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Assistance for Teaching

The possible applications of generative AI are diverse and range from summaries, translations and idea creation to concrete content generation and critical scrutiny. The focus here is on how these tools can be used gainfully in teaching, how the application should be documented and what possible changes result for teaching.

Contents

The diversity of application scenarios, the available tools, the integration into existing applications and the access options for generative AI are currently in a constant state of flux. It is therefore essential to communicate clearly to students how generative AI is used by you, should be used by them, and under what circumstances there are restrictions. It is also necessary to indicate when additional transparency is required.

Applications

Generative AI offers great potential to support teaching and learning, prepare content didactically, scrutinise it critically and save time if used correctly. The Innovedum project Assessing the Potential of AI for Scientific Writing Techniques developed a comprehensive guide with many practical examples to help lecturers enrich their courses with GenAI:

The following is a small collection of concrete application possibilities to inspire your own creativity. When using generative AI, it is important to always ensure that academic integrity is maintained by adhering to the principles of responsibility, transparency and fairness.

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."

The subpage Performance assessments examines in detail how examination formats and assessment procedures are changing through the use of generative AI. It offers concrete approaches and examples for the contemporary design of examinations and assessments in the context of digital technologies.

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?

The article external page Augmented Course Design: Using AI to Boost Efficiency and Expand Capacity contains some further ideas on the integration of GenAI into teaching and learning. Also the guide external page Handlungsempfehlungen für den didaktischen Einsatz von generativer KI in der Hochschullehre shares specific information on the use of AI-based applications in teaching at tertiary level.

GenAI-Ready Assignments

GenAI-Ready Assignements workflow

The widespread availability of AI tools is fundamentally changing the culture of learning. Many carefully designed exercise formats and tasks no longer achieve the desired results because they were developed before the advent of generative AI.

The Download GenAI-Ready Assignments Blueprint (PDF, 544 KB) provides ideas on how existing exercises can be adapted to continue promoting learning and, in particular, critical thinking. The focus is on four key aspects:

  • Time over Task
  • Variation over Routine
  • Reflection over Completion
  • Collaboration over Competition

The focus is on promoting intrinsic motivation and engagement by aligning learning tasks with basic psychological needs in accordance with Self-Determination Theory (according to Ryan & Deci).

AI Competence Framework for Lecturers

The AI competence framework for lecturers serves as a guide for developing and promoting competencies in the field of artificial intelligence (AI). It helps lecturers develop a deep understanding of the diverse applications of AI and use it in a targeted manner to improve teaching and learning processes.

It also promotes critical engagement with the potential and challenges of AI and encourages the continuous development and application of innovative AI-supported methods and tools in teaching. It is designed to enable lecturers to guide students toward the reflective, competent, and responsible use of AI—in particular to promote active, self-directed learning.

This competence framework was developed as part of the “Open Education & Digital Competencies” program by swissuniversities in collaboration with the University of Zurich and ETH Zurich.

Potential for change

There are already a variety of application scenarios for generative AI in teaching. However, this technology also harbours the potential to fundamentally change the entire education system. Students need to be prepared for the AI-supported workplaces of the future, and this will not only change the skills to be learnt, but also the way in which teaching and learning take place.

GenAI offers opportunities, such as the promotion of personalised learning or the automation of complex processes. However, we must not lose sight of risks such as dependence on AI tools or one-sided advantages for certain stakeholder groups.

The subpage Performance assessments examines in detail how examination formats and assessment procedures are changing through the use of generative AI. It offers concrete approaches and examples for the contemporary design of examinations and assessments in the context of digital technologies.

The ubiquitous availability of AI tools prompts us to reflect on the examination culture applied. The question arises as to how students' competences can be assessed, measured or documented in the age of GenAI.

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