Influence of Artificial Intelligence Emotional Trust on Academic Anxiety among College Students: A Moderated Chain Mediation Model

Influence of Artificial Intelligence Emotional Trust on Academic Anxiety among College Students: A Moderated Chain Mediation Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of Artificial Intelligence Emotional Trust on Academic Anxiety among College Students: A Moderated Chain Mediation Model Chao Zhang, Na Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8695381/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective - This study aims to determine the influence of artificial intelligence emotional trust (AIET) on the academic anxiety of college students. Methods - A questionnaire was used to survey a sample of 419 college students. Results - Results show that (1) AIET is significantly negatively correlated with negative low arousal and academic anxiety ( r =-0.45, -0.38, P <0.001) but positively correlated with time efficacy ( r =0.46, P <0.001). Moreover, negative low arousal is significantly negatively correlated with time efficacy ( r =-0.51, P <0.001) but significantly positively correlated with academic anxiety ( r =0.44, P <0.001). In addition, a significant negative correlation exists between time efficacy and academic anxiety ( r =-0.67, P <0.001). (2) Negative low arousal and sense of time efficacy are chain mediators in the effect of AIET on academic anxiety. (3) In the chain mediation, nonreactivity plays a moderating role in the relationship between time efficacy and academic anxiety. Conclusion - This research reveals the psychological factors that can lead to academic anxiety among college students. This research is beneficial to college educators to understand the formation of unhealthy learning behaviors among college students and has reference significance to college students to help them avoid serious mental health problems. Psychology Educational Psychology college student artificial intelligence emotional trust academic anxiety negative low arousal time efficacy nonreactivity Figures Figure 1 Figure 2 1. Introduction In 2024, the scale of the global artificial intelligence (AI) core industry reached US $ 864.3 billion, an increase of 22.1%, over US $ 707.8 billion in 2023, and the average annual growth rate in the past four years (2021–2024) was 21.5%. The industrial chain runs through the entire system, from the bottom technology link to the upper application link, including semiconductor chips, algorithm research and development, data acquisition and processing, platform construction, and industrial application. In addition, thanks tothe integration of large model, quantum computing and intelligent computing, generative AI is widely used. In 2025, the scale of the global AI core industry will exceed 1trillion dollars(Maslej et al., 2025 ). AI technology has economic benefits for social development and can exert a profound impact on college education. For example, generative AI, which is represented by ChatGPT, has become an important tool for college students’ daily learning. The large AI language model can not only generate text, pictures, and videos, based on college student users’ needs, but also improve their learning experience and make academic research highly interesting. However, the AI technology “algorithm black box” bottleneck has prevented users from understanding the basis of intelligent system decision making on a timely basis, which may cause them to doubt the output (Durán & Jongsma, 2021 ; Véliz et al., 2021 ). Therefore, human beings’ trust in intelligent agents can exert an important impact on their learning. Previous studies focused mainly on the impact of interpersonal trust on academic achievement, academic efficacy, and academic engagement and did not examine the impact of people’s trust in machines on academic emotions, such as academic anxiety and academic burnout (Payne et al., 2022 ; Benlahcene et al., 2024 ). Based on the new era of AI technology, this study explores how college students’ trust in intelligent agents can affect their academic anxiety to increase their interest in learning and enhance their learning quality and provide a reference for educators’ teaching. 