This thesis investigates how conversational interaction, specifically through alignment dialogue, can help behavior support systems better understand and adapt to a user's real-time needs. Behavior support systems are often deployed to help individuals manage health-related goals, such as increasing physical activity or adhering to dietary guidelines. However, these systems tend to rely on pre-collected behavioral data, which makes it difficult to adapt support to the nuanced, changing circumstances of daily lives. We thus introduce alignment dialogues as conversations between an AI system and a user aimed at eliciting reasons for non-adherence, surfacing underlying values, and revealing contextual barriers. Rather than merely logging behavior, these dialogues help uncover the user's lived experience to iteratively build a more accurate user model at run time.
After conducting an exploratory focus group study, we found that probing for deeper knowledge like user values often triggers negative emotional responses, suggesting that the interpersonal impact of an agent's language is a critical design dimension. These findings informed a large-scale user experiment testing different dialogue strategies, which highlighted a tension between conversational depth and usability: while open-ended questions allow for greater perceived completeness, they also increase cognitive effort compared to multiple-choice formats. To assess how these outputs could be used in healthcare, we explored sharing dialogue content with professionals as a Conversational Intermediary AI (CIAI). Interviews and experiments suggest that while these agents facilitate early diagnoses and sensitive discussions, the effectiveness of the information is sensitive to the presentation format and length of the dialogue.
Finally, we demonstrate technical feasibility through a system prototype that transforms user responses into structured knowledge using RDF triples and a user knowledge graph. This allows the system to cross-reference user data with medical expertise to recommend actions that are contextually relevant and clinically sound. Our findings show that the effectiveness of alignment is determined not only by the accuracy of the information gathered but by the interpersonal and emotional quality of the interaction. This approach redefines personalization from categorization to interpretation, where systems must learn to make sense of the user's situation. Ultimately, this research suggests that personalization grounded in dialogue opens new paths for hybrid collaboration, advocating for behavior support systems that adapt by conversing, reflecting, and aligning with users.
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Effective support from personal assistive technologies relies on accurate user models that capture user values, preferences, and context. Knowledge-based techniques model these relationships, enabling support agents to align their actions with user values. However, understanding values in a single context is insufficient due to the dynamic nature of behaviour. This study explores the use of dialogue strategies to update user models. Participants were randomly assigned to different strategies and they discussed one randomly chosen non-adherence situation with the agent. Then, their emotions, acquired information accuracy, completeness, and dialogue experience were rated. Our findings suggest that multiple-choice dialogues may limit response depth, reducing the perceived completeness of behaviour reasons. In contrast, open-ended questions allow more detailed input but require more time and effort, potentially worsening the dialogue experience. Through inductive coding, we identified key topics, such as individual challenges, priorities, tangible outcomes, and values, essential for constructing personalised user models. We also analyzed conversation paths to improve dialogue-based user model updates in support agents. Further research is needed to refine the relationship between dialogue strategies and self-conscious emotions, considering diverse backgrounds and health goals, while enhancing dialogue design. ... Read more

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. ... Read more

Behavior change support systems need to take into account individual needs and preferences to provide appropriate support. In this demonstration, we illustrate how this might be achieved through the explicit modeling of user characteristics within knowledge graphs (KG), captured in a dialogue between the system and the user. We demonstrate how up-to-date information enables reasoning for providing personalized support. ... Read more

For personal assistive technologies to effectively support users, they need a user model that records information about the user, such as their goals, values, and context. Knowledge-based techniques can model the relationships between these concepts, enabling the support agent to act in accordance with the user's values. However, user models require updating over time to accommodate changes and continuously align with what the user deems important. In our work, we propose and investigate the use of human-agent alignment dialogues for establishing whether user model updates are needed and acquiring the necessary information for these updates. In this paper, we perform an exploratory qualitative focus group study in which we investigate participants' opinions about written examples of alignment dialogues, as a foundation for their design. Transcripts were analyzed using thematic analysis. A main theme that emerged concerns the potential impact of agent utterances on the user's feelings about themselves and about the agent. ... Read more

This project proposes a different way of looking at AI alignment, namely by introducing AI Alignment Dialogues. We argue that alignment dialogues have a number of advantages in comparison to data-driven approaches, especially for behaviour support agents, which aim to support users in achieving their desired future behaviours rather than their current behaviours. The advantages of alignment dialogues include allowing the users to directly convey higher-level concepts to the agent and making the agent more transparent and trusted. ... Read more