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Designing Pedagogical Conversational Agents for Achieving Common Ground
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2023 | DESRIST | Citations: 0
Authors: Tolzin, Antonia; Körner, Anita; Dickhaut, Ernestine; Janson, Andreas; Rummer, Ralf; Leimeister, Jan Marco

Abstract: As educational organizations face difficulties in providing personalized learnin ... Expand

Abstract: As educational organizations face difficulties in providing personalized learning material or individual learning support., pedagogical conversational agents (PCAs) promise individualized learning for students. However, the problem of conversational breakdowns of PCAs and consequently poor learning outcomes still exist. Hence, effective and grounded communication between learners and PCAs is fundamental to improving learning processes and outcomes. As understanding each other and the conversational grounding is crucial for conversations between humans and PCAs, we propose common ground theory as a foundation for designing a PCA. Conducting a design science research project, we propose theory-motivated design principles and instantiate them in a PCA. We evaluate the utility of the artifact with an experimental study in higher education to inform the subsequent design iterations. We contribute design knowledge on conversational agents in learning settings, enabling researchers and practitioners to develop PCAs based on common ground research in education and providing avenues for future research. Thereby, we can secure further understanding of learning processes based on grounding communication. Collapse

Semantic filters: randomized field experiment kernel theory

Topics: Google+ mobile application user requirement

Methods: data transformation principal component analysis design artifact design science design principle

Theories: theory of shared mental models
Designing Effective Conversational Repair Strategies for Chatbots

2022 | European Conference On Information Systems | Citations: 6
Authors: Reinkemeier, Fabian; Gnewuch, Ulrich

Abstract: Conversational breakdowns often force users to go through frustrating loops of ... Expand

Abstract: Conversational breakdowns often force users to go through frustrating loops of trial and error when trying to get answers from chatbots. Although research has emphasized the potential of conversational repair strategies in helping users resolve breakdowns, design knowledge for implementing such strategies is scarce. To address this challenge, we are conducting a design science research (DSR) project to design effective repair strategies that help users recover from conversational breakdowns with chatbots. This paper presents the first design cycle, proposing, instantiating, and evaluating our first design principle on identifying the cause of conversational breakdowns. Using 21,736 real-world user messages from a large insurance company, we conducted a cluster analysis of 5,668 messages leading to breakdowns, identified four distinct breakdown types, and built a classifier that can be used to automatically identify breakdown causes in real time. Our research contributes with prescriptive knowledge for designing repair strategies in conversational breakdown situations. Collapse

Semantic filters: randomized field experiment kernel theory

Topics: knowledge base user experience personality anonymity

Methods: chatbot cluster analysis design science computational algorithm design principle

Theories: communication theory
Designing Effective Mobile Health Apps: Does Combining Behavior Change Techniques Really Create Synergies?
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2021 | Journal of Management Information Systems | Citations: 17
Authors: Spohrer, Kai; Fallon, Monica; Hoehle, Hartmut; Heinzl, Armin

Abstract: Although several theories could be applied to stimulate mobile health (mHealth) ... Expand

Abstract: Although several theories could be applied to stimulate mobile health (mHealth) use and change people's health behaviors, theory-driven mHealth designs are scarce. Instead, developers and researchers often implement behavior change techniques and mix multiple techniques without explicitly considering their underlying theoretical mechanisms. We call this practice into question and propose that combining behavior change techniques doesnot necessarily result in synergistic effects. Drawing on theories of protection motivation and social upward comparison, we engage in explanatory design theorizing to understand interactions of behavior change techniques and their implications for mHealth design. We design, theorize about, and evaluate four mHealth prototypes for stress alleviation. In a five-week field experiment with n = 138 participants and a subsequent qualitative substantiation, we show that there is a negative interaction effect of protection motivation and social upward comparison, rendering their combined application less effective. Our findings elicit mutual boundary conditions for theories of protection motivation and social upward comparison. If mechanisms of one theory are present, they restrict the effectiveness of mechanisms of the other theory. Thus, mHealth developers need to use caution when combining different behavior change techniques within one mHealth artifact. Collapse

Semantic filters: randomized field experiment kernel theory

Topics: mobile health mobile application smartphone anonymity electronic health

Methods: design artifact theory development survey experiment field experiment

Theories: protection motivation theory health belief model theory of change social comparison theory

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Found 3 articles

Designing Pedagogical Conversational Agents for Achieving Common Ground

Designing Effective Conversational Repair Strategies for Chatbots

Designing Effective Mobile Health Apps: Does Combining Behavior Change Techniques Really Create Synergies?