Participatory design effectively engages stakeholders in technology development but is often constrained by small, resource-intensive activities. This study explores a scalable complementary method, enabling broad pattern identification in the design for interfaces in autonomous vehicles. We implemented a human-centered, iterative process that combined crowd creativity, structured participatory principles, and expert feedback. Across iterations, participant concepts evolved from simple cues to multimodal systems. Novel suggestions ranged from personalized features, like tracking lights, to inclusive elements like haptic feedback, progressively refining designs toward greater contextual awareness. To assess outcomes, we compared representative designs: a popular-design, reflecting the most frequently proposed ideas, and an innovative-design, merging participant innovations with expert input. Both were evaluated against a benchmark through video-based simulations. Results show that the popular-design outperformed the alternatives on both interpretability and user experience, with expert-validated innovations performing second best. These findings highlight the potential of scalable participatory methods for shaping emerging technologies.

As autonomous vehicles become more integrated into shared human environments, effective communication with road users is essential for ensuring safety. While previous research has focused on developing external Human-Machine Interfaces (eHMIs) to facilitate these interactions, we argue that involving users in the early creative stages can help address key challenges in the development of this technology. To explore this, our study adopts a participatory, crowd-sourced approach to gather user-generated ideas for eHMI designs. Participants were first introduced to fundamental eHMI concepts, equipping them to sketch their own design ideas in response to scenarios with varying levels of perceived risk. An initial pre-study with 29 participants showed that while they actively engaged in the process, there was a need to refine task objectives and encourage deeper reflection. To address these challenges, a follow-up study with 50 participants was conducted. The results revealed a strong preference for autonomous vehicles to communicate their awareness and intentions using lights (LEDs and projections), symbols, and text. Participants’ sketches prioritized multi-modal communication, directionality, and adaptability to enhance clarity, consistently integrating familiar vehicle elements to improve intuitiveness.

This paper presents an experiment with three artificial agents adopting different strategies when being interrupted by human conversational partners. The agent either ignored the interruption (the most common behavior in conversational engines to date), yielded the turn to the human conversational partner right away, or acknowledged the interruption, finished its thought and then responded to the content of the interruption. Our results show that this change in the agent’s conversational behavior had a significant impact on which personality traits people assigned to the agent, as well as how much they enjoyed interacting with it. Moreover, the data also indicates that human interlocutors adapted their own conversational behavior. Our findings suggest that the interactive behavior of an artificial agent should be carefully designed to match its desired personality and the intended conversational dynamics.

Acquiring a second language in adulthood differs considerably from the approach taken at younger ages. Learning rates tend to decrease during adolescence, and socio-emotional characteristics, like motivation and expectations, take a different perspective for adults. In particular, acquiring communicative competence is a stronger objective for older learners, as an appropriate use of language in social contexts ensures a better community immersion and well-being. This skill is best attained through interactions with proficient speakers, but if this option is not available, social robots present a good alternative for this purpose. However, to obtain optimal results, a robot companion should adapt to the learner's proficiency level and motivation continuously to encourage speech production and increase flu-ency. Our work attempts to achieve this goal by developing an adaptive robot that modifies its spoken dialogue strategy, and visual feedback, to reflect a student's knowledge, proficiency and engagement levels in situated interactions for longterm learning.

The main aim of this study is to investigate if verbal, vocal, and facial information can be used to identify low-engaged second language learners in robot-led conversation practice. The experiments were performed on voice recordings and video data from 50 conversations, in which a robotic head talks with pairs of adult language learners using four different interaction strategies with varying robot-learner focus and initiative. It was found that these robot interaction strategies influenced learner activity and engagement. The verbal analysis indicated that learners with low activity rated the robot significantly lower on two out of four scales related to social competence. The acoustic vocal and video-based facial analysis, based on manual annotations or machine learning classification, both showed that learners with low engagement rated the robot’s social competencies consistently, and in several cases significantly, lower, and in addition rated the learning effectiveness lower. The agreement between manual and automatic identification of low-engaged learners based on voice recordings or face videos was further found to be adequate for future use. These experiments constitute a first step towards enabling adaption to learners’ activity and engagement through within- and between-strategy changes of the robot’s interaction with learners.

Many small group activities, like working teams or study groups, have a high dependency on the skill of each group member. Differences in skill level among participants can affect not only the performance of a team but also influence the social interaction of its members. In these circumstances, an active member could balance individual participation without exerting direct pressure on specific members by using indirect means of communication, such as gaze behaviors. Similarly, in this study, we evaluate whether a social robot can balance the level of participation in a language skill-dependent game, played by a native speaker and a second language learner. In a between-subjects study (N = 72), we compared an adaptive robot gaze behavior, that was targeted to increase the level of contribution of the least active player, with a non-adaptive gaze behavior. Our results imply that, while overall levels of speech participation were influenced predominantly by personal traits of the participants, the robot's adaptive gaze behavior could shape the interaction among participants which lead to more even participation during the game.

The performance of Automatic Speech Recognition (ASR)systems has constantly increased in state-of-the-art develop-ment. However, performance tends to decrease considerably inmore challenging conditions (e.g., background noise, multiplespeaker social conversations) and with more atypical speakers(e.g., children, non-native speakers or people with speech dis-orders), which signifies that general improvements do not nec-essarily transfer to applications that rely on ASR, e.g., educa-tional software for younger students or language learners. Inthis study, we focus on the gap in performance between recog-nition results for native and non-native, read and spontaneous,Swedish utterances transcribed by different ASR services. Wecompare the recognition results using Word Error Rate and an-alyze the linguistic factors that may generate the observed tran-scription errors.

Uncertainty is a frequently occurring affective state that learners experience during the acquisition of a second language. This state can constitute both a learning opportunity and a source of learner frustration. An appropriate detection could therefore benefit the learning process by reducing cognitive instability. In this study, we use a dyadic practice conversation between an adult second-language learner and a social robot to elicit events of uncertainty through the manipulation of the robot's spoken utterances (increased lexical complexity or prosody modifications). The characteristics of these events are then used to analyze multi-party practice conversations between a robot and two learners. Classification models are trained with multimodal features from annotated events of listener (un)certainty. We report the performance of our models on different settings, (sub)turn segments and multimodal inputs.