ Efficiency and productivity & [8; 9; 12; 20; 22; 26; 27; 29; 30] \\ Code writing and debugging & [11; 13; 15; 20; 22; 24; 26; 27] \\ Real-time feedback & [13; 17; 22; 23; 26; 27; 28; 30] \\ Personalized learning & [12; 19; 23; 25; 26; 27; 28; 30] \\ Teacher assistance & [8; 9; 12; 19; 25; 29] \\ Skill development & [14; 21; 23; 27; 30] \\ Creativity and critical thinking & [9; 22; 24; 29; 30] \\ \hline \end{tabular}
\end{table}
Table 6: Benefits of AI utilization in computer science instruction.  
to misconceptions and inaccurate learning outcomes [14; 23]. For instance, ChatGPT faces challenges in interpreting SQL queries and determining if different queries yield identical results, largely due to the absence of a clear theoretical foundation for SQL equivalence [26]. This can lead to errors in grading and misinterpretations of logical relationships between queries [26].  
Additionally, an excessive reliance on AI tools in database education has the potential to hinder students' development of critical thinking and problem-solving skills, which are essential for comprehending intricate database structures and SQL logic [29]. It is crucial to strike a balance between AI utilization and conventional learning methodologies to foster independent thinking and problem-solving skills [29].  
However, biases in the training data, particularly those related to newly proposed solutions and curricula, may lead to LLMs reinforcing outdated or biased content, potentially impacting student learning [30]. Data privacy and security are also significant issues, with concerns over the misuse of both training data and student input [30].  
Addressing these concerns is crucial for the successful integration of AI-driven systems in education. The ongoing refinement of AI-driven tutoring systems, particularly in areas beyond programming assistance, is essential to ensure the effective and ethical use of these technologies in educational settings.