2. Literature review 2.1 Artificial intelligence emotional trust and academic anxiety Academic anxiety refers to the emotional state of excessive tension, worry, and fear in the face of academic tasks or competition (Voelker, 2000). Meanwhile, AIET refers to an individual’s internal belief in an intelligent agent in the process of human–computer interaction. Such a belief is less affected by the objective world and more dependent on the personal feelings of the individual and the intuition of the intelligent agent (Riley, 2024). According to theory of interpersonal relationships, emotional trust is formed through social interaction with others and expressed as confidence in others’ ability to benefit oneself (McAllister, 1995). Emotional trust is perceptual in appearance and focuses on one’s trust in others’ intentions and emotions. Previous studies found a close relationship between trust and anxiety (AlRuthia et al, 2020; Låftman et al, 2024). However, other studies viewed trust and anxiety as two opposing concepts; that is, anxiety may arise from lack of trust. Such studies showed that the higher the emotional trust of college students in AI technology, the lower their degree of academic burnout, and the lower their probability of experiencing academic anxiety. Therefore, this study proposes the following hypothesis: Hypothesis 1 (H1): AIET can affect academic anxiety. 2.2 Mediating role of negative low arousal and time efficacy in the relationship between AIET and academic anxiety In addition to directly affecting academic anxiety, AIET may indirectly influence academic anxiety through negative low arousal. Negative low arousal refers to an individual’s negative emotions and relatively low overall arousal level (including physiological activation and psychological excitement), which is manifested mainly as boredom, helplessness, and depression (Govaerts & Grégoire, 2008). Such a state may lead to slowed behavior, reduced social and activity participation, and an overall decline in the quality of life. Social support theory posits that trust is an important component of social support and plays a pivotal role in establishing and maintaining social relationships and providing support (Gottlieb & Bergen, 2010). Previous studies found that the more the social support received by students on campus, the easier the mobilization of their positive emotions(Hirsch & Barton, 2011; Balk et al, 1993 ). Meanwhile, learning emotion theory suggests a significant correlation between negative low arousal and negative high arousal emotions（Pekrun et al., 2002）. Persistent negative low arousal may lead to emotional issues, which may trigger strong negative emotions under certain conditions and transition from negative low arousal to negative high arousal. For example, long-term boredom and depression may transform into intense anger or anxiety from a certain event. Some studies on academic emotions among college students observed a negative pattern; that is, when students experience burnout and lose interest in learning during the learning process, they may feel exhausted and develop anxiety toward learning（Pham Thi & Duong, 2024）. Therefore, this study proposes the following hypothesis: Hypothesis 2 (H2): Negative low arousal is a mediating variable in the relationship between AIET and academic anxiety. Moreover, AIET may indirectly affect academic anxiety through time efficacy. Time efficacy refers to an individual’s confidence and belief in their ability to manage their time（Middel, 2008）. Interpersonal trust may exert a positive impact on self-efficacy (Song & Zhao, 2025), because trust can create a positive and supportive atmosphere that can help individuals remain in a happy and optimistic psychological state. Such a positive psychological state can help individuals cope effectively with stress and challenges and thus increase their sense of self-efficacy. However, time efficacy can have a negative impact on academic anxiety. Social cognitive theory suggests that self-efficacy can affect an individual’s emotions through cognitive, motivational, and affective factors（Maddux et al., 1987）. Previous studies found that time efficacy can have a negative impact on negative emotions（Mesurado et al., 2018）. That is, individuals who exhibit high time efficacy can typically cope with time uncertainty, demonstrate stable emotions, and are less susceptible to external factors. In addition, individuals who exhibit a high sense of time efficacy tend to adopt positive coping strategies and use of their time reasonably to solve their problems when faced with difficulties and challenges, which may reduce their likelihood of experiencing negative emotions. Therefore, this study proposes the following hypothesis: Hypothesis 3 (H3): Time efficacy is a mediating variable in the relationship between AIET and academic anxiety. 