# ChatGPT in Computer Science Education: A Case Study on a Database Administration Course
## 5 Conclusions  
This article has presented an empirical study to explore the usage of ChatGPT to learn about database administration in computer science education. We found that ChatGPT was moderately used, but it was more used than traditional internet learning resources, and a trend of increasing use could be observed compared to the previous year. We also found uneven usage by students and that the students who end up earning better grades were the ones who used it more and had better perceptions about ChatGPT. In fact, positive correlations were found between the use of this tool and the success of solving database administration problems during a practical exam. Lastly, we found that one of the elements that could encourage students to use this tool intensively and productively was the theoretical background they had about database administration. Therefore, it can be concluded that ChatGPT can be an excellent educational instrument in the context of database administration and that to use it properly, it is necessary for students to have a solid theoretical basis. However, as mentioned in the Introduction, it should be noted that GenAI tools such as ChatGPT have a number of limitations to consider: the possibility of hallucinating, the lack of critical thinking that could be generated in the GenAI users, and even prohibitions on the use of these tools in the process of software development by contractors. In addition, during the execution of this case study, we have found that prompting skills are also important, and teachers can use a guided practice strategy, where prompting and guided step-by-step practice are employed to train these skills as well as to increase the student's theoretical background. Training students in the use of GenAI tools like ChatGPT is key to prevent the appearance of new digital trenches between students with solid knowledge and prompting skills that can further increase their performance through the proper use of GenAI tools and students with a lower degree of the fundamentals and worse prompting skills, who lag behind and will obtain even worse results. The presented conclusions are strongly connected to computer science education since they are based on a case study in that context, but we deem that they are fully transferable to any engineering or science discipline in which the practical mastery of technologies and problem solving play a key role. Lastly, some limitations and future work are presented. First, the sample size is only 40 students, so the conclusions reported here should be taken with caution. Furthermore, there are many difficulties associated with the generalization of single case studies like the presented [49], so more studies like this should be conducted to consolidate our findings. Once this occurs and the presented framework has been applied several times, we could provide a more reliable and generalizable mathematical model. Second, the gender distribution is unbalanced, which is common in computer science studies, and unfortunately we do not have a way to solve this limitation in the study. Third, the study design precludes the assertion that ChatGPT caused higher grades. The conduction of a randomized controlled trial in which there was an experimental group with access to ChatGPT and a control group without access to ChatGPT would allow one to isolate the ChatGPT variable and assert cause-and-effect relationships. However, although this is very desirable from a research point of view, from a teaching perspective it is complicated, as it violates the principle of student equity. Fourth, the conclusions reported are based on various data, including those obtained through the questionnaire. This instrument provides self-reported data that can influence responses due to social desirability bias and inaccurate self-reporting. In detail, the prompting skills of the students are measured using this self-perception questionnaire, rather than more objective tests. In the future, the prompting skills of the students could be measured by analyzing their prompts.