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Auflistung nach Autor:in "Maedche, Alexander"

1 - 10 von 40
  • Zeitschriftenartikel
    Advanced User Assistance Systems
    (Business & Information Systems Engineering: Vol. 58, No. 5, 2016) Maedche, Alexander; Morana, Stefan; Schacht, Silvia; Werth, Dirk; Krumeich, Julian
  • Zeitschriftenartikel
    AI-Based Digital Assistants
    (Business & Information Systems Engineering: Vol. 61, No. 4, 2019) Maedche, Alexander; Legner, Christine; Benlian, Alexander; Berger, Benedikt; Gimpel, Henner; Hess, Thomas; Hinz, Oliver; Morana, Stefan; Söllner, Matthias
  • Zeitschriftenartikel
    Algorithmic Management
    (Business & Information Systems Engineering: Vol. 64, No. 6, 2022) Benlian, Alexander; Wiener, Martin; Cram, W. Alec; Krasnova, Hanna; Maedche, Alexander; Möhlmann, Mareike; Recker, Jan; Remus, Ulrich
  • Konferenzbeitrag
    BotOrNot: A Platform for Conducting Experiments with Undisclosed Chat Agents
    (Mensch und Computer 2022 - Tagungsband, 2022) Hanschmann, Leon; Gnewuch, Ulrich; Maedche, Alexander
    Chatbots are omnipresent in today's online environments in work and private life. While early chatbots were easy to identify, recently released open-domain chatbots, such as GPT3 and Blenderbot2, increasingly blur the line between human and chatbot interaction. Dedicated research is required to better understand how different design configurations of open-domain chatbots impact their users, whether users are able to distinguish between human and bot chat agents, and how users respond to the undisclosed identity of their counterpart. However, there is a lack of experimental platforms that integrate state-of-the-art chatbots in order to enable such research. We therefore propose BotOrNot, which enables large-scale experimental research with participants in a Turing test setting. Participants are matched with either Blenderbot2/GPT3 or another human participant and tasked to figure out whether the counterpart is a human or bot. We designed the platform in a way that it allows to adapt the settings of the experiment to enable different experimental scenarios and follow an open approach allowing to integrate future bots via an API. Participants can personalize their avatars and chatbots can also be personalized with regards their personality and avatar.
  • Zeitschriftenartikel
    Call for Papers, Issue 1/2019
    (Business & Information Systems Engineering: Vol. 59, No. 4, 2017) Brocke, Jan; Hevner, Alan R.; Maedche, Alexander
  • Konferenzbeitrag
    A chatbot response generation system
    (Mensch und Computer 2020 - Tagungsband, 2020) Feine, Jasper; Morana, Stefan; Maedche, Alexander
    Developing successful chatbots is a non-trivial endeavor. In particular, the creation of high-quality natural language responses for chatbots remains a challenging and time-consuming task that often depends on high-quality training data and deep domain knowledge. As a consequence, it is essential to engage experts in the chatbot response development process which have the required domain knowledge. However, current tool support to engage domain experts in the response generation process is limited and often does not go beyond the exchange of decoupled prototypes and spreadsheets. In this paper, we present a system that enables chatbot developers to efficiently engage domain experts in the chatbot response generation process. More specifically, we introduce the underlying architecture of a system that connects to existing chatbots via an API, provides two improvement mechanisms for domain experts to improve chatbot responses during their chatbot interaction, and helps chatbot developers to review the collected response improvements with a sentiment supported review dashboard. Overall, the design of the system and its improvement mechanisms are useful extensions for chatbot development systems in order to support chatbot developers and domain experts to collaboratively enhance the natural language responses of a chatbot.
