We present the first dataset, an annotation scheme, discourse analysis, and baseline experiments on argumentation and domain content types in scholarly articles on political science, specifically on the theory of International Relations (IR). The dataset comprises over 1 600 sentences stemming from three foundational articles on Neo-Realism, Liberalism, and Constructivism. We show that our annotation scheme enables educationally-relevant insight into the scholarly IR discourse and that state-of-the-art classifiers, while effective in distinguishing basic argumentative elements (Claims and Support/Attack relations) reaching up to 0.97 micro F1 , require domain-specific training and fine-tuning on the more fine-grained tasks of relation and content type prediction.

Conversational agents in virtual environments are an established approach for immersively conveying the information and narratives of museums and cultural heritage while expanding their accessibility to a wider and remote audience. The rapid development of large language models and text-to-speech technologies has raised the agents’ conversational level significantly, which allows their use for proactive guidance of visitors. This raises the vital question of how such agents should be visually represented to promote Knowledge transfer in immersive virtual environments. In this paper, we compared two representation concepts for agent embodiments in the context of a virtual museum by examining a stylized humanoid guide and a novel animism-based approach that enables users to talk to exhibited objects. Our work addresses the challenge of naturally introducing a virtual educational environment to users and encouraging their interest and engagement with the content. A user study $(N=29)$ revealed high usability and similar presence scores for the experience with each of the embodiments. A majority of participants showed a preference for the animated objects. In terms of user experience, they evoked significant stimulation and high levels of engagement. Our results suggest that agents that show emotions through appropriate word choice influence engagement levels. Based on our findings, we recommend humanoid guides for delivering general background information, while animated objects promote detailed questions about their own stories and a more stimulating exchange.

Embodied conversational agents are an established approach for immersively conveying the narratives and information of virtual museums. Recent advances in large language models and text-to-speech systems enable the active guidance of users by agents and raise the question of how such assistants should be visually embodied to engage visitors and promote knowledge transfer. This demo showcases a stylized humanoid guide, a novel animism-based approach with speaking objects, and a combination of both concepts to interactively guide users through a digital replica of the famous Gropius Room at the Bauhaus University in Weimar.

Given the material nature of data physicalization, their creators need to make many design decisions, including material choices and scale. Our study explores the impact of scale in physicalization, motivated by the assumption that size can affect user experience. We created two different physicalizations (for the same dataset) in three sizes each, and evaluated the resulting six objects with a questionnaire approach and interviews. Our findings highlight that scale needs to be chosen wisely given its impact on representation legibility (ease of viewing and understanding) and affordances for interaction. We discuss factors to take into account when designing large-scale physicalizations and in further research on the potential role of scale in physicalizaton. In particular, we argue that for large-scale physicalizations, scale should matter and communicate meaning, for instance, supporting an intuitive understanding of magnitudes, or a specific experience. Thus, scale needs to be an explicit design decision, that interacts with other design parameters.