@article{FroschLindauer2024, author = {Frosch, Katharina and Lindauer, Friederike}, title = {AI-based Automated Production of Learning Content - A Means to Bridging the Digital Divide in Workplace Learning?}, series = {International Journal On Advances in Systems and Measurements}, volume = {17}, journal = {International Journal On Advances in Systems and Measurements}, number = {3\&4}, publisher = {IARIA}, pages = {156 -- 165}, year = {2024}, language = {de} } @inproceedings{FroschLindauerZuidhof2024, author = {Frosch, Katharina and Lindauer, Friederike and Zuidhof, Niek}, title = {Exploring the Digital Divide in Workplace Learning: A Rapid Review}, series = {ICDS 2024, The Eighteenth International Conference on Digital Society, Barcelona, Spain}, booktitle = {ICDS 2024, The Eighteenth International Conference on Digital Society, Barcelona, Spain}, publisher = {IARIA}, pages = {1 -- 6}, year = {2024}, abstract = {This article examines the digital learning divide in workplace learning, with an emphasis on the disparity in the distribution of advanced learning technologies (ALT) across different types of workplaces. The study employs a rapid literature review methodology to analyze the utilization of ALT in workplace learning. The findings indicate that the use of ALT is predominantly concentrated in the education, health and medical sectors, with limited implementation in other sectors. Moreover, in smaller organizations, in non-technical sectors and among white-collar workers, there are fewer opportunities for technology-enhanced learning. The study highlights the need for more inclusive and comprehensive research to address the digital divide in workplace learning, taking into consideration practice-based evidence and exploring the themes covered by training. Furthermore, the paper proposes an investigation into the complexity and resource intensity of implementing ALT to enhance technology-based learning in all workplaces. In general, this research establishes the basis for comprehending and bridging the digital learning divide in the workplace.}, language = {en} } @inproceedings{FroschLindauerWinkel2024, author = {Frosch, Katharina and Lindauer, Friederike and Winkel, Carmen}, title = {Utilizing Chatbots for Automating Examinations in Higher Education: Perceived Fairness, Trust and Learning Outcomes}, series = {EDEN 2024 Annual Conference, University of Graz, Graz, Austria, 2024}, booktitle = {EDEN 2024 Annual Conference, University of Graz, Graz, Austria, 2024}, pages = {21 -- 22}, year = {2024}, abstract = {Large Language Models (LLM) are increasingly used to support educational assessment (Gonz{\´a}lez-Calatayud et al.,2021), offering a promising approach to addressing challenges associated with scalability, consistency, andpersonalized feedback that manual assessment implies (Fagbohun et al., 2024).Pre-configured interfaces for building custom chatbots based on common AI models provide a low-level approachfor educators to design their own AI-based examination tools that ask learners questions and provide feedback onthe answers. An example of a prototypical application - albeit based on a self-programmed interface - is Nitze's(2024) StudyBuddy, which simulates oral exams. Yet, the extent to which these AI-based exam assistants canautonomously conduct complete exam assessments—creating questions and evaluating and grading responseswithout manual oversight—is still largely unexplored. Despite the clear advantages, issues related to the quality ofthe generated content, the precision of assessments, ethical implications, and acceptance among learners persist assignificant concerns.This paper is a proof-of-concept study combined with a field experiment for an AI-based exam assistant based onthe StudyBuddy suggested by Nitze (2024). The exam assistant provides students with a set of quiz questions andcase studies based on an 80-page reader on topics in organizational behavior, offering personalized feedback andquestion-by-question scoring ranging from 0 to 100\%, along with an overall grade and personalized feedback. Toevaluate learning behavior and outcome (in the practice phase), perceived fairness (in the exam phase) and overalllearner acceptance, we run a field experiment with N=35 students enrolled in a business management program at atechnical university in Germany. They take part in a quiz assignment provided and graded by the exam assistant andsubsequently answer a survey with questions based on the Perceived Fairness scale (Sonnleitner \& Kovacs, 2020)and the Trust in Automated Systems Test (Wojton et al., 2020). Additionally, the learning outcomes are assessed ina retrospective pre-post design according to Drennan and Hyde (2008). Results will be available by the End of April2024.Insights gained from this study will shed light on the question whether custom chatbots can be effectively used forscalable, efficient assessment processes in higher education. As the ready-made interfaces already available requireminimal prerequisites and technical skills, they present an accessible opportunity for instructors to develop theirown AI-based examination tools, and to ease manual corrections, to guarantee consistent grading and to