Human Behavior and Emerging Technologies最新文献

The adoption of online learning modalities has increasingly become prevalent, particularly with the advent of COVID-19, aiming to ensure student access to learning materials. This significant shift towards offering online educational formats compels educational institutions to alter their approach and develop curricula to guarantee an optimal student experience and satisfaction within the online environment. The aim of this research is to comprehensively examine the key factors that significantly impact the satisfaction of undergraduate students with online learning in Vietnamese universities. The quantitative research methodology was implemented through the collection of surveys from a total of 437 Vietnamese students. Utilizing the PLS-SEM statistical approach, the findings reveal that technology, communication, course, outcome, and motivation for learning have significant positive influences on students’ satisfaction with online education during the COVID-19 pandemic, while the effect of instructors’ attitude and the sudden change from traditional to online classes have been found with as nonsignificant. Valuable implications and practical recommendations are suggested for educational organizations and institutions in Vietnam to enhance specific activities that promote students’ satisfaction with online learning and improve teaching methods provided by instructors.

Fake news has been linked to the rise of psychological disorders, the increased disbelief in science, and the erosion of democracy and freedom of speech. Online social networks are arguably the main vehicle of fake news spread. Educating online users with explanations is one way of preventing this spread. Understanding how online belief is formed and changed may offer a roadmap for such education. The literature includes surveys addressing online opinion formation and polarization; however, they usually address a single domain, such as politics, online marketing, health, and education, and do not make online belief change their primary focus. Unlike other studies, this work is the first to present a cross-domain systematic literature review of user studies, methodologies, and opinion model dimensions. It also includes the orthogonal polarization dimension, focusing on online belief change. We include peer-reviewed works published in 2020 and later found in four relevant scientific databases, excluding theoretical publications that did not offer validation through dataset experimentation or simulation. Bibliometric networks were constructed for better visualization, leading to the organization of the papers that passed the review criteria into a comprehensive taxonomy. Our findings show that a person’s individuality is the most significant influential force in online belief change. We show that online arguments that balance facts with emotionally evoking content are more efficient in changing their beliefs. Polarization was shown to be cross-correlated among multiple subjects, with politics being the central polarization pole. Polarized online networks start as networks with high opinion segregation, evolve into subnetworks of consensus, and achieve polarization around social network influencers. Trust in the information source was demonstrated to be the chief psychological construct that drives online users to polarization. This shows that changing the beliefs of influencers may create a positive snowball effect in changing the beliefs of polarized online social network users. These findings lay the groundwork for further research on using personalized explanations to reduce the harmful effects of online fake news on social networks.

This study is aimed at exploring the impact of artificial intelligence (AI) on academic research by conducting a focus group research strategy. The focus group consists of individuals who are actively involved in academic research and have experience working with AI technologies. The purpose of the focus group is to gather in-depth insights into how AI has influenced research methodologies, findings, and overall knowledge creation. The study will begin by identifying seven participants through purposive sampling, with an aim of recruiting a diverse group of individuals from various academic disciplines. Purposive sampling, also known as selective sampling, enhances the study’s validity by ensuring that the sample consists of individuals with a high level of expertise in the subject matter. Seven is large enough to generate a diverse range of perspectives and experiences and small enough to ensure that every participating academic researcher has a chance to contribute to the conversation. The focus group is conducted using a Zoom video conferencing to gather academics from different institutions across the world. It also eliminates distance issue required for conducting an in-person session. This provides opportunity to cover a wide array research specialization representation. Data analysis is conducted using a thematic analysis approach, with a focus on identifying key themes and patterns that emerge from the data. The findings of this study contribute to a better understanding of the impact of AI on academic research and provide insights into the potential future direction of AI in academic research. While the study is aimed at providing practical recommendations for researchers who are interested in incorporating AI into their research practices, it also ignites the conversation on future incorporation of technologies into academic research activity.

Throughout the COVID-19 crisis, scientists around the globe have engaged in science communication to an unprecedented degree to convey first-hand epidemiological knowledge and information on preventive measures. The present work is aimed at empirically investigating the impact of direct exposure to scientists as compared to general COVID-19-related media consumption (N = 698) on central cognitive, affective, and behavioral variables, based on the extended parallel process model (EPPM) and its adaptations. A segment of the sample comprises individuals recruited independently, while others were sourced from an online panel. Importantly, this study sample was conducted at the outset of the COVID-19 pandemic. The results revealed that direct exposure to scientists positively affected recipients’ knowledge and self-efficacy. General media consumption, by contrast, positively affected perceived threat as well as fear and uncertainty. Both sources positively affected the adherence to protective measures.

Technologies that stimulate human social and sexual impulses could affect users and societies. Here, we report on two experiments designed to test participant responses to (1) “virtual friend” chatbots that vary in capacity to engage users socially and emotionally (i.e., emotional sophistication) and (2) “digital lover” technologies—in the form of sex toys, sex robots, or virtual reality entities—that vary in capacity to physically stimulate users (i.e., physical sophistication). Participants (173 female, 176 male) read vignettes that each described a particular technology and then answered whether, if their romantic partner were to use the described technology, they would anticipate jealousy or anger, and whether they would prefer to see the technology banned. Participant anticipations of jealousy and anger were so similar that we combined them in a single composite measure. In experiment 1, both the anticipation of jealousy-anger and the inclination to ban chatbots increased with emotional sophistication, particularly in female participants. In experiment 2, both sexes anticipated greater jealousy-anger and were more inclined to ban more physically sophisticated digital lovers. Female participants expressed higher levels of both responses across the range of sophistication. Experiment 2 participants were more likely to anticipate jealousy-anger and more inclined to ban sex robots than sex toys or virtual reality lovers. Our results show only limited consistency with evolutionary theories concerning sex differences in jealousy. Generally, the anticipated levels of jealousy-anger and inclination to ban the described technologies were low, suggesting low levels of resistance to the idea of the technologies.

