Assistance for Teaching

In the preparation of courses, generative AI offers numerous application possibilities to facilitate the work, improve the didactic preparation and also save time. Here are some concrete ideas for prompting:

• Formulation of learning objectives
  "Formulate three concrete learning objectives for first-semester Bachelor's students in the Department of Physics in the ¡®Students are able to ...¡¯ formulation for the following course content: ....."
• Drafting a rubric
  "Design a rubric with five evaluation criteria. The criteria are rated as exceeded, fulfilled and not fulfilled. Each assessment value should be formulated in detail. The rubric is used for a semester course on the topic ... and the project work is to be assessed. The content objectives of the project work are ...."
• Planning for workshops
  "Create a schedule for a project-based workshop lasting three hours for Master's students. This involves working on a specific project. The theoretical content has already been taught and is available online. The following content objectives are to be achieved: ..."
• Formulation of assignments
  "Summarise the following content in the three most important key statements. Then formulate an assignment for 30 minutes of group work on these key messages. The target group is fourth-semester students with a health science background. The results should be presented creatively; give examples. Content: ..."
• Creation of questions
  "Design two questions that would stimulate a discussion on the following topic..." or "Create three clicker questions with four possible answers each on the following content: ..."

During the course, the use of generative AI can be encouraged, discussed and critically scrutinised. It is important to seek discussion with the students and address the topic directly.

Possible concrete application scenarios for the students could be, for example:

• "Create a list of five pros and five cons for your idea using generative AI."
• "Check your arguments with the help of generative AI by having them critically scrutinised."
• "Generate three additional practice exercises on the content discussed."
  
• "Have a summary written and discuss the result to see if the most important points have been captured correctly."

In the existence of generative AI tools, it is also important to review and reconsider the validity of the performance assessments used. The principle that students are always responsible for their submitted work remains central and it must therefore always be clarified how AI tools are to be used and how they are correctly referenced (further information on this can be found under Academic Integrity).

Preparation:

• Have the tasks been checked for their validity in view of the general availability of generative AI?
• What kind of performance assessments promote skills development and preparation for AI-driven jobs of the future?

Realisation:

• Have the framework conditions for students been clearly defined as to how and which AI tools may be used?
• Is equal access guaranteed when using AI tools?

Post-processing:

• How can generative AI be used gainfully for lecturers and students in the follow-up process?
• Is the use of AI tools always transparent?

• external pageLarge Language Models und ihre Potenziale im Bildungssystemcall_made, a paper by educational researchers.
• external pageHey ChatGPT, kannst du mir helfencall_made, opportunities and risks of using ChatGPT & Co. during studies from a student perspective.
• external pageEDUCAUSE Horizon Report with honorory trend: Artifical Intelligencecall_made, Teaching & Learning Edition 2024.

• external pageVision einer neuen Pr¨¹fungskulturcall_made, a discussion paper by the Hochschulforum Digitalisierung.
• external pageAkademische Integrit?t und eine neue Bewertungskultur im Zeitalter von KIcall_made
• external pageNutzung von ChatGPT in technischen Pr¨¹fungencall_made, a pilot study by the Fachhoschule Nordwestschweiz.