2.3 Chain mediating effect of negative low arousal and time efficacy If negative low arousal and time efficacy can mediate the relationship between AIET and academic anxiety, then what is the relationship between negative low arousal and time efficacy? When individuals are in a state of negative low arousal, they will lack interest in learning tasks, which can lead to scattered attention and difficulty concentrating to complete such tasks. Such an inefficient state can directly affect individuals’ time efficacy and render them unable to complete expected tasks within a limited amount of time. For example, Hirvonen et al.（2020） found that negative low arousal in primary school students can significantly predict their learning efficacy. At the same time, individuals who are in a state of negative low arousal may lack the motivation to develop and implement effective time management strategies and may be unable to plan their time reasonably, set priorities, or use time-planning tools to improve their work efficiency. Such a decline in an individual’s time management ability will further reduce their time efficacy. Furthermore, researchers observed that negative low arousal can positively affect academic procrastination among middle school students（Cao et al., 2025; Wang et al., 2025）. Therefore, this study proposes the following hypothesis: Hypothesis 4 (H4): Negative low arousal and time efficacy will have a chain mediating effect in the relationship between AIET and academic anxiety . 2.4 Moderating effect of nonreactivity In the process of the indirect effect of AIET on academic anxiety, moderating variables may affect the mediating effect on the relationship between the factors, such as nonreactivity. Nonreactivity is a coping style that involves not immediately exhibiting a habitual response in the face of external stimuli and an important element of individual mindfulness, which is a positive psychological quality（Iani et al., 2019）. Individuals can effectively manage and regulate their emotions through mindfulness and thus reduce the impact of emotional fluctuations and negative emotions. Previous studies reported that individuals’ level of mindfulness is significantly negatively correlated with their negative emotions (e.g., anxiety, depression, or anger)（Hill & Updegraff, 2012; Keng & Tong, 2016）. Individuals who demonstrate high time efficacy can adopt a positive coping style to adjust their emotions and reduce the impact of negative emotions in the face of learning pressure. Therefore, this study proposes the following hypothesis: Hypothesis 5 (H5): Nonreactivity can regulate the relationship between time efficacy and academic anxiety in the chain mediation. In summary, though previous studies observed the impact of interpersonal trust on college students’ negative emotions, research has yet to determine whether human trust in intelligent agents can affect individuals’ negative emotions, as well as the role of negative low arousal and time efficacy as a mediator and that of nonreactivity as a moderator. The hypothesis model is presented in Figure 1. 3. Methods 3.1 Participants A total of 450 college students in Guangdong Province, China were invited through random sampling to participate in this study. After 31 invalid questionnaires (questionnaires with regular answers) were excluded, the collected questionnaires totaled 419 (for a recovery rate of 93.11%). Among the participants who provided valid responses, 70 were male and 349 were female. Moreover, 163 participants were from an urban area, and 256 participants were from a rural area. In addition, among the participants, 69 were only children and 350 were non-only children. The average age of the participants was 20.93 years, with a standard deviation of 2.08 years. 3.2 Instruments 3.2.1 Artificial intelligence emotional trust scale The AIET scale revised by Liu, J. H.(2024), which is composed of six items, was used in this study. A sample item is, “When the task is difficult, I think I can rely on artificial intelligence.” The scale uses a seven-point Likert scale scoring method that ranges from 1 ( completely disagree ) to 7 ( completely agree ), and the higher the total score, the higher the AIET level. In this study, the Cronbach’s α coefficient of the scale is 0.85. 