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The integration of artificial intelligence (AI) into computer science (CS) education is evolving, yet its specific application in database instruction remains underexplored. This systematic review analyzes 31 empirical studies published between 2020 and 2025, examining how AI applications support teaching and learning in CS, with an emphasis on database education. Following the PRISMA methodology, the review categorizes AI applications according to instructional design models, roles, actions, benefits, and challenges. Findings indicate that AI tools, particularly chatbots, intelligent tutoring systems, and code generators, effectively support personalized instruction, immediate feedback, and interactive problem-solving across CS and database-specific contexts. However, challenges persist, including AI inaccuracies, biases, student dependency in AI, and academic integrity risks. The review also identifies a shift in programming education as AI reshapes software development practices, prompting a need to align curricula with evolving industry expectations. Despite growing attention to AI applications in programming education, database-related research remains limited. This review highlights the necessity for further empirical investigations specifically in database instruction, including more extensive studies addressing AI-driven pedagogical strategies and their long-term impacts. The results suggest that careful integration of AI tools can complement traditional instruction, emphasizing the critical role of human educators in achieving meaningful and effective learning outcomes.
Gaitantzi et al. (Thu,) studied this question.