Computers in Human Behavior: Artificial Humans最新文献

Social robots as storytellers combine advantages of human storytellers – such as embodiment, gestures, and gaze – and audio books – large repertoire of voices, sound effects, and background music. However, research on adding non-speech sounds to robotic storytelling is yet in its infancy. The current series of four online studies investigates the influence of sound effects and background music in robotic storytelling on recipients’ storytelling experience and enjoyment, robot perception, and emotion induction across different story genres, i.e. horror, detective, romantic and humorous stories. Results indicate increased enjoyment for romantic stories and a trend for decreased fatigue for all genres when adding sound effects and background music to the robotic storytelling. Of the four genres examined, horror stories seem to benefit the most from the addition of non-speech sounds. Future research should provide guidelines for the selection of music and sound effects to improve the realization of non-speech sound-accompanied robotic storytelling. In conclusion, our ongoing research suggests that the integration of sound effects and background music holds promise for enhancing robotic storytelling, and our genre comparison provides first guidance of when to use them.

Generative AI applications have increasingly gained visibility in recent educational literature. Yet less is known about how access to generative tools, such as ChatGPT, influences help-seeking during complex problem-solving. In this paper, we aim to advance the understanding of learners' use of a support strategy (hints) when solving data science programming tasks in an online AI-enabled learning environment. The study compared two conditions: students solving problems in DaTu with AI assistance (N = 45) and those without AI assistance (N = 44). Findings reveal no difference in hint-seeking behavior between the two groups, suggesting that the integration of AI assistance has minimal impact on how individuals seek help. The findings also suggest that the availability of AI assistance does not necessarily reduce learners’ reliance on support strategies (such as hints). The current study advances data science education and research by exploring the influence of AI assistance during complex data science problem-solving. We discuss implications and identify paths for future research.

The emphasis on artificial intelligence (AI) is rapidly increasing across many diverse aspects of society. This manuscript discusses some of the key topics related to the expansion of AI. These include a comparison of the unique cognitive capabilities of human intelligence with AI, and the potential risks of using AI in clinical medicine. The general public attitudes towards AI are also discussed, including patient perspectives. As the promotion of AI in high-risk situations such as clinical medicine expands, the limitations, risks and benefits of AI need to be better understood.

Theory suggests that robots with human-like mental capabilities (i.e., high agency and experience) evoke stronger aversion than robots without these capabilities. Yet, while several studies support this prediction, there is also evidence that the mental prowess of robots could be evaluated positively, at least by some individuals. To help resolving this ambivalence, we focused on rather stable individual differences that may shape users’ responses to machines with different levels of (perceived) mental ability. Specifically, we explored four key variables as potential moderators: monotheistic religiosity, the tendency to anthropomorphize, prior attitudes towards robots, and the general affinity for complex technology. Two pre-registered online experiments (N₁ = 391, N₂ = 617) were conducted, using text vignettes to introduce participants to a robot with or without complex, human-like capabilities. Results showed that negative attitudes towards robots increased the relative aversion against machines with (vs. without) complex minds, whereas technology affinity weakened the difference between conditions. Results for monotheistic religiosity turned out mixed, while the tendency to anthropomorphize had no significant impact on the evoked aversion. Overall, we conclude that certain individual differences play an important role in perceptions of machines with complex minds and should be considered in future research.

The interplay between artificial intelligence (AI) and psychology, particularly in personality assessment, represents an important emerging area of research. Accurate personality trait estimation is crucial not only for enhancing personalization in human-computer interaction but also for a wide variety of applications ranging from mental health to education. This paper analyzes the capability of a generic chatbot, ChatGPT, to effectively infer personality traits from short texts. We report the results of a comprehensive user study featuring texts written in Czech by a representative population sample of 155 participants. Their self-assessments based on the Big Five Inventory (BFI) questionnaire serve as the ground truth. We compare the personality trait estimations made by ChatGPT against those by human raters and report ChatGPT's competitive performance in inferring personality traits from text. We also uncover a ‘positivity bias’ in ChatGPT's assessments across all personality dimensions and explore the impact of prompt composition on accuracy. This work contributes to the understanding of AI capabilities in psychological assessment, highlighting both the potential and limitations of using large language models for personality inference. Our research underscores the importance of responsible AI development, considering ethical implications such as privacy, consent, autonomy, and bias in AI applications.