providestudents with individualized, constructive feedback on their solutions even in large courses where personalizedfeedback would not be possible when manual grading is used.The results of this study will shed light on whether customised chatbots can be effectively used for scalable, efficientassessment processes in higher education. The readily available interfaces, requiring minimal prerequisites andHow do we promote fairness in digital learning futures?22technical skills, enable educators to develop their own AI-based assessment tools. This would open opportunitiesfor educators to build their own AI-based assessment tools and facilitate manual corrections, ensure consistentgrading and provide students with individualised, constructive feedback, even in large courses where personalisedfeedback would not be possible with manual grading.}, language = {en} } @techreport{FroschDamusHaberlandetal.2025, author = {Frosch, Katharina and Damus, Martha and Haberland, Steffi and H{\"a}dicke, Svenja and Lindauer, Friederike and Winkel, Carmen}, title = {Lehren mit KI: Talk2Transform (T2T)}, series = {Wie KI Studium und Lehre ver{\"a}ndert Anwendungsfelder, Use-Cases und Gelingensbedingungen}, journal = {Wie KI Studium und Lehre ver{\"a}ndert Anwendungsfelder, Use-Cases und Gelingensbedingungen}, publisher = {Hochschulforum Digitalisierung}, pages = {23 -- 25}, year = {2025}, language = {de} } @article{FroschLindauer2025, author = {Frosch, Katharina and Lindauer, Friederike}, title = {Learning With Short Bursts: How Effectively Can We Build Competencies in Climate Change-Related Areas Based on Microlearning?}, series = {European Journal of Education}, volume = {60}, journal = {European Journal of Education}, number = {2}, publisher = {Wiley}, doi = {10.1111/ejed.70088}, pages = {1 -- 17}, year = {2025}, abstract = {The study examines the effectiveness of microlearning in developing the capacity to address climate change and adapt to environmental challenges. Conducted as an online field experiment with 140 participants, the study focused on the impacts of smartphone use as an illustrative learning domain for education in climate change-related areas. Using a pre-post research design and simultaneous equation models, the study found improvements in knowledge retention, with a median increase of 38\%. The results indicated that the brief microlearning units also promoted sustainability action competencies, such as confidence in one's influence and willingness to act sustainably. This suggests that higher-order learning processes were also triggered, although to a much lesser extent. Furthermore, learner satisfaction was identified as a mediating variable for these positive outcomes. The study concludes that short bursts of knowledge delivered through microlearning activities can be used alongside traditional training methods to build the critical skills needed for initiatives such as the EU Green Deal.}, language = {en} } @inproceedings{FroschLindauerWinkel2025, author = {Frosch, Katharina and Lindauer, Friederike and Winkel, Carmen}, title = {Is Learning with an AI-Powered Chatbot for Everyone? A First Look at How Learning Preferences May Influence Learning Outcomes}, series = {Ubiquity Proceedings}, volume = {6}, booktitle = {Ubiquity Proceedings}, number = {1}, publisher = {ubiquity press}, doi = {10.5334/uproc.207}, pages = {1 -- 10}, year = {2025}, abstract = {In an era where AI-powered chatbots are increasingly being integrated into education and corporate learning, it is critical to determine whether these approaches benefit all learners or primarily cater to those with specific preferences. This study explores the interplay between learning preferences and learning outcomes in communication training using an AI-powered chatbot. In a field experiment with 17 participants, systematic thinkers and intrinsically motivated learners reported higher satisfaction and greater skill improvement, while those who preferred model learning and direct feedback benefited less. These findings suggest that AI-powered chatbots should be carefully designed to accommodate diverse learners and mitigate potential negative effects.}, language = {en} } @inproceedings{LindauerDamusWinkeletal.2025, author = {Lindauer, Friederike and Damus, Martha and Winkel, Carmen and Frosch, Katharina}, title = {AI-Driven Communication Training for Cybersecurity with the Talk to Transform Simulator}, series = {KI-Forum 2025 : KI in Forschung und Lehre an Hochschulen}, booktitle = {KI-Forum 2025 : KI in Forschung und Lehre an Hochschulen}, publisher = {HsH Applied Academics}, doi = {10.25968/opus-3790}, pages = {8}, year = {2025}, abstract = {Effective communication skills are increasingly recognized as critical for leadership in digital transformation contexts. Recently, AI-Chatbots such as Talk to Transform (T2T) have been developed to enhance leadership competencies through interactive role-plays and feedback. This paper proposes their adaptation for cybersecurity training. We discuss the current landscape of cybersecurity training, highlight the importance of communication, and present T2T as an innovative approach to bridge this gap through chatbot-driven role-plays.}, language = {en} }