Subjects: Psychology >> Cognitive Psychology submitted time 2024-05-08
Abstract： With the advent of the era of human-robot coexistence, robots gradually penetrate into children’s lives. Robots play an important role in children’s study and life, and effective human-robot interaction is conducive to robots to play a greater role. Trust is one of the prerequisites for effective interaction between humans and robots. Do children trust robots the same as trusting people? As the trend of robot development, how does anthropomorphism affect children’s trust in robots? This research adopted the trust game paradigm of Berg et al. (1995) and Evans et al. (2013). The trust behavior of children aged 4-6 in the economic game was investigated through two experiments. At the same time, anthropomorphic factors that may affect children’s trust in robots are investigated, including anthropomorphic appearance (anthropomorphic appearance) and anthropomorphic behavior (verbal feedback and social contingent interaction). In the first experiment, by investigating children’s trust behavior in robots NAO and JIBO (high anthropomorphism VS low anthropomorphism) in anonymous trust game, it was found that the trust of 4-year-old children in robots was significantly lower than that of 5-year-old and 6-year-old children. However, the influence of appearance anthropomorphism only appears in 6-year-old children, and the trust of children is positively correlated with the degree of appearance anthropomorphism of robots. In the second experiment, the robot was made to have anthropomorphic behavior by using WeChat video calls with people, NAO and JIBO, and the trust behavior of children to different trust objects was investigated in the anonymous trust game, and the role of anthropomorphic behavior was investigated. The results show that children’s trust can be significantly improved when the robot had anthropomorphic behavior characteristics. It can be seen that the trust of children aged 4~6 in robots is not only related to their age, but also influenced by the anthropomorphism of robots (anthropomorphism in appearance and anthropomorphism in behavior), and the degree of anthropomorphism is positively related to children’s trust behavior.
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Developmental Psychology submitted time 2023-05-06
Abstract： [Objective]A new generation of interactive models, called digital voice assistants(DVAs), can respond to young children's speech requests automatically and interactwith them by voice. Research on the development of young children's epistemic trustin DVAs is scarce. Previous research has concentrated on the development and influencing factors of young children's epistemic trust in human informants or traditional electronic media (e.g., computers, webpages, internet). The semisocialnature of these devices determines the specific theoretical and practical value ofinvestigating young children's epistemic trust in DVAs. Based on this, the purposeof the current study was to investigate the epistemic trust of young children (aged3-6) and adults in DVAs in various domains and to confirm the significance of accuracyin their trust. [Methods] The paradigm of dual-informant sources was employed in both experiments.A sample size of 88 children was required for an effect size of w= 0.30, 1–β=0.8, α= 0.05, according to G*Power 3.1. In Experiment 1, 30 adults and 90 childrenaged 4-6 were given testimony from distinct information sources (DVAs vs. humans)in either the natural or social domain to investigate the children's willingnessto ask questions, explicit trust judgments, and final endorsements. Whereas thenatural domain involved a task to label novel things, the social domain involvedinquiry into social customs. The accuracy of the informants was manipulated inExperiment 2, which was based on Experiment 1, and 90 children aged 3-5 and 30 adultswere exposed to various informants. [Results] The research participants were asked questions about their willingnessto ask, explicit trust judgments, and final endorsements. The results of Experiment1 showed that the children preferred to ask the DVAs questions about the naturaldomain rather than the social domain, with the DVAs being preferred overall . Moreover, the 6-year-old children preferred the DVAs as the information source morethan the 4- to 5-year-old children. The adults were more likely to trust the DVAsthan the young children. The results of Experiment 2 revealed that the children ofall ages and adults were more likely to accept correct informant testimony in boththe natural and social domains. In other words, the children were more likely touse the current accuracy of informants as a cue to assess and decide which informantto trust, and when the DVAs lost their accuracy, the children's preference disappeared along with their intellectual trust. The preference for accurate informants was more obvious in the adults and 4- to 5-year-olds than in the 3-year-olds, with the 3-year-olds being less sensitive to accuracy. Accuracy wasan essential indicator of the DVAs' dependability. [Limitations] This study did not include attribution tasks and the experimentalmaterial lacked some ecological properties. [Conclusions] Our study is the first to investigate the development of epistemictrust in DVAs among children aged 3-6 in China. The results show that children canuse DVAs as a source of information and knowledge. Young children become more likelyto believe the testimonies of DVAs as they grow older. Children are more likely totrust DVAs in the natural domain than in the social domain. Furthermore, youngchildren are more likely to accept the testimony of reliable informants. The resultsof this study may contribute to our understanding of the usability and utility ofhuman interaction with technological systems and offer suggestions for the use ofDVAs in homes and classrooms to support early learning.
