Social cues and implications for designing expert and competent artificial agents: A systematic review

Do a pedagogical agent's clothing and an animated video's setting affect learning?

Pedagogical agents are often used to enhance social cues in learning materials. The inclusion of pedagogical agents raises several design questions, for example on what kind of clothing the agent should wear. Further, it is not yet clear how the setting of an animated learning video (i.e., the digitally created background) affects learning. In an online experiment (N = 200), we investigated whether creating thematically appropriate clothing and setting has some added value in that it improves learning outcomes in comparison to more neutral assets. Whereas all participants acquired knowledge from the animated video, there were no main effects of clothing and setting for any of the dependent variables, but an interaction for learning outcomes (η p 2 = 0.02 ), indicating that the appropriately dressed agent worked better combined with the inappropriate setting than with the appropriate setting. Overall, given those non-significant main effects and the small effect size of the interaction, there seem to be some degrees of freedom for designers of pedagogical agents in animated learning videos. However, these degrees of freedom may be limited to at least moderate (i.e., neutral) levels of appropriateness.

The Impact of Virtual Humans on Psychosomatic Medicine

OBJECTIVE

Virtual humans are likely to enhance the delivery of health care over the next decade. Virtual humans are artificially intelligent computer agents with hyperrealistic, autonomously animated embodiments based on affective computing techniques. Virtual humans could be programmed to screen for health conditions, triage patients, and deliver health interventions, with appropriate facial expressions and body gestures, functioning as a supplement to human care. This article provides a perspective on the implications of virtual humans for behavioral and psychosomatic medicine, and health psychology.

METHODS

A narrative review was conducted to integrate observations and findings from research on virtual humans from 91 articles in this multidisciplinary area.

RESULTS

Virtual humans can be used for multimodal behavior analysis of patients, individualized tailoring of interventions, and detection of changes of psychological and behavioral measures over time. Virtual humans can also pair the scalability of a website with the interactivity and relational skills of a human tele-therapist. Research is beginning to show the acceptability, feasibility, and preliminary effectiveness of virtual humans in a range of populations. Virtual humans can be easily tailored in terms of their appearance, voice, and language, and may be adapted to fit the characteristics of a patient population or hard-to-reach groups. If co-designed with these communities, virtual humans may help to promote health care engagement and improve outcomes.

CONCLUSIONS

Virtual humans can engage and motivate patients, and deliver personalized psychological and behavioral health care. This article provides an overview of the potential impact of virtual humans on psychosomatic medicine and discusses ethical implications.

Multi-Chatbot or Single-Chatbot? The Effects of M-Commerce Chatbot Interface on Source Credibility, Social Presence, Trust, and Purchase Intention

Most chatbot interfaces in contemporary m-commerce platforms feature a single chatbot that provides recommendations for all product categories. Nonetheless, there is an emerging research interest in multi-chatbot systems designating multiple chatbots as product/domain-specific advisers. Given the dearth of studies investigating the effects of multi-chatbot versus single-chatbot in the m-commerce context, we addressed this research gap by conducting an online between-subjects experiment to explore how the m-commerce chatbot interface types can differently influence source credibility, social presence, trusting beliefs, and purchase intention. Based on 154 valid responses, the single-chatbot interface led to higher social presence and trusting beliefs toward the m-commerce platform than the multi-chatbot interface. Males attributed the chatbot with higher competence and reported higher purchase intention through the m-commerce platform when engaging with the single-chatbot interface than the multi-chatbot interface. These findings suggest that designating chatbots as product-specific advisers in a multi-chatbot interface without labels to accentuate expertise could not evoke the users to categorize them as product specialists. Moreover, the multi-chatbot interface could have imposed user confusion and unfamiliarity cues, decreasing trust in the m-commerce platform. These findings’ theoretical, design, and managerial implications are discussed through the lens of the computers-are-social-actors paradigm, source credibility theory, source specialization, multiple source effect, and m-commerce behavioral research.