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Kontextabhängiges Prompt- und Flowdesign in Sprachassistenzsystemen
Kontextabhängiges Prompt- und Flowdesign in Sprachassistenzsystemen
With the rise of Conversational User Interfaces (CUIs), Human-Computer Interaction has made a leap towards natural and intuitive interactions between humans and computers. In the car, CUIs provide a particularly suitable and low-distraction interaction framework. However, inconsiderately designed speech-based interactions with high complexity can result in the opposite effect and increase drivers’ cognitive load. CUI designers for in-car interfaces hence face the challenge of developing voice-based interactions for a potentially vulnerable target group in a demanding setting. At the same time, they are not supported by sufficient as well as sufficiently tailored and empirically validated CUI design guidelines. This thesis closes this gap, by answering the research question of how to context-sensitively design CUI prompts and flows in the car. It does so under consideration of various conversational contexts to adequately account for the multi-facetted nature of human interactions. To this end, five research projects develop and validate concrete linguistic-driven design guidelines for CUI prompts and flows. To determine an efficient way of validating CUI prompts, a first round of studies was conducted to answer the research question of how to efficiently validate in-car prompts in the paper A Question of Fidelity. Online crowdsourcing studies emerged as a valid alternative to large-scale driving simulator studies. Subsequently, research identified linguistic parameters with a potential impact on the user experience of CUI prompts in How to Design the Perfect Prompt. Three ensuing studies validated the obtained linguistic best practices and prompt design guidelines for different conversational contexts, namely a) the type of interaction, b) the domain of interaction, and c) the initiation of interaction. How to Design the Perfect Prompt, Secure, Comfortable or Functional, and How May I Interrupt showed that CUI prompts need to display a suitable level of (in)formality, complexity/simplicity, and (im)mediacy. Furthermore, an informal, straightforward, and result-oriented speaking style under consideration of the abovementioned contexts is advised. Proactive prompts are thereby specifically dependent on a low level of linguistic complexity as well as a suggestive tone of voice. Additionally, proactive in-car interactions need to carefully consider when to interrupt drivers. To gain insights into best practices for designing CUI flows, the paper Failing With Grace explores error handling strategies from Human-Human-Interaction and their applicability to Human-Computer Interaction. The “Principle of Least Collaborative Effort” and concomitant considerations around so-called costs aid CUI practitioners in designing nuanced user-centric and efficient dialog flows for both successful and erroneous conversations.
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Meck, Anna-Maria
2024
English
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Meck, Anna-Maria (2024): Kontextabhängiges Prompt- und Flowdesign in Sprachassistenzsystemen. Dissertation, LMU München: Faculty for Languages and Literatures
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Abstract

With the rise of Conversational User Interfaces (CUIs), Human-Computer Interaction has made a leap towards natural and intuitive interactions between humans and computers. In the car, CUIs provide a particularly suitable and low-distraction interaction framework. However, inconsiderately designed speech-based interactions with high complexity can result in the opposite effect and increase drivers’ cognitive load. CUI designers for in-car interfaces hence face the challenge of developing voice-based interactions for a potentially vulnerable target group in a demanding setting. At the same time, they are not supported by sufficient as well as sufficiently tailored and empirically validated CUI design guidelines. This thesis closes this gap, by answering the research question of how to context-sensitively design CUI prompts and flows in the car. It does so under consideration of various conversational contexts to adequately account for the multi-facetted nature of human interactions. To this end, five research projects develop and validate concrete linguistic-driven design guidelines for CUI prompts and flows. To determine an efficient way of validating CUI prompts, a first round of studies was conducted to answer the research question of how to efficiently validate in-car prompts in the paper A Question of Fidelity. Online crowdsourcing studies emerged as a valid alternative to large-scale driving simulator studies. Subsequently, research identified linguistic parameters with a potential impact on the user experience of CUI prompts in How to Design the Perfect Prompt. Three ensuing studies validated the obtained linguistic best practices and prompt design guidelines for different conversational contexts, namely a) the type of interaction, b) the domain of interaction, and c) the initiation of interaction. How to Design the Perfect Prompt, Secure, Comfortable or Functional, and How May I Interrupt showed that CUI prompts need to display a suitable level of (in)formality, complexity/simplicity, and (im)mediacy. Furthermore, an informal, straightforward, and result-oriented speaking style under consideration of the abovementioned contexts is advised. Proactive prompts are thereby specifically dependent on a low level of linguistic complexity as well as a suggestive tone of voice. Additionally, proactive in-car interactions need to carefully consider when to interrupt drivers. To gain insights into best practices for designing CUI flows, the paper Failing With Grace explores error handling strategies from Human-Human-Interaction and their applicability to Human-Computer Interaction. The “Principle of Least Collaborative Effort” and concomitant considerations around so-called costs aid CUI practitioners in designing nuanced user-centric and efficient dialog flows for both successful and erroneous conversations.