Speaker
Description
Contemporary education must be student-centered and competence-oriented. The focus mustbe on independently tackling and solving problems in line with constructivist learningtheories. This also requires a transition in teaching activities away from the pure transfer ofknowledge towards supporting students in achieving their learning objectives: the well-knownshift from teaching to learning. This quickly leads to a seemingly insurmountable problem:Lecturers quickly reach the limits of feasibility depending on the number of students. Inaddition, in the spirit of the “Universal Design for Learning”, it is desirable for students to beable to receive support not only at certain times, but whenever they find the time to workindependently on specific topics and problems.
One idea for solving this problem is the use of GPTs, i.e. generative pre-trained transformers.Probably the best-known example of this is chatGPT, a large language model (LLM) thatprovides knowledge in some impressive ways. Based on chatGPT, it is also possible to createyour own GPTs for specific topics. This suggests the idea of using such specifically customizedGPTs to support students. The following scientific questions arise from this idea:
What is chatGPT (in different versions) already capable of in terms of solvingproblems in mathematics and mechanics?
How accurate and reliable are the solution approaches and solutions provided bychatGPT/ GPTs?
Is it possible to additionally instruct GPTs in terms of how and in what way feedbackand answers should be provided? In particular, how can it be ensured that GPTs donot directly “give away” complete solution paths?
In this contribution, we will present a case study that relates to the content of one of ourundergraduate mechanics lectures and specifically addresses the answers to these questions.
