# ChatGPT in Computer Science Education: A Case Study on a Database Administration Course
## 1 Introduction
The European School of Management and Business (EUDE) developed EDU, a virtual co-tutor powered by Generative AI, using IBM's suite of AI solutions including Watson Assistant and Watson Discovery. This system supports administrative, academic, and logistic queries in real-time, which could be applied to database administration courses. Loyola University Chicago implemented LUie, an AI-powered digital assistant built on the Oracle Digital Assistant platform. Although not specifically for database administration, this system demonstrates how AI can be integrated into university systems to provide continuous student support and personalized information. Focusing on data management, it has been reported that the GenAI scope can fill the gap when automation fails to understand the semantics of the data. Thus, difficult database problems can take a new approach, boosting data discovery, query synthesis, schema matching, and the like [33]. By their nature, these GenAI tools are predictive models; therefore, they can perform well when it comes to combining them with information retrieval. MIT researchers found that SQL does not allow one to incorporate probabilistic AI models, and, at the same time, solutions with probabilistic models making inferences do not support complex database queries. To fill this gap, they created GenSQL [34], a Generative AI system for databases that allows users to perform complex statistical analyses on tabular data without extensive technical knowledge. This tool could be particularly useful in teaching database administration by allowing students to make predictions, detect anomalies, guess missing values, fix errors, or generate synthetic data. Ref. [35] explored the use of ChatGPT as an assistant to automatically correct SQL exercises in a university database course. ChatGPT effectively identifies syntactic and semantic errors, provides explanations, and assigns grades with a strong correlation to human teacher ratings, although, interestingly, it tends to be stricter. Its accuracy decreases with more complex exercises due to subjectivity in error assessment. Although ChatGPT excels at detecting syntax errors (88.9%), it often confuses them with semantic ones, and its suggestions are only valuable in about half of the cases. Comparisons of Few-Shot and Zero-Shot Learning reveal that the latter produces clearer explanations, while the former relies heavily on provided examples. The study concludes that ChatGPT shows potential as a teaching assistant, but further research is needed to refine its application in education. In ref. [36], a chatbot is used to support students in completing SQL queries in a step-by-step process. Students who used it performed better on the final exam (4 out of 10 passed the exam, a number halved among students who did not use the tool), while teachers received insightful performance metrics on student progress, aiding them in instructional decisions. [PERSON] et al. research also highlighted the critical role of prompt engineering in obtaining successful results with GenAI for SQL practice [37], highlighting that students benefited significantly from teacher guidance in developing effective prompts. Together, these studies underline Generative Als potential as an SQL learning aid, while also highlighting the importance of instructor support for effective prompt formulation and student guide for GenAI usage. They also show the need for further research on the utility of these tools in database administration courses, as there are still some open questions about (i) the combined use of traditional and new tools, (ii) the ability of these tools to solve complex problems at the university level, and (iii) the possible improvement in learning database administration using them. In consideration of the aforementioned findings and previous efforts, we have undertaken an empirical study which addresses the following research questions (RQs):  
* RQ1.