Subjects: Psychology >> Educational Psychology Subjects: Psychology >> Developmental Psychology submitted time 2022-12-01
Abstract：
Background. Academic cheating has been a challenging problem for educators for centuries. It is well established that students often cheat not only on exams but also on homework. Despites recent changes in educational policy and practice, homework remains one of the most important academic tasks for elementary school students in China. However, most of the existing studies on academic cheating for the last century have focused almost exclusively on college and secondary school students, with few on the crucial elementary school period when academic integrity begins to form and develop. Further, most research has focused on cheating on exams with little on homework cheating. The present research aimed to bridge this significant gap in the literature. We used the advanced artificial intelligence methods to investigate the development of homework cheating in elementary school children and the key contributing factors so as to provide scientific basis for the development of early intervention methods to promote academic integrity and reduce cheating.
Method. We surveyed elementary school students from Grades 2 to 6 and obtained a valid sample of 2,098. The questionnaire included students’ self–reported cheating on homework (the dependent variable). The predictor variables included children’s ratings of (1) their perceptions of the severity of consequences for being caught cheating, (2) the extent to which they found cheating to be acceptable, and the extent to which they thought their peers considered cheating to be acceptable, (3) their perceptions of the effectiveness of various strategies adults use to reduce cheating, (4) how frequently they observed their peers engaging in cheating, and (5) several demographic variables. We used ensemble machine learning (an emerging artificial intelligence methodology) to capture the complex relations between cheating on homework and various predictor variables and used the Shapley importance values to identify the most important factors contributing children’s decisions to cheat on homework.
Results. Overall, 33% of elementary school students reported having cheated on homework, and the rate of such self–reported cheating behavior increased with grade. The best models with the ensemble machine learning accurately predicted the students’ homework cheating with a mean Area Under the Curve (AUC) value of 80.46%. The Shapley importance values showed that all predictors significantly contributed to the high performance of our computational models. However, their importance values varied significantly. Children’s cheating was most strongly predicted by their own beliefs about the acceptability of cheatings, how commonly and frequently they had observed their peers engaging in academic cheating, and their achievement level. Other predictors such as children’s beliefs about the severity of the possible consequences of cheating (e.g., being punished by one’s teacher), their beliefs about the effectiveness of cheating deterrence strategies (e.g., working harder) and demographic characteristics, though significantly, were not important predictors of elementary school children’s homework cheating.
Conclusion. This study for the first time examined elementary school students' homework cheating behavior. We used machine learning integration algorithms to systematically investigate the key factors contributing to elementary school students' homework cheating. The results showed that homework cheating already exists in the elementary school period and increases with grade. Advanced machine learning algorithms revealed that elementary school students' homework cheating largely depends on their acceptance of cheating, their peers' homework cheating, and their own academic performance level. The present findings advance our theoretical understanding of the early development of academic integrity and dishonesty and forms the scientific basis for developing early intervention programs to reduce academic cheating. In addition, this study also shows that machine learning, as the core method of artificial intelligence, is an effective method that can be used to analyze developmental data analysis.
Peer Review Status:Awaiting Review
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