The rapid advancement and widespread adoption of voice assistance technology have shown promise in benefiting individuals with disabilities, offering increased social participation, independence, and leisure activities. However, barriers to their full utilization have been identified, leading to potential abandonment by users with disabilities. This rapid review is aimed at filling the gap in the literature by investigating the utilization of voice assistants among people with disabilities for independent living and community participation. A comprehensive search was conducted in academic literature databases, including PubMed, Embase, and Web of Science, and gray data was sourced from public social media domains through Infegy. The analysis included 48 articles and 281 social media posts that met the inclusion criteria. Neurodiversity, disabilities affecting vision, and general disabilities were the most frequently discussed categories in both sources. The most common tasks performed using voice assistants were interface control, reminders, and environmental control, with a focus on enabling independence. Barriers to use mentioned in the literature included cognitive load during use, speech interpretation, lack of nonverbal control, and privacy concerns, while gray data reported limited functionality and speech interpretation as primary barriers. Amazon Alexa was the most discussed brand in both sources. The findings highlight the need for further research and innovation to fully harness the potential benefits of voice assistants for individuals with disabilities. By addressing the identified barriers and tailoring voice assistance technology to cater to the specific needs of different disability types, this technology can become a powerful tool for enhancing the lives of individuals with disabilities and promoting greater independence and community participation.

This study investigates the association between social media use and attitudes toward AI technologies. A nationally representative two-wave longitudinal survey (N = 5110) examined the mediating roles of perceived AI fairness and threat concerning three AI technologies: algorithms, facial recognition technology, and driverless passenger vehicles. Hypotheses were derived from media effect theories and the heuristic and systematic model of human-AI adoption. The results showed that social media use predicted more positive attitudes toward the three AI technologies indirectly through increased perceived AI fairness and reduced perceived AI threat. The findings contribute to our understanding of social media effects on attitudes toward AI and the underlying psychological mechanisms, providing valuable theoretical insights and practical implications.

AI technologies transform language instruction by offering feedback, support, and guidance to students, ultimately leading to a more effective and efficient learning experience. The present study investigated the impacts of integrating Writerly and Google Docs to enhance EFL writing instruction. It also assessed students’ perceptions towards using these AI technologies. The study employed a quasiexperimental pretest-posttest two-group design. It used a mixed-methods approach, utilizing tests, questionnaires, focus group discussions, and teacher diaries to gather data from a sample of 92 randomly selected participants. In the experimental group, students enhanced their writing skills through the integration of Writerly and Google Docs, while the control group students received instruction using the traditional paper and pencil feedback system. When the quantitative data were analyzed through independent samples T-test and descriptive statistics, the qualitative data were analyzed thematically. The results confirmed that the integration of Writerly and Google Docs AI technologies, significantly improved EFL writing instruction, as evidenced by a statistically significant difference in writing performance between the experimental and control groups. Hence, students who learned through the integration of Writerly and Google Docs showed improved writing performance as they were able to produce essays that effectively addressed task achievement, coherence and cohesion, lexical resource, and grammatical range and accuracy, whereas those who learned through the conventional method were less effective in producing quality essays. The findings also revealed that the experimental group students had positive perceptions towards integrating Writerly and Google Docs because they found these AI writing technologies interesting, effective, goal-oriented, and supportive. Consequently, this study recommends researchers, curriculum designers, material designers, teachers, and students pay due attention to Writerly and Google Docs.

Digital computers were invented in the 1940s. They are sophisticated and versatile machines whose functioning is grounded in elaborate theory. Advances in theory and the availability of computers helped computer science to develop as an academic discipline, and university departments for the same started coming up in the 1960s. Computer science covers all phenomenon related to computers and consists primarily of man-made laws governing building, programming, and using computers. Computational thinking is a way of thinking influenced by computers and computer science. There are two schools of thought on computational thinking. The first school sees computational thinking as the use of computers to explore the world, while the other sees computational thinking as the application of concepts from computer science to solve real-world problems. Scholars typically agree that computational thinking has four essential components, viz., abstraction, decomposition, algorithm design, and generalization. Computational thinking is often feted by computer scientists as a useful skill that can be used by anybody anywhere. However, it is necessary to find out ways for successfully using computational thinking in domains other than computer science before it can be declared a universal skill.

Voice assistants (VAs) are becoming a feature of our everyday life. Yet, the user experience (UX) is often limited, leading to underuse, disengagement, and abandonment. Co-designing interactions for VAs with potential end-users can be useful. Crowdsourcing this process online and anonymously may add value. However, most work has been done in the English-speaking West on dialogue data sets. We must be sensitive to cultural differences in language, social interactions, and attitudes towards technology. Our aims were to explore the value of co-designing VAs in the non-Western context of Japan and demonstrate the necessity of cultural sensitivity. We conducted an online elicitation study (N = 135) where Americans (n = 64) and Japanese people (n = 71) imagined dialogues (N = 282) and activities (N = 73) with future VAs. We discuss the implications for coimagining interactions with future VAs, offer design guidelines for the Japanese and English-speaking US contexts, and suggest opportunities for cultural plurality in VA design and scholarship.