EDULEARN24 AI-Maturity-Matrix Workshop: Materials available

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Last week, we had the pleasure of conducting a workshop at EDULEARN24 (IATED) in Spain. The AI-Maturity-Matrix workshop sparked some very stimulating discussions. Thanks to all participants. Most of you were able to assign yourselves to one of the 10 sections of the matrix and finally found out, “Are we AI-Newbies, AI-Experimenters, or AI-Avant-Garde, or …?”

Today, as promised, the materials from the workshop are available for download and can be used by other moderators and workshops.

You can download the moderator-slides (pptx) and the participant-worksheet (docx) here:

http://ai-workshop.trainings-online.de

The affiliated paper “AI MATURITY MATRIX – A MODEL FOR SELF-ASSESSMENT AND CATEGORIZATION OF AI-INTEGRATION IN ACADEMIC STRUCTURES” can be downloaded on ResearchGate:

https://www.researchgate.net/profile/Stefan-Bieletzke

Two Sigma Challenge

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Dive into the future of higher education with SMARTA (Student Motivation and Reflective Training AI-Assistants). This project introduces AI chatbots as personal study coaches, aiming to bridge the educational gap known as the Two Sigma Problem. With the power of AI, SMARTA offers a trio of specialized chatbots designed to enhance student motivation, deepen engagement, and personalize the learning journey for over 5000 students at a German university. From fostering empathetic support to encouraging self-directed learning and interactive dialogues, these chatbots mark a significant leap towards mimicking the personalized touch of one-on-one tutoring. Discover how SMARTA leverages AI to turn the Two Sigma challenge into an unparalleled opportunity for students alike. Get ready to explore the cutting-edge intersection of technology and education, where personalization meets excellence. Join us on this enlightening journey to redefine the landscape of higher education through the lens of AI.

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

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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: https://trainex22.de/campusmanagement/ki-reifegrad/index_e.cfm

July24: Workshop-Materials are published and can be downloaded here:
http://ai-workshop.trainings-online.de