Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Best-of-Breed: Service-Oriented Integration of Artificial Intelligence in Interoperable Educational Ecosystems
    ( 2023)
    Krauß, Christopher orcid-logo
    ;
    Streicher, Alexander
    ;
    Poxleitner, Eva
    ;
    Altun, Daniela
    ;
    Müller, Joanna
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    An, Truong-Sinh
    ;
    Müller, Christoph
    Artificial Intelligence (AI) offers great potential for optimizing learning processes, teaching methods, learning content, or organizational procedures. However, the success of AI components in educational environments is by no means guaranteed and depends on several conditions in their respective learning settings. In this article, we analyze requirements that are often addressed prior to introducing AI features. We address organizational, methodological, didactical, content-related, and technical challenges. The research question of this work is how AI features can best be incorporated into modern educational system landscapes to create sustainable system architectures that are accepted and perceived as added value by users. Thereby, the article discusses two approaches to software architecture: Best-of-Suite (for monolithic architectures) and Best-of-Breed (for service-oriented architectures). Monolithic systems offer a wide range of functions, can be offered by a single provider but can become difficult to manage and create dependencies. Specialized and service-oriented systems, in turn, consist of modular functions handled by specialized services, are more flexible and scalable, and can be integrated with a wide range of tools and services, but require more effort to set up and manage. We explain why the Best-of-Breed strategy is a sensible approach to the use of AI components, how this can be implemented sustainably with the help of a middleware component, and we report on the user experiences from a field test. While in this work we evaluate the implemented system with a cybersecurity training as an on-the-job course, the middleware has been successfully used in other educational contexts, as well.
  • Publication
    Lessons learned from creating, implementing and evaluating assisted e-learning incorporating adaptivity, recommendations and learning analytics
    ( 2022)
    Altun, Daniela
    ;
    Krauß, Christopher orcid-logo
    ;
    Streicher, Alexander
    ;
    Müller, Christoph
    ;
    Atorf, Daniel
    ;
    Rerhaye, Lisa
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    Kunde, Dietmar
    Applications of adaptive e-learning, recommender systems and learning analytics are typically presented individually, however, their combination poses several challenging requirements ranging from organizational to technical issues. This article presents a technical study from a holistic application of a variety of e-learning assistance technologies, including recommender systems, chatbots, adaptivity, and learning analytics. At its core we operationalize interoperability standards such as the Experience API (xAPI) and Learning Tools Interoperability (LTI), and control the data flow via a standard-encapsulating middleware approach. We report on the challenges regarding organization, methodology, content, didactics, and technology. A systematic evaluation with the target group discusses the users’ expectations with the measured interactions.