Now showing 1 - 10 of 29
  • Publication
    Insert-expansions for Tool-enabled Conversational Agents
    This paper delves into an advanced implementation of Chain-of-Thought-Prompting in Large Lan- guage Models, focusing on the use of tools (or "plug-ins") within the explicit reasoning paths generated by this prompting method. We find that tool-enabled conversational agents often become sidetracked, as additional context from tools like search engines or calculators diverts from original user intents. To address this, we explore a concept wherein the user becomes the tool, providing necessary details and refining their requests. Through Conversation Analysis, we characterize this interaction as insert-expansion — an intermediary conversation designed to facilitate the preferred response. We explore possibilities arising from this ’user-as-a-tool’ approach in two empirical studies using direct comparison, and find benefits in the recommendation domain.
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    How to Support Students’ Self-Regulated Learning in Times of Crisis: An Embedded Technology-Based Intervention in Blended Learning Pedagogies
    With the increasing prevalence of technology-enhanced learning environments, self-regulated learning (SRL) has become a crucial skill for management students and graduates in the 21st century. Self-regulated learners can take control of their own learning process by setting learning objectives and selecting appropriate learning strategies. As a result of the recent COVID-19 crisis, universities were compelled to shift to online course delivery, which greatly reduced social interaction between educators and learners and challenged educators’ feedback practices. To address this issue, we developed and embedded a technology-based intervention with temporal-proximate and regular formative feedback assessments in a large-scale management course to promote graduate students’ SRL practices. We evaluated the intervention in a quasi-experiment, which found that students with the embedded SRL intervention had higher self-assessment and learning outcome scores and lower absolute self-assessment deviation. Our study makes at least three contributions. First, we shed light on students’ SRL strategies in times of emergency remote learning, highlighting their extensive need for social support and comparison. Second, we extend the literature on SRL and social-cognitive theory by unveiling a hidden effect when embedding temporal-proximate and regular interventions. Third, we contribute an empirically evaluated intervention to foster students’ SRL in blended learning and online pedagogies.
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    Scopus© Citations 2
  • Publication
    Quantum computing
    ( 2022-08-05) ; ;
    Bosch, Samuel
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    Steinacker, Léa
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    Quantum computing promises to be the next disruptive technology, with numerous possible applications and implications for organizations and markets. Quantum computers exploit principles of quantum mechanics, such as superposition and entanglement, to represent data and perform operations on them. Both of these principles enable quantum computers to solve very specific, complex problems significantly faster than standard computers. Against this backdrop, this fundamental gives a brief overview of the three layers of a quantum computer: hardware, system software, and application layer. Furthermore, we introduce potential application areas of quantum computing and possible research directions for the field of information systems.
    Scopus© Citations 33
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  • Publication
    Towards Designing an Adaptive Argumentation Learning Tool
    (Proceedings of the International Conference on Information Systems (ICIS) 2019, 2019-12) ;
    Digitalization triggers a shift in the compositions of skills and knowledge needed for students in their future work life. Hence, higher order thinking skills are becoming more important to solve future challenges. One subclass of these skills, which contributes significantly to communication, collaboration and problem-solving, is the skill of how to argue in a structured, reflective and well-formed way. However, educational organizations face difficulties in providing the boundary conditions necessary to develop this skill, due to increasing student numbers paired with financial constraints. In this short paper, we present the first steps of our design science research project on how to design an adaptive IT-tool that helps students develop their argumentation skill through formative feedback in large-scale lectures. Based on scientific learning theory and user interviews, we propose preliminary requirements and design principles for an adaptive argumentation learning tool. Furthermore, we present a first instantiation of those principles.
  • Publication
    An AI Approach for Predicting Audience Reach of Presentation Slides
    There is a near overflow of presentation slides on digital platforms, such as SlideShare.net, with 40 million. This presents a challenge in assessing their projected impact due to its high complexity and required expertise. We propose a novel approach using machine learning techniques to predict presentation slide audience reach. We crawled a unique dataset of over 8000 slides and extracted relevant attributes. A model was trained where we are the first to employ both numerical and textual inputs. Initial results with an R² value of 0.579 suggest that the audience reach of presentation slides can be automatically evaluated. Our findings contribute to the current understanding of the assessment of online documents, introducing possibilities for further research, such as focusing on domain-specific applications and incorporating them as tools for decision support in content management systems on sharing platforms.
  • Publication
    Towards Assisted Excellence: Designing an AI-Based System for Presentation Slide Evaluation
    Creating and disseminating high-quality presentation slides have be come a foundation for effective communication in educational, corporate, and scientific domains. This study addresses the challenge of enhancing the quality of user-generated presentation content amidst the vast quantities of existing re sources. The study is concerned with an ongoing design science research project that focuses on constructing a nascent design theory for a novel AI-based slide evaluation support system (SESS) that aims to assist users, particularly educators, create high-quality presentation slides. The proposed concept leverages recent developments in Generative Artificial Intelligence (genAI) to analyze multi modal user-generated content. Drawing on signaling theory, user-generated online reviews, and expert interviews, this research aims to contribute to deline ating the capabilities of artificial intelligence in digital content evaluation, spe cifically in assisting users to improve the quality of their presentation slides. For practitioners, we offer a set of generalized design principles and design features for the implementation in the development of an AI-based SESS.
  • Publication
    WHERETO FOR AUTOMATED COACHING CONVERSATION: STRUCTURED INTERVENTION OR ADAPTIVE GENERATION?
    In an age of lifelong learning, it is important that adult learners can effectively use their motivation and resources to reach their learning goals. In conversation, coaches can intervene to promote learning goal attainment by using behavioural change techniques (BCTs). In a coaching chatbot, such techniques can be ordered in an established, structured way to good effect. With recent technological advances, chatbot responses can be generated adaptively; this means that BCTs can be applied in an adaptive but less structured way. It is yet unclear whether this latter form of configuring coaching interventions is viable, how they compare to more established structured interventions, and whether both methods can be combined. For the purpose of answering this, we propose a 2x2 experimental design with the two intervention types as factors and goal attainment as the dependent variable. Results will indicate avenues for automating skilled conversation including choice of technology.
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  • Publication
    What to Learn Next? Designing Personalized Learning Paths for Re-&Upskilling in Organizations
    ( 2023-01-06) ;
    Leonie Freise
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    Ulrich Bretschneider
    The fast-paced acceleration of digitalization requires extensive re-&upskilling, impacting a significant proportion of jobs worldwide. Technology-mediated learning platforms have become instrumental in addressing these efforts, as they can analyze platform data to provide personalized learning journeys. Such personalization is expected to increase employees’ empowerment, job satisfaction, and learning outcomes. However, the challenge lies in efficiently deploying these opportunities using novel technologies, prompting questions about the design and analysis of generating personalized learning paths in organizational learning. We, therefore, analyze and classify recent research on personalized learning paths into four major concepts (learning context, data, interface, and adaptation) with ten dimensions and 34 characteristics. Six expert interviews validate the taxonomy’s use and outline three exemplary use cases, undermining its feasibility. Information Systems researchers can use our taxonomy to develop theoretical models to study the effectiveness of personalized learning paths in intra-organizational re-&upskilling.
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