“Ultimately, educators have to figure out how to get students to master basic skills, and also teach them how to use AI responsibility in their work.” – Carl T. Bergstrom and Jevin D. West
Considering the level of engagement with generative AI in DTU courses, multiple strategies may be envisioned:
“No-no” courses that teach students basic personal competences with limited use of AI during the course and no use of AI in exams. This may include e.g., basic math, programming etc.
AI-adapted courses where students are encouraged in learning objectives to use AI, yet students are partially prohibited from using AI in the evaluation. An example of such a course could be 02450 Introduction to machine learning, where the use of tools is tested in project reports, while a final personal assessment is carried out as a multiple-choice exam without AI tools.
AI first courses, i.e., courses that encourage the use of AI in all phases of the course, during learning and in the exams/evaluation.
There is a possibility that the two latter strategies may harvest an AI bonus and engage students at more complex scientific levels than in pre-AI courses. For more on these categories, see e.g., notes from a Workshop on AI in DTU Compute/Cogsys Courses