Thomas Mildner

This double-blind cluster-randomized controlled study examined the effectiveness of a multilingual physical-digital recruitment strategy to increase the involvement of linguistically diverse households in the German sub-survey of the World Health Organization European Childhood Obesity Surveillance Initiative. Students in primary schools received German study invitations either alone or with a supplemental flyer containing a QR-code for accessing translations online. A total of 114 classrooms (2306 students) received the flyer, while 115 classrooms (2276 students) served as control group. Main outcomes were active of responses to invitations and study participation. Response proportions were 48% overall, 48.6% in the flyer and 47.4% in the control group. Participation proportions were 26.7% overall, 27.8% in the flyer and 25.4% (571/2251) in the control groups. The flyer increased the odds of participation (14.9, 95% CI 1.003–1.315), but not the odds of active responses. In medium-social-level schools, response and participation proportions in the flyer group rose by 1.5% (ROR 1.015, 95% CI 1.003–1.027) and by 2% (ROR 1.020, 95% CI 1.005–1.034), respectively, for each percentage point increase in the proportion of non-German speaking households per classroom. The flyer’s effect in low- and high-social level schools was inconclusive. Although modest in magnitude, the addition of the flyer increased overall participation and, in particular, the involvement of linguistically diverse households in medium-social-level schools.

In recent years, the widespread adoption of voice user interfaces (VUIs) highlighted the growing significance of speech interaction in our everyday activities. However, researchers, designers, and developers lack a dedicated interaction model tailored to the intricacies of communication with speech agents. This paper proposes a novel interaction model specifically crafted for speech interaction to better align with the evolving landscape of these systems. Incorporating traditional elements such as sender, message, and receiver, our model also integrates dynamic factors like context, user preferences, and evolving agent capabilities. Drawing from communication models and human-computer interaction (HCI) frameworks, this model aims to deepen our understanding of the process of human-agent speech interaction in real-world scenarios. By initiating discourse on a dedicated speech interaction model, this work serves as a basis for future exploration and refinement, adaptable to evolving technologies and user needs.

Deceptive and coercive design practices are increasingly used by companies to extract profit, harvest data, and limit consumer choice. Dark patterns represent the most common contemporary amalgamation of these problematic practices, connecting designers, technologists, scholars, regulators, and legal professionals in transdisciplinary dialogue. However, a lack of universally accepted definitions across the academic, legislative, practitioner, and regulatory space has likely limited the impact that scholarship on dark patterns might have in supporting sanctions and evolved design practices. In this paper, we seek to support the development of a shared language of dark patterns, harmonizing ten existing regulatory and academic taxonomies of dark patterns and proposing a three-level ontology with standardized definitions for 64 synthesized dark pattern types across low-, meso-, and high-level patterns. We illustrate how this ontology can support translational research and regulatory action, including transdisciplinary pathways to extend our initial types through new empirical work across application and technology domains.