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Artificial Intelligence (AI) technology in the educational context, particularly chatbotics, has made significant changes in learning English. This mixed-methods study is intended to explore university students' attitudes toward the potential role of artificial intelligence (AI)-assisted mobile applications. Meanwhile, the role of social presence and human-likeness on learner motivation was examined through a chatbot lens. A total of 256 English as a foreign language (EFL) learners interacted with a chatbot known as Computer Simulation in Educational Communication (CSIEC). Participants' audio-recorded practices, transcriptions, three scales of social presence, learner motivation, and human-likeness, along with a semi-structured focus group interview, were used to collect data, Structural Equation Modeling (SEM) was used for data analysis. Moreover, thematic analysis was adopted to explore the participants' attitudes and perceptions toward using CSIES. The quantitative results indicated that learner motivation was significantly predicted by social presence and human-likeness. The thematic analysis of qualitative data reflected that the attributed descriptions to the CSIEC teacher enhanced learners' motivation, eagerness, and confidence to learn English. The findings of this study may be used to guide future research in using chatbots outside the classroom to serve as learning companions, and educators can utilize them to tailor assessment and feedback procedures. [ABSTRACT FROM AUTHOR]

Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and complex reasoning. However, their alignment with human emotions and values, which is critical for real-world applications, has not been systematically evaluated. Here, we assessed LLMs' Emotional Intelligence (EI), encompassing emotion recognition, interpretation, and understanding, which is necessary for effective communication and social interactions. Specifically, we first developed a novel psychometric assessment focusing on Emotion Understanding (EU), a core component of EI. This test is an objective, performancedriven, and text-based evaluation, which requires evaluating complex emotions in realistic scenarios, providing a consistent assessment for both human and LLM capabilities. With a reference frame constructed from over 500 adults, we tested a variety of mainstream LLMs. Most achieved above-average Emotional Quotient (EQ) scores, with GPT-4 exceeding 89% of human participants with an EQ of 117. Interestingly, a multivariate pattern analysis revealed that some LLMs apparently did not rely on the human-like mechanism to achieve human-level performance, as their representational patterns were qualitatively distinct from humans. In addition, we discussed the impact of factors such as model size, training method, and architecture on LLMs' EQ. In summary, our study presents one of the first psychometric evaluations of the human-like characteristics of LLMs, which may shed light on the future development of LLMs aiming for both high intellectual and emotional intelligence. Project website: https://emotional-intelligence.github.io/ [ABSTRACT FROM AUTHOR]

Purpose: Virtual assistants are increasingly used for persuasive purposes, employing the different modalities of voice and text (or a combination of the two). In this study, the authors compare the persuasiveness of voice-and text-based virtual assistants. The authors argue for perceived human-likeness and cognitive load as underlying mechanisms that can explain why voice- and text-based assistants differ in their persuasive potential by suppressing the activation of consumers' persuasion knowledge. Design/methodology/approach: A pre-registered online-experiment (n = 450) implemented a text-based and two voice-based (with and without interaction history displayed in text) virtual assistants. Findings: Findings show that, contrary to expectations, a text-based assistant is perceived as more human-like compared to a voice-based assistant (regardless of whether the interaction history is displayed), which in turn positively influences brand attitudes and purchase intention. The authors also find that voice as a communication modality can increase persuasion knowledge by being cognitively more demanding in comparison to text. Practical implications: Simply using voice as a presumably human cue might not suffice to give virtual assistants a human-like appeal. For the development of virtual assistants, it might be beneficial to actively engage consumers to increase awareness of persuasion. Originality/value: The current study adds to the emergent research stream considering virtual assistants in explicitly exploring modality differences between voice and text (and a combination of the two) and provides insights into the effects of persuasion coming from virtual assistants. [ABSTRACT FROM AUTHOR]

The Turing Test (TT) is best regarded as a model to test for intelligence, where an entity's intelligence is inferred from its ability to be attributed with 'human-likeness' during a text-based conversation. The problem with this model, however, is that it does not care if or how well an entity produces a meaningful conversation, as long as its interactions are humanlike enough. As a consequence, the TT attracts projects that concentrate on how best to fool the judges. In light of this, I propose a new version of the TT: the Questioning Turing Test (QTT). Here, the entity has to produce an enquiry rather than a conversation; and it is parametrised along two further dimensions in addition to 'human-likeness': 'correctness', evaluating if the entity accomplishes the enquiry; and 'strategicness', evaluating how well the entity accomplishes the enquiry, in terms of the number of questions asked (the fewer, the better). [ABSTRACT FROM AUTHOR]

