Abstract:

The release of the language-based AI application ChatGPT in November 2022 attracted international attention and led to a nuanced scientific debate on the opportunities, challenges and implications of generative AI for research, practice and policy (Dwivedi et al. 2023). The 'big language models' were also found to have both benefits and risks for the dimensions of teaching and learning when used in differentiated educational contexts (Kasneci et al. 2023). In the context of higher education, the changes brought about by technological developments have led to considerable uncertainty from the perspective of both teachers and students. In addition to (examination) legal issues, the objectivity, reliability and validity of the information generated by AI is also viewed critically (Rademacher 2023). Like the general debate on the potential uses of AI technologies, the debate on AI at universities is largely characterised by the weighing up of the opportunities and risks of such technologies in areas of application such as governance, administration, research and teaching. These issues relate to the support of decision-making processes as well as the promotion of innovation and the personalisation of learning processes (Wannemacher/Bodmann 2021).

Given the growing number of students worldwide, concepts that use AI applications to provide as many students as possible with fast, personalised advice without a significant loss in quality compared to human advice are gaining in importance.

With 130,000 students, the IU International University of Applied Sciences is the largest university in Germany and one of the largest and fastest growing universities in Europe. The distance learning sector in particular is growing rapidly across Europe.

'Syntea' is an AI learning buddy developed by the IU, which is a personalised AI learning assistant designed to recognise the individual needs and preferences of learners. Syntea adapts to the learning pace and rhythm of students, tracks their learning progress and helps them to identify and close gaps in their knowledge (Möller et al., 2024).

The presentation will focus on how personalised, largely self-referential AI learning systems can enable transformative education. In the biography-inspired theory of transformational education, education is not understood as a "linear process of subsumption of knowledge elements", but as a "transformation of meaning structures" (Koller 2012, p. 15), which can be triggered by "irritation" and "strangeness" alone.

Learners use AI to learn autodidactically. By using pedagogical arrangements such as Syntea, all of the learner's activities can potentially be digitally recorded, which can then be networked with other content appropriate to the learning level as a starting point for new learning incentives. This focus on the learner and their integration into a ubiquitous, real-time and opaque data structure could pose a problem in terms of educational theory.

The question then arises as to how an alien, irritating element, which is seen as necessary for education, can arise at all in the data left behind by the learner and the learning opportunities generated from it, which are centred on the learner. The alien can only lead to irritation, and thus at best to education, if it appears as something incomprehensible (Waldenfels 1997) and is thus decoupled from the learner or from a digital data doublet.

Keywords:

AI, distance-learning, chatbots in Higher Education, AI-supported teaching, transformative education.