Mental disorders impact a large proportion of individuals worldwide, with young adults being particularly susceptible to poor mental health. Past research shows that help-seeking self-stigma plays a vital role in deterring help-seeking among young adults; however, this relationship has primarily been examined in the context of human-delivered psychotherapy. The present study aimed to understand how young adults’ perceptions of help-seeking self-stigma associated with different modes of psychotherapy, specifically human-delivered and artificial intelligence (AI)-delivered, influence attitudes towards using AI chatbots for psychotherapy. This study employed a cross-sectional survey design to measure perceived help-seeking self-stigma and attitudes towards both human- and AI-delivered psychotherapy. The results demonstrated that high help-seeking self-stigma associated with human-delivered psychotherapy was linked to more negative attitudes towards human-delivered psychotherapy but more positive attitudes towards AI-delivered psychotherapy. Moreover, high help-seeking self-stigma associated with AI-delivered psychotherapy was linked to more negative attitudes towards AI-delivered psychotherapy but more positive attitudes towards human-delivered psychotherapy. These findings have important real-world implications for future clinical practice and mental health service delivery. The results indicate that young adults who are reluctant to engage with human-delivered psychotherapy due to help-seeking self-stigma may be more inclined to seek help through alternative modes of psychotherapy, such as AI chatbots. Limitations and future directions are discussed.

With the increasing influence of artificial intelligence (AI) on various aspects of society, understanding public attitudes towards AI becomes crucial. This study investigated attitudes towards AI among Hong Kong middle-aged and older adults. In June 2023, an online survey was conducted among a sample of 740 smartphone users aged 45 years or older (Max = 78) in Hong Kong. Using exploratory factor analysis, we found three factors from the General Attitude to Artificial Intelligence Scale (GAAIS) - Perils, Power, and Promises. Subsequently, with latent profile analysis we revealed three latent profiles: (i) Enthusiasts (18.4%; high on Promises and Power but low on Perils); (ii) Skeptics (12.3%; high on Perils but low on Promises and Power), and (iii) Indecisive (69.3%; moderate on all three factors). The Enthusiasts were more likely to be male, with higher socio-economic status, better self-rated health, and greater mobile device proficiency, optimism, innovativeness, but also less insecurity with technology, compared to the Indecisive, and then to the Skeptics. Our findings suggest that most middle-aged and older adults in Hong Kong hold an ambivalent view towards AI, appreciating its power and potentials while also cognizant of the perils it may entail. Our findings are timely considering the recent debates on ethical use of AI evoked by smart phone applications such as ChatGPT and will be valuable for practitioners and scholars for developing inclusive AI-facilitated services and applications.

Background

In recent years, chatbots developed for mental health intervention purposes have been widely implemented to solve the challenges of workforce shortage and accessibility issues faced by traditional health services. Nevertheless, research assessing the technologies’ potential and risks remains sporadic.

Purpose

This review aims to synthesise the existing research on engagement, user attitude, and effectiveness of psychological chatbot interventions.

Method

A systematic review was conducted using relevant peer-reviewed literature since 2010. These studies were derived from six databases: PubMed, PsycINFO, Web of Science, Science Direct, Scopus and IEEE Xplore.

Results

Engagement level with chatbots that complied with digital intervention standards, lead to positive mental health outcomes. Although users had some uncertainties about the usability of these tools, positive attitudes towards chatbots regarding user experience and acceptability were frequently identified due to the chatbots' psychological capabilities and unique functions. High levels of outcome efficacy were found for those with depression. The differences in demographics, psychological approaches, and featured technologies could also influence the extent of mental health chatbot performances.

Conclusion

Positive attitudes and engagement with chatbots, as well as positive mental health outcomes, shows chatbot technology is a promising modality for mental health intervention. However, implementing them amongst some demographics or with novel features should be carefully considered. Further research using mainstream mental health chatbots and evaluating them simultaneously with standardised measures of engagement, user attitude, and effectiveness is necessary for intervention development.