New AI-based technologies like ChatGPT severely limit the effectiveness and significance of traditional didactic elements, such as handing in seminar papers on a specific question or submitting source code as an exercise. If students use AI tools like ChatGPT to generate the answer without or with very limited own work, this poses serious threads to such didactic concepts, as these tools are able to produce solutions in a matter of seconds. In many cases the solution sounds very good, moreover in many cases the solution is correct.

At the time this abstract is being written the authors do not know any reliable method to distinguish between any piece of text written by students and similar AI generated text.

This phenomenon is not limited to computer science students; it can be assumed that they are even more affected by their relevant technical competencies.

ChatGPT is based on an artificial neural network technology, called “transformer networks”. This technology allows not only to take natural language as an input but also gives the answers in several languages in grammatically correct sentences structured in a way similar as sentences written by humans are. The network was trained by using most data available in the entire internet with a cutoff date of September 2021. Therefore, many answers seem to reflect a profound knowledge. Our first assessment of ChatGPT generated answers showed that in many cases these answers follow a specific pattern. This pattern might allow trained teachers to recognize the source. The challenge is, it is very difficult to prove the answers do not come from the student himself, as the answer is generated and not stored in the internet. All standard tools used to check for plagiarism fail utterly to detect the source of the text.

Thus, it is necessary for teachers of computer science courses to develop new didactic concepts that allow a fair and appropriate performance assessment of the students or to integrate the potentials of ChatGPT into the lessons in a meaningful way.

Therefore, some general considerations as well as practical recommendations for teaching are given.

Keywords: AI, Education, Computer Science.