Users often mindlessly treat machines such as voice assistants (VAs) like social beings, speaking politely despite VAs functioning as commandable tools that do not require polite expressions. In fact, unnecessary words can even impair interactions, causing frustration. This contrast between social and functional interactions is especially relevant in family homes with children, where VAs are often integrated long-term. Since children are still learning social communication rules, they might adopt negatively connotated communication patterns with the VA, such as a lack of politeness, commanding phrasing, or verbal abuse. Over 2.5 years, 128 parents therefore completed six online surveys about their own and their children’s communication behaviour towards and relationship with their VA, as well as their own social perception of and satisfaction with the VA. Multilevel analyses revealed that parents considered themselves to be more polite to the VA than their children. Perceived relationship closeness between children and VA positively predicted children’s commanding, politeness, and verbal abuse tendencies towards the VA over time, whereas for the parents, relationship closeness only influenced politeness. The findings highlight the potential impact of social perceptions on long-term communication with VAs and tackle the concern of VA’s negative impact on human-human communication.

Conversational artificial intelligence (AI) is increasingly being incorporated into mental health applications such as chatbot coaches.Since AI-based chatbots are seen as free from human biases or emotional reactions, users tend to expect non-judgemental interactions with them.However, as in interactions with other people, expectations can be violated in interactions with chatbots.Using Expectancy Violations Theory as a framework, this study aims to explore how these violations, in contrast to confirmations, may impact users' trust, feeling of being understood, and connectedness to the chatbot.Due to the specific setting (AI-based chatbot acting as mental health coach), fear of stigma and technical affinity were also considered.In an online experiment with a 2x2 between-subjects design (N = 95), participants interacted with a chatbot that was either described as judgmental or non-judgmental and either displayed a judgmental or non-judgmental communication behavior.Results show that the non-judgmental chatbot behavior results in more positive trust evaluations than the judgmental chatbot behavior.Neither the confirmation nor the violation of expectations was found to have a significant impact.Overall, the chatbot's communication behavior was more decisive for evaluations than users' expectations, technical affinity, or fear of stigma.

People increasingly encounter AI-powered chatbots in their daily lives which often make use of anthropomorphic cues -potentially with the intention to trick users into trusting the system.This study investigates the impact of anthropomorphic design cues on user perception and behavioral intention in human-chatbot interactions focusing on the social bot My AI within Snapchat.In a between-subjects experimental design (N=323) participants interacted with either an anthropomorphic version of the chatbot, featuring human-like cues such as personalized greetings, use of emoticons, and a human-like appearance, or a less anthropomorphic version minimizing these cues.The findings challenge prior assumptions by revealing that anthropomorphic cues neither influenced the intention to use the chatbot nor the trust in it.However, perceived anthropomorphism was found to enhance trust in the chatbot, which in turn positively influenced use intention.Additionally, users who perceived the chatbot as more human-like were more likely to view it as a friend, a perception that further mediated the effect of anthropomorphism on trust.These results suggest that while anthropomorphic design does not universally increase trust and use intention directly, perceived anthropomorphism and perception as a friend plays an important role in user interactions with AI-based chatbots.Future studies will need to clarify whether the design decision to frame the interaction as dialogue might be sufficient to increase perceived anthropomorphism and its related effects.

Since robots trigger social reactions as social interaction partners but are less socially complex and intimidating, they may offer a promising way to train young adults’ social skills and improve their social confidence. Since social robots can take over different roles and display different behaviors, two questions arise: Should the robot take over the role of a coach (higher expertise, higher hierarchical level) or a training partner (equal expertise, equal hierarchical level)? Is the training style more appreciated when it is challenging (focusing on shortcomings that can be improved) or encouraging (focusing on successes that should be strengthened)? These two aspects are examined in an online study with 2x2 between-subjects design where participants get to interact with a virtual robot taking over the different training roles and styles. Results show that a robot coach is evaluated more positively than a robot training partner, but otherwise does not lead to any evaluation differences. The challenging in comparison to the encouraging training style, however, leads to clearly negative evaluations of the robot and the interaction with it on various measures. Furthermore, participants’ loneliness was found to have an overarching negative effect on the robot's perceived sociability.

Despite the rising prevalence of intelligent voice assistants in people's homes, it remains opaque to most users how they function. With the overall goal to foster informed usage and responsible handling of personal data, the user's understanding increasingly moves into the focus of research. Particularly in the context of black box technologies such as voice assistants, individuals build intuitive mental models (also called folk theories) which can act as a guide for how they understand and interact with such systems. To shed light on individuals' mental models regarding intelligent voice assistants, we applied a visual elicitation method during a citizen science workshop: 26 participants were asked to visualize how they imagine a voice assistant to function. The resulting drawings were categorized based on their theorization complexity level. While basic awareness of the overall functionality (user input -- something happens -- voice assistant output) was found in every drawing, 13 participants revealed mental models on how the input is received (e.g., audio recording and speech processing), six on how the information is then processed (e.g., database requests and data categorization), and five on how the output is generated (e.g., speech synthesis). Overall, the results underline the need to address individuals' gaps in understanding of intelligent technology that is already in widespread use.

Intelligent voice assistants such as Alexa and Google Home enable individuals who would otherwise be excluded to access the Internet. For instance, older adults have difficulties operating certain interfaces such as a computer or smartphone because of physical or mental constraints. Due to their intuitive, speech-based control in dialogue form, voice assistants hold the potential to assist older adults and promote their independence. However, for a successful adoption, the technology needs to be accepted and trusted by the target group. With the aim to explore older adults' attitudes towards intelligent voice assistants, semi-structured interviews were conducted with 19 adults between 65 and 86 years. The interviews included a wizard-of-oz interaction with a voice assistant and a vignette describing a scenario in which the voice assistant appears to be eavesdropping. On the one hand, the interviewees evaluated the voice assistant as useful, easy to comprehend, and pleasant. On the other hand, they voiced general data protection concerns, fears of being overheard and observed as well as a strong aversion to unprompted recommendations and advertisement. Most strikingly, fears of being patronized or manipulated by the voice assistant were mentioned frequently, which was justified by a strong need to maintain independence -- particularly with advancing age.