3.2.2 Negative low arousal scale The negative low arousal scale from the Multidimensional State Boredom Questionnaire, edited by Hunter et al. (2016), was used in this study. The scale is composed of five items, such as “For me, everything is repetitive and boring.” The scale employs a seven-point Likert scale scoring method that ranges from 1 ( completely disagree ) to 7 ( completely agree ), with a high total score indicating a high level of negative low arousal. In this study, the Cronbach’s α coefficient of the scale is 0.86. 3.2.3 Time efficacy scale The time efficacy scale in the college student learning time management strategy questionnaire revised by Chen, L. (2012) was used in this study. The scale consists of seven items, such as “I often have no deadline for completing my own work.” The scale uses a five-point Likert scale scoring system that ranges from 1 ( completely disagree ) to 5 ( completely agree ), with a high total score indicating a high level of time efficacy. In this study, the Cronbach’s alpha coefficient of the scale is 0.87. 3.2.4 Academic anxiety scale The academic anxiety scale for college students revised by Zhao, S. Y., & Cai, T. S. (2012) was used in this study. The scale consists of seven items, such as “I feel nervous and uneasy before exams.” The scale employs a five-point Likert scale scoring system that ranges from 1 ( completely disagree ) to 5 ( completely agree ), with a high total score indicating a high level of academic anxiety. In this study, the Cronbach’s α coefficient is 0.85. 3.2.5 Nonreactivity scale The nonreactivity scale from the Chinese version of the mindfulness five-factor questionnaire revised by Deng (2011) was used in this study. The scale is composed of seven items, such as “In difficult situations, I will pause for a moment and not respond immediately.” The scale uses a five-point Likert scale scoring method that ranges from 1 ( completely disagree ) to 5 ( completely agree ), with a high total score indicating a high level of nonreactivity. In this study, the Cronbach’s α coefficient of the scale is 0.88. 3.3 Data analysis Descriptive statistics were employed to describe the collected data by using SPSS 24.0, followed by Pearson correlation analysis. PROCESS macro Model 6, which was developed by Igartua & Hayes (2021), was used to test the chain mediating effect; Model 87 was used to test the moderating effect; and the bias-corrected percentile bootstrap method (repeated sampling 5,000 times) was used to test the 95% confidence intervals. 4. Results 4.1 Common-method variance test The data from the questionnaire survey may contain common-method bias. In this study, common-method bias was detected by performing Harman’s single-factor test（Miguel et al., 2019）. The test results show that 10 factors have an eigenvalue that exceeds 1, and the variation contributed by the first factor accounts for only 29.52%, which is below the threshold of 40.00%. Therefore, the data in this study are not significantly affected by common-method bias. 4.2 Descriptive statistics and correlation analysis Pearson correlation analysis was conducted on the variables, and the results are shown in Table 1, where AIET demonstrates a significant negative correlation with negative low arousal and academic anxiety ( r =-0.45, -0.38, P<0.001) but a significant positive correlation ( r =0.46, P <0.001) with time efficacy. Moreover, negative low arousal exhibits a significant negative correlation with time efficacy ( r =-0.51, P <0.001) but a significant positive correlation with academic anxiety ( r =0.44, P <0.001). Furthermore, time efficacy demonstrates a significant negative correlation with academic anxiety ( r =-0.67, P <0.001). Table 1. Means, standard deviations, and correlations Variable 1 2 3 4 5 6 7 1. Gender 1 2. Place of origin 0.14** 1 3. Only child status 0.25*** 0.41*** 1 4. AIET 26.04±4.65 0.17** 0.11* 0.16** 1 5. Negative low arousal 15.13±5.37 -0.17** -0.20*** -0.22*** -0.45*** 1 6. Time efficacy 22.11±5.13 -0.10* 0.14** 0.12* 0.46*** -0.51*** 1 7. Academic anxiety 20.30±5.61 -0.03 -0.11* -0.10* -0.38*** 0.44*** -0.67*** 1 Note: *P<0.05, **P<0.01, ***P<0.001 4.3 Analysis of chain mediating effect Standardized operations were performed on the collected data, and PROCESS Model 6 was employed to evaluate the chain mediating effect. In the analysis, gender, place of origin, and