Abstract:

Education is undergoing a major transformation as a result of digitalization. Learning and teaching are becoming more modular as well as flexible and are no longer tied to physical spaces. At the same time, tailored learning paths for different goals can be offered to learners. With digitalization and the growing importance of location-independent study formats, microcredentials have become increasingly important. Their compact structure and focus on targeted skill acquisition make micromodules particularly well suited to the requirements of lifelong learning. For lecturers, the development of high-quality micromodules is associated with a large initial additional effort, as well as technical hurdles, and the willingness to familiarize themselves with new tools or processes. These barriers can prevent lecturers from committing to the concept of micromodules and using them in their teaching.

This paper examines how artificial intelligence (AI) can help minimize the workload involved in developing and delivering micromodules and shows a concept for the design and use of an AI agent. Rather than focusing on content generation, this paper analyzes a concept in which AI and humans cooperate through guided interaction to analyze and prepare course materials and deliver them as micromodules. The underlying idea is that AI does not replace the didactic expertise of lecturers but supports it by analyzing the structure of the materials, suggesting coherent module structures, formulating or refining learning objectives, identifying suitable interactive elements, and generating initial versions of the micromodule, which conform to a platform-independent standard. This AI system incorporates didactic principles and modularization guidelines, providing lecturers with a tool to develop a suitable micromodule for their purposes.

Based on the concept analysis and practical experience gained from previous micromodule development, the paper argues that an AI-supported workflow can reduce the workload, simplify technical challenges involved in developing micromodules, and help maintain consistent quality and design across different modules. The analysis indicates that such a system can lower entry barriers, support more sustainable development, and improve the scalability of micromodule implementation.

Through this conceptual approach, the paper offers a perspective on how universities can meaningfully integrate AI into their learning ecosystems. It demonstrates how intelligent systems can help overcome practical challenges and thus enable the efficient development of micromodules, on which higher education is increasingly dependent.

Keywords:

Microcredentials, AI, Life-long learning, e-learning, Digital transformation.