AI Teaching Master, Process Expert, or Excellent Avant-garde?

Where does your university stand in the field of AI? The AI Maturity Matrix can assist with self-assessment.

Do most professors see AI more as a danger than an opportunity? Does the examination office ban the use of GPT? This suggests a traditional and careful approach.

Or has your university already included AI as an optional module in every degree program, training teachers to integrate it into their subjects in a practical way? Are the people in charge of accreditation informed about including AI in new module handbooks? If so, your university might be on its way to becoming an AI teaching leader.

Does your administration, marketing, and admissions team, as well as your research department, make extensive use of AI? Do you give teachers the chance to use generative AI to create case studies or tasks? Do students also have access to GPT in a way that protects their privacy? If yes, then you’re likely on the path to becoming an AI process expert.

Or are you working on AI innovations both in your curriculum and in the processes of administration and learning? Congratulations. You’re on your way to being a part of the excellent AI-Avant-garde!

To answer the question, “Where does the university stand in the field of AI?”, we are currently developing a classification model including a questionnaire. This tool aims to enable a university to conduct a self-assessment.

To compare this with an external evaluation, clear definitions and measurement criteria must be developed for each axis and category. Integrating a third dimension will also help to capture the qualitative aspects of AI integration. For example, traffic light colors can be used to indicate whether the integration is successful and comprehensive.

Reading example: The university depicted in Quadrant 9 has integrated AI into its curriculum MODERATELY, meaning only in specific modules. However, these specific modules are rated as rather poor quality (red), for example, because the modules are not current or not relevant to practice. The university has HIGHLY integrated AI into its processes, meaning in both administration and teaching, and this has been achieved with satisfactory quality (yellow) so far.

As part of this project, a national AI index of universities in Germany is being created, spanning across various institutions. The goal is to also provide interested universities with a guide on how to progress from one stage to another, for example, from being an “AI Teaching Experimentator” to an “AI Teaching Master”. Initial ideas are promising.

The quick test can be found here:

Singularity – General Artificial Intelligence

Berlin, 2020. Alongside an interview with Howard Rheingold, we explored the main theme ‘The End of Utopia?’. This included a discussion on ‘Singularity: Point-of-no-Return to Utopia or Dystopia?’. Here’s a summary:

Today’s Artificial Intelligence (AI) is a technically limited assistant, often not much smarter than a person with natural stupidity. However, through machine learning and exponentially accelerated progress, these weak assistants will soon become expert geniuses. The idea of singularity is that there will be a moment when machines start improving themselves forcefully, leading from expert geniuses to a universal machine genius. It’s the point where humans recognize this universal AI as having strong intelligence, growing exponentially and quickly surpassing human intelligence by a wide margin. It’s the turning point where there’s no going back to a world without this AI. If this universal AI is given decision-making and action-taking abilities, society will change into either a positively utopian or negatively dystopian society, depending on the moral stance the AI adopts. […]

The full text (in german) can be found here:
Singularit├Ąt: Point-of-no-Return zur Utopie oder Dystopie? (