This paper explores the potential of conversational intermediary AI (CIAI) between patients and healthcare providers, focusing specifically on promoting healthier lifestyles for Type 2 diabetes. CIAI aims to address the constraint of limited healthcare provider time by acting as an intermediary in-between infrequent consultations. CIAI enables healthcare providers to understand patients better and offer personalized support. Through an exploratory focus group with healthcare domain experts, we gather insights into CIAI’s envisioned in diabetes care. Our findings highlight the potential benefits of CIAI in diabetes care.@en

Changing one’s behavior is difficult, so many people look towards technology for help. However, most current behavior change support systems are inflexible in that they support one type of behavior change and do not reason about
how that behavior is embedded in larger behavior patterns. To allow users to flexibly decide what they desire to change, a system needs to represent and reason about that desire. Moreover, we argue that reasoning about the context of a behavior could improve an agent’s support. Therefore, we propose a formal framework for a reasoning agent to represent and reason about the personal behavioral context of desired user changes. This framework models an individual’s possible and current behavior, their desire for change, as well as other relevant changes that a system could use to support a desired change. In a user survey we show that people feel these other relevant changes would be useful in more flexibly supporting their desired change in behavior. This work provides a foundation for more flexible personalized behavior change support.@en

Support agents that help users in their daily lives need to take into account not only the user’s characteristics, but also the social situation of the user. Existing work on including social context uses some type of situation cue as an input to information processing techniques in order to assess the expected behavior of the user. However, research shows that it is important to also determine the meaning of a situation, a step which we refer to as social situation comprehension. We propose using psychological characteristics of situations, which have been proposed in social science for ascribing meaning to situations, as the basis for social situation comprehension. Using data from user studies, we evaluate this proposal from two perspectives. First, from a technical perspective, we show that psychological characteristics of situations can be used as input to predict the priority of social situations, and that psychological characteristics of situations can be predicted from the features of a social situation. Second, we investigate the role of the comprehension step in human–machine meaning making. We show that psychological characteristics can be successfully used as a basis for explanations given to users about the decisions of an agenda management personal assistant agent.

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Disabled people can benefit greatly from assistive digital technologies. However, this increased human-machine symbiosis makes it important that systems are personalized and transparent to users. Existing work often uses data-oriented approaches. However, these approaches lack transparency and make it hard to influence the system’s behavior. In this paper, we use knowledgebased techniques for personalization, introducing the concept of Semantic User Models for representing the behavior, values and capabilities of users. To allow the system to construct such a user model, we investigate the use of a conversational agent which can elicit the relevant information from users through dialogue. A conversational interface is essential for our case study of navigation support for visually impaired people, but in general, has the potential to enhance transparency as users know what the system represents about them. For such a dialogue to be effective, it is crucial that the user understands what the conversational agent is asking, i.e., that misalignments that decrease the transparency are avoided or resolved. In this paper, we investigate whether we can use a conversational agent for Semantic User Model elicitation, which types of misalignments can occur in this process and how they are related, and how misalignments can be reduced. We investigate this in two (iterative) qualitative studies (n ¼ 7 & n ¼ 8) with visually impaired people in which a personalized user model for navigation support is elicited via a dialogue with a conversational agent. Our results show four hierarchically structured levels of humanagent misalignment. We identify several design solutions for reducing misalignments, which point to the need for restricting the generic user model to what is needed in the domain under consideration. With this research, we lay a foundation for conversational agents capable of eliciting Semantic User Models.@en