Research on anthrozoology has greatly increased in recent decades, especially with regard to anthropomorphism and attitudes toward animals in general. Nevertheless, these studies have rarely distinguished between different nonhuman species. Previous studies have indicated human preferences for apes, a finding which usually is explained by their strong likeness to humans. Moreover, anthrozoological research has rarely focused on dolphins even though they seem to have a unique position in human–animal relationships, one that has been reported throughout history. This paper presents two studies examining which characteristics humans attribute to dolphins, apes, and whales (Study 1) and whether humans attribute more positive and fewer negative characteristics to dolphins than to apes or whales (Study 2). In study 1, 86 German university students were asked to name characteristics of one species (dolphins, apes, or whales). The participants suggested many more positive than negative attributes and predominately characterized apes as human-like. In study 2, the important attributes from study 1 were included in a questionnaire comprising items referring to 12 positive and six negative attributes. The participants in study 2 (n = 258 German university students) rated dolphins significantly more positively than apes on six of the 12 positive and on five of the six negative attributes. Furthermore, the participants rated dolphins more positively than whales on most of the attributes. In sum, the results of these two studies confirm the attribution of positive characteristics to dolphins, but provide no evidence of a glorification of dolphins as frequently suggested by anecdotal reports. The findings are discussed with regard to the frequently claimed influence of human-likeness on human preferences for different species. [ABSTRACT FROM AUTHOR]

• Human-likeness is about intrinsic rather than extrinsic qualities of a humanoid robot. • Human-likeness is not higher for human-looking robots compared to non-human looking robots. • Human and non-human looking robots are not experienced differently (i.e. involvement, eeriness, task-related experiences). • There is little evidence for Media Equation nor for Uncanny effects and to date, the Singularity between humans and machines seems far off. Design of Artificial Intelligence and robotics habitually assumes that adding more humanlike features improves the user experience, mainly kept in check by suspicion of uncanny effects. Three strands of theorizing are brought together for the first time and empirically put to the test: Media Equation (and in its wake, Computers Are Social Actors), Uncanny Valley theory, and as an extreme of human-likeness assumptions, the Singularity. We measured the user experience of real-life visitors of a number of seminars who were checked in either by Smart Dynamics' Iwaa, Hanson's Sophia robot, Sophia's on-screen avatar, or a human assistant. Results showed that human-likeness was not in appearance or behavior but in attributed qualities of being alive. Media Equation, Singularity, and Uncanny hypotheses were not confirmed. We discuss the imprecision in theorizing about human-likeness and rather opt for machines that 'function adequately.' [ABSTRACT FROM AUTHOR]

Recently, there is an increasing trend to study the impact of anthropomorphism on human trust. However, previous research has shown divergent results. In this study, shared autonomous vehicles (SAVs), an emerging mobility solution, are used to explore the threshold effects of anthropomorphism on human trust. Structural equation modelling is deployed to test the research model based on a valid survey sample of 451 respondents. Results show that a superficial level of anthropomorphism can significantly boost human trust via interaction quality and facilitating condition. By contrast, a deep level of anthropomorphism would decrease human trust. Although the negative effect does not reach statistical significance based on the whole sample, results of the multigroup analysis show that a deep level of anthropomorphism has a significant negative effect on human-SAV interaction quality when respondents possess the following characteristics: (1) male, (2) low-income, (3) low-education, or (4) no-vehicle ownership. Regarding theoretical contribution, this study enriches the literature by identifying the threshold effects of anthropomorphism on human trust. Regarding policy and management implications, this study offers some implications on adding anthropomorphic features to SAVs. • The threshold effects of anthropomorphism (ANT) on human trust are investigated. • Superficial ANT boosts trust via interaction quality and facilitating condition. • Superficial ANT effects are moderated by income and vehicle ownership. • Deep ANT effects are moderated by gender, income, education, and vehicle ownership. [ABSTRACT FROM AUTHOR]

The uncanny valley hypothesis describes how increased human-likeness of artificial entities, ironically, could elicit a surge of negative reactions from people. Much research has studied the uncanny valley hypothesis, but little research has sought to examine people's reactions to a broad range of human-likeness manifested in real-world robots. We focused on examining people's emotional responses to real-world, as opposed to hypothetical, robots because these robots impact real-life human–robot interactions. We measured both positive and negative emotional responses to a large collection of full-body images of robots (N = 251) with various human-like features. We found evidence for the existence of not one, but two uncanny valleys. Mori's uncanny valley emerged for high human-like robots and a second uncanny valley emerged for moderately low human-like robots. We attributed these valleys to unique combinations of perceptual mismatches between human-like features, specified by a match between surface and facial feature dimensions accompanied by a mismatch with the body-manipulator dimension. We also found that patterns of the uncanny valleys differed between positive (shinwakan) and negative (bukimi) emotional responses. Lastly, the word uncanny appeared to be an unreliable measure of the uncanny valley. Implications for robot design and the uncanny valley research are discussed. • We examined the existence of the uncanny valley with 251 real-world robots. • We found two uncanny valleys for high and moderately low human-like robots. • Unique combinations of perceptual mismatch in human-likeness explained the valleys. • The specific patterns of the valleys differed for positive and negative responses. • The word uncanny did not appear to be a reliable measure of the uncanny valley. [ABSTRACT FROM AUTHOR]