Abstract:

Artificial Intelligence (AI) could be reshaping the educational domain, suggesting that universities may need to adapt to keep pace or potentially risk becoming outdated. It could be beneficial for an institution to thoughtfully assess its current standing, which might serve as a foundation for gradually charting a course towards an increasingly AI-integrated future.

The AI MATURITY MATRIX presents a comprehensive self-assessment framework that could assist higher education institutions in identifying their current level of AI integration in two distinct areas: one being administrative and educational processes, and the other the integration into teaching modules or the curriculum.

Thus the structure of the framework is defined along two principal axes: the application of AI in university operations, and on the other hand, its integration into the curricula. These axes are systematically broken down into three maturity levels: low, medium, and high. This division results in nine distinct categories, enabling institutions to categorize themselves as, for example, "AI Teaching Masters", "AI Process Experts", or members of the "AI-Avant-Garde". Additionally, the model offers the possibility to consider qualitative assessments through a traffic light color-coding system, allowing for a visual representation of the effectiveness and quality of AI integration.

The framework utilizes a questionnaire to help institutions categorize their level of AI integration. This questionnaire was refined through empirical testing. A web-based interactive quick test consisting of 10 questions with 40 responses offers a preliminary classification of the institution, providing immediate insights into their AI maturity stage. Based on the initial results, a deeper analysis is necessary or advisable, enabling institutions to more precisely explore their AI capabilities and areas for improvement.

To illustrate the framework's application, consider the 'Teaching Experimentator' category. Universities classified under this category are at the early stages of AI adoption, often experimenting with AI in limited, isolated scenarios, such as specific teaching tasks or learning content. While they may recognize AI's potential, they have not fully integrated AI strategies into their teaching methodologies, and of course, AI remains notably absent from their institutional frameworks. The matrix not only guides such universities in identifying their strengths but also has the potential to open their eyes, encouraging advancement toward more sophisticated stages of AI maturity.

Additionally, this model can be applied to create an international AI index or survey, which, based on the established categories, allows for the comparison of the maturity levels of universities across different countries. This facilitates a global perspective on how institutions are integrating AI into their operations and curricula.

In summary, the AI MATURITY MATRIX could be seen as a valuable strategic tool that may have the potential to influence the direction of universities as they navigate through the digital evolution of education. It can serve as a useful instrument for institutions looking to thoughtfully assess and categorize their use of AI, enabling them to not just participate in, but also contribute to shaping the landscape of modern education.

Weblink of the interactive questionnaire: http://AI-Matrix.KI-Campus.eu

Keywords:

AI, Artificial Intelligence Integration, Educational Technology Assessment, AI Maturity Framework, University Digital Transformation, Higher Education Innovation, Academic AI Implementation, Strategic Educational Planning, AI in University Administration.