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Human-robot interaction often employs the Wizard-of-Oz (WoZ) paradigm, where a human controls the robot. However, this approach has limitations, such as a lack of autonomy that impedes real-world applications. Large language models (LLMs) can replace WoZ in conversational tasks, such as brainstorming. We propose that, in such application domains, LLM-controlled robots can achieve comparable perceived social intelligence to WoZ-controlled robots. An experiment (n=27, within-subject design) tested this by having participants brainstorm with an LLM- and WoZ-controlled Furhat robot. Bayesian analyses revealed substantial evidence for the null model for perceived social intelligence, social presentation, and social information processing, indicating similar perceptions of social intelligence for WoZ- and LLM-controlled robots. Participants tentatively preferred the LLM-controlled robot, and reliably identified when the robot was WoZ- or LLM-controlled. This study highlights the potential of LLMs to replace the WoZ paradigm and transform HRI in various research and application domains.

With the large number and diversity of tools available to design and run studies with conversational agents, it can be challenging to find the right tool to address a particular research question. In this groupwork, together with attendees we will share our experiences using the tools that are currently available (e.g., Dialogflow, Rasa). This is followed by a co-creation session where we will dream up the ideal supportive tool.

Contact tracing apps (CTAs) have been introduced as a means to identify and isolate possible cases infected with COVID-19. Since the adoption rate determines the effectiveness of CTAs, it is important to examine what factors contribute to a higher CTA uptake. This study aimed to use an integrative approach to explain early CTA adoption, whereby three perspectives are distinguished: technology-related (derived from the Unified Theory of Acceptance and Use of Technology [UTAUT]), health-related (derived from the Health Belief Model [HBM]), and context-related. A survey was administered among a representative sample of the Dutch population (N = 1865). A hierarchical logistic regression analysis was performed in which the models were compared. Results showed that an integrative model including all three perspectives (i.e., UTAUT, HBM, and context-related variables) resulted in better model fit than any of the other models. All UTAUT variables were associated with CTA adoption in the expected directions. Regarding the HBM, self-efficacy, perceived barriers and perceived benefits were associated with CTA adoption in the expected directions. Several context-related variables, such as fear, were associated with CTA adoption. Our findings demonstrate that extending the UTAUT with preventive health-behavioral factors and contextual factors contribute to better understanding of CTA adoption.

Begin jaren nul deden biologische akkerbouw- en veebedrijven mee aan provinciale stimuleringsprojecten voor zogenoemde koppelbedrijven. Biologische akkerbouwers wilden de aanvoer van biologische mest veiligstellen, veehouders wilden voer uit de regio. Terugkijkend stellen akkerbouwers dat ze gras-klaver als rustgewas en het kunnen intensiveren van het overige deel van het bouwplan waardeerden.

The current study investigated how individual differences among children affect the added value of social robots for teaching second language (L2) vocabulary to young children. Specifically, we investigated the moderating role of three individual child characteristics deemed relevant for language learning: first language (L1) vocabulary knowledge, phonological memory, and selective attention. We expected children low in these abilities to particularly benefit from being assisted by a robot in a vocabulary training. An L2 English vocabulary training intervention consisting of seven sessions was administered to 193 monolingual Dutch five-year-old children over a three- to four-week period. Children were randomly assigned to one of three experimental conditions: 1) a tablet only, 2) a tablet and a robot that used deictic (pointing) gestures (the no-iconic-gestures condition), or 3) a tablet and a robot that used both deictic and iconic gestures (i.e., gestures depicting the target word; the iconic-gestures condition). There also was a control condition in which children did not receive a vocabulary training, but played dancing games with the robot. L2 word knowledge was measured directly after the training and two to four weeks later. In these post-tests, children in the experimental conditions outperformed children in the control condition on word knowledge, but there were no differences between the three experimental conditions. Several moderation effects were found. The robot's presence particularly benefited children with larger L1 vocabularies or poorer phonological memory, while children with smaller L1 vocabularies or better phonological memory performed better in the tablet-only condition. Children with larger L1 vocabularies and better phonological memory performed better in the no-iconic-gestures condition than in the iconic-gestures condition, while children with better selective attention performed better in the iconic-gestures condition than the no-icon