## 4 Discussion
### AI in Database Instruction  
The integration of AI in database instruction is still in its early stages [14; 23; 26; 28; 29; 30], despite its widespread adoption in programming education [4; 14; 23; 27; 28; 29; 30]. AI-powered tools, such as digital assistants, have demonstrated considerable potential in providing personalized support [3; 14; 23; 27; 28; 30], interactive learning [1; 14; 23; 27; 28; 29; 30], and progress monitoring [5; 14; 30], benefiting both students and educators. However, challenges such as student dependency on AI tools and concerns regarding academic integrity must be carefully addressed [1; 14; 23; 26; 27; 28; 30]. Database education encompasses complex subjects, including data modeling, normalization, and query optimization, which extend beyond basic syntax correction [14; 23; 29]. While AI has been employed for SQL learning through chatbot-based platforms and automated assessment tools, a gap remains in AI-driven solutions designed to visualize database relationships, detect logical errors, and provide adaptive feedback. This underscores a significant research opportunity [14; 23; 27; 28; 29; 30]. Expanding AI's role beyond syntax assistance to include concept-based learning and intelligent tutoring systems has the potential to enhance students' comprehension of abstract database principles. Recent advancements, particularly in LLMs such as ChatGPT, have introduced novel opportunities for integrating AI into DB instruction. These models assist students by generating queries from natural language inputs, improving their understanding of complex database concepts [14; 23; 26; 27; 28; 30]. These models also facilitate flexible grading and independent learning, particularly in online settings. However, pilot studies on AI-enabled microlearning have revealed mixed student feedback [28]. While students have expressed appreciation for personalization and real-time interaction [28; 30], concerns regarding inconsistent responses and inaccuracies have also been raised [23; 26; 27; 29; 30]. These findings un derscore the need for the ongoing refinement of AI tools [23] to ensure their optimal performance and effectiveness in supporting learning. Integrating AI tools into database courses could enhance teaching efficiency by automating tasks such as quiz creation and grading, allowing instructors to focus more on personalized student engagement [23; 26; 28; 29; 30]. Additionally, students have reported that AI-integrated assignments have helped them to develop problem-solving and analytical skills, particularly in database design and programming [23; 27; 28; 29; 14; 23; 30]. According to the results of the systematic review, the primary applications of AI in database education are through three key approaches. One approach involves the use of AI-powered chatbots specifically designed for database instruction. These chatbots enable students to inquire about database concepts and SQL queries, providing immediate feedback and explanations. This form of support helps students navigate complex topics such as relational database structures, normalization, and query optimization, facilitating a more interactive and accessible learning experience. Another significant approach is the use of intelligent self-assessment tools, which provide personalized feedback based on each student's learning progress and individual characteristics. These tools analyze students' database-related responses and offer personalized recommendations, adapting to their unique learning needs. A further method of AI integration involves its incorporation into learning management systems, such as Moodle, where AI-driven tools assist students through interactive question-and-answer sessions. These systems provide real-time hints, explanations, and recommendations, enabling students to engage with the content in a more structured and supportive environment. In addition to the utilization of structured AI-driven platforms, a more flexible approach has emerged.