Teaching

Generative AI could potentially be used in multiple ways, including automating tasks (increasing productivity) and augmenting the human-in-the-loop (improving quality). Before starting to explore an idea, consider the human alignment and legal aspects (including ethics, safety, potential biases, explainability, robustness, oversight etc.) Also be mindful on plagiarism, data privacy and copyright. For instance, if you use an external service (LLM) to process your prompts and data, it will generally not be acceptable to upload copyright-protected (most) material without prior permission. ...

Generative AI could potentially improve teaching and learning, help to better reach learning objectives or improve the evaluation of your course. Here is a flow you may use to get started considering how to use generative AI in your course: First, list the current learning objectives. For each, describe the desired outcome, as detailed and concretely as possible. Consider how generative AI will influence these outcomes, listing the pros and cons. decide (yes/no) whether AI should be used to support these outcomes, weighing the pros and cons. For areas where generative AI will be used, describe in as much detail as relevant how generative AI can be USED to SUPPORT the outcomes. Consider what needs to happen to make this work Consider how this will impact the evaluation/exam Consider how this will impact the learning objectives Define measureable criteria for successful implementation evaluate and adjust after the course has run For a template and example look at AI in 41031 Industrial Design (.xlsx format). ...

Generative AI will impact the evaluation of many courses. To get started, you may consider: What will be the difference between a student using generative AI and one that doesn’t? Consider to which degree generative AI is (or will be) part of your course in teaching and assignments during the course. Do you allow students to use generative AI in similar ways at the evaluation (exam or project hand-in)? If NOT, how do you then train the students to the evaluation/exam situation? Do you need e.g. to pen and paper assignements also? ...

When considering how the learning objectives of a course or a module are impacted by AI, and what competences students should acquire, consider both: Are the current Learning Objectives impacted by the availability and potential use of Generative AI? What are the foundational skills that need to be mastered by students without the use of Generative AI? What skills may be automated or augmented by Generative AI; in the course as well as after the course? How can this be reflected in the learning objectives (if not already)? How are relevant Generative AI competences taught, and how can those contribute to the (procedural and conceptual) competences of the student? See also the additional points below for inspiration. Generative AI Competences When it comes to Generative AI Competences, upon graduation all students – to varying degrees – are likely to be expected to: ...

“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. ...

This section contains stories and works that can perhaps serve as inspiration for others. Some are thoughts and notes for specific courses, others are notes from e.g workshops or students on using generative AI.