  • Zeitschriftenartikel
    Cognitive state detection with eye tracking in the field: an experience sampling study and its lessons learned
    (i-com: Vol. 23, No. 1, 2024) Langner, Moritz; Toreini, Peyman; Maedche, Alexander
    In the future, cognitive activity will be tracked in the same way how physical activity is tracked today. Eye-tracking technology is a promising off-body technology that provides access to relevant data for cognitive activity tracking. For building cognitive state models, continuous and longitudinal collection of eye-tracking and self-reported cognitive state label data is critical. In a field study with 11 students, we use experience sampling and our data collection system esmLoop to collect both cognitive state labels and eye-tracking data. We report descriptive results of the field study and develop supervised machine learning models for the detection of two eye-based cognitive states: cognitive load and flow. In addition, we articulate the lessons learned encountered during data collection and cognitive state model development to address the challenges of building generalizable and robust user models in the future. With this study, we contribute knowledge to bring eye-based cognitive state detection closer to real-world applications.
  • Zeitschriftenartikel
    Design Blueprint for Stress-Sensitive Adaptive Enterprise Systems
    (Business & Information Systems Engineering: Vol. 59, No. 4, 2017) Adam, Marc T. P.; Gimpel, Henner; Maedche, Alexander; Riedl, René
    Stress is a major problem in the human society, impairing the well-being, health, performance, and productivity of many people worldwide. Most notably, people increasingly experience stress during human-computer interactions because of the ubiquity of and permanent connection to information and communication technologies. This phenomenon is referred to as technostress. Enterprise systems, designed to improve the productivity of organizations, frequently contribute to this technostress and thereby counteract their objective. Based on theoretical foundations and input from exploratory interviews and focus group discussions, the paper presents a design blueprint for stress-sensitive adaptive enterprise systems (SSAESes). A major characteristic of SSAESes is that bio-signals (e.g., heart rate or skin conductance) are integrated as real-time stress measures, with the goal that systems automatically adapt to the users’ stress levels, thereby improving human-computer interactions. Various design interventions on the individual, technological, and organizational levels promise to directly affect stressors or moderate the impact of stressors on important negative effects (e.g., health or performance). However, designing and deploying SSAESes pose significant challenges with respect to technical feasibility, social and ethical acceptability, as well as adoption and use. Considering these challenges, the paper proposes a 4-stage step-by-step implementation approach. With this Research Note on technostress in organizations, the authors seek to stimulate the discussion about a timely and important phenomenon, particularly from a design science research perspective.
  • Konferenzbeitrag
    Designing Gaze-Aware Attention Feedback for Learning in Mixed Reality
    (Mensch und Computer 2022 - Tagungsband, 2022) Liu, Shi; Toreini, Peyman; Maedche, Alexander
    Mixed Reality (MR) has demonstrated its potential in the application field of education. In particular, in contrast to traditional learning, students using MR get the possibility of learning and exploring the content in a self-directed way. Meanwhile, research in learning technology has revealed the significance of supporting learning activities with feedback. Since such feedback is often missing in MR-based learning environments, we propose a solution of using eye-tracking in MR to provide gaze-aware attention feedback to students and evaluate it with potential users in a preliminary user study.
  • Konferenzbeitrag
    From ChatGPT to FactGPT: A Participatory Design Study to Mitigate the Effects of Large Language Model Hallucinations on Users
    (Mensch und Computer 2023 - Tagungsband, 2023) Leiser, Florian; Eckhardt, Sven; Knaeble, Merlin; Maedche, Alexander; Schwabe, Gerhard; Sunyaev, Ali
    Large language models (LLMs) like ChatGPT recently gained interest across all walks of life with their human-like quality in textual responses. Despite their success in research, healthcare, or education, LLMs frequently include incorrect information, called hallucinations, in their responses. These hallucinations could influence users to trust fake news or change their general beliefs. Therefore, we investigate mitigation strategies desired by users to enable identification of LLM hallucinations. To achieve this goal, we conduct a participatory design study where everyday users design interface features which are then assessed for their feasibility by machine learning (ML) experts. We find that many of the desired features are well-perceived by ML experts but are also considered as difficult to implement. Finally, we provide a list of desired features that should serve as a basis for mitigating the effect of LLM hallucinations on users.