only child status were used as control variables to examine the mediating effect of negative low arousal and time efficacy on the role of AIET in academic anxiety（Gillen-O’Neel et al., 2011; Fernandez et al., 2023）. Repeated sampling was conducted 5,000 times on the standardized data from the 419 questionnaires by using bootstrap 95% confidence intervals, with calculated bias correction. The results of the multiple regression analysis indicate that AIET has a significant direct predictive effect on academic anxiety ( β =-0.46, P0.05). In addition, AIET has a significant negative predictive effect on negative low arousal ( β =-0.57, P<0.001), negative low arousal has a significant negative predictive effect on time efficacy ( β =-0.31, P<0.001), and time efficacy has a significant negative predictive effect on academic anxiety ( β =-0.63, P<0.001; Table 2). The mediating effect indicates that negative low arousal and time efficacy play a fully mediating role in the impact of AIET on academic anxiety. The mediating effect can be observed in three paths: (1) AIET → negative low arousal → academic anxiety, (2) AIET → time efficacy → academic anxiety, and (3) AIET → negative low arousal → time efficacy → academic anxiety. The 95% confidence intervals of the three paths do not include 0; thus, the mediating effect is significant (Table 3). Table 2. Analysis of chain mediating effect on relationship between AIET and academic anxiety Regression Index Coefficient and significance Outcome Predictors R R² F β t Academic anxiety 0.39 0.16 18.89*** Gender 0.83 1.17 Place of origin -0.73 -1.27 Only child status -0.41 -0.53 AIET -0.46 -8.25*** Negative low arousal 0.49 0.24 32.49*** Gender -1.01 -1.32 Place of origin -1.39 -2.25* Only child status -1.66 -2.00* AIET -0.57 -9.43*** Time efficacy 0.57 0.33 40.18*** Gender -0.18 -0.31 Place of origin 0.46 0.98 Only child status -0.39 -0.63 AIET 0.31 6.20*** Negative low arousal -0.31 -8.34*** Academic anxiety 0.68 0.46 59.19*** Gender 1.01 1.78 Place of origin -0.03 -0.06 Only child status -0.17 -0.28 AIET -0.10 -1.91 Negative low arousal 0.10 2.44* Time efficacy -0.63 -13.13*** Note: * P <0.05, *** P <0.001 Table 3. Mediating effects Estimate SE 95% CI Percentage Items Lower Upper Total indirect effect -0.37 0.04 -0.44 -0.29 AIET → negative low arousal → academic anxiety -0.06 0.02 -0.10 -0.01 16.22% AIET → time efficacy → academic anxiety -0.20 0.03 -0.26 -0.14 54.05% AIET → negative low arousal → time efficacy → academic anxiety -0.11 0.02 -0.15 -0.08 29.73% 4.4 Moderating effect analysis The data collected in this study were standardized and analyzed to determine their moderating effect by using PROCESS Model 87. The moderating effect of nonreactivity was examined while controlling for gender, place of origin, and only child status（Galla et al., 2020; Royuela-Colomer & Calvete, 2016）. The results show that the interaction between time efficacy and nonreactivity is significant ( β =0.07, P<0.05), which indicates that nonreactivity moderates the relationship between time efficacy and academic anxiety (Table 4). A simple slope test was conducted to further explore how nonreactivity can regulate the relationship between time efficacy and academic anxiety, with the critical value being the mean plus or minus one standard deviation. The participants were divided into a high- and low-nonreactivity group, and an interaction diagram was created (Figure 2). In the low-nonreactivity group (M-1 SD), time efficacy has a strong negative predictive effect on academic anxiety ( B simple =-0.63, t =-12.91, P <0.001). However, in the high-nonreactivity group (M+1 SD), the negative predictive effect of time efficacy on academic anxiety is weak ( B simple =-0.50, t =-9.16, P< 0.001). A comparison of the absolute values of the simple slopes shows that, as the nonreactivity level increases, the chain mediating effect of negative low arousal and time efficacy on the relationship between AIET and academic anxiety decreases (Table 5). Table 4. Analysis of moderated mediating effect on relationship between AIET and academic anxiety Independent variable Equation 1 Equation 2 Equation 3 Negative low arousal Time efficacy Academic anxiety β SE t β SE t β SE t Gender -0.16 0.12 -1.32 -0.04 0.11 -0.31 0.21 0.10 2.07* Place of origin -0.22 0.09 -2.25* 0.09 0.09 1.01 0.01 0.08 0.12 Only child status -0.26 0.10 -2.00* -0.08 0.12 -0.63 -0.02 0.11 -0.17 AIET -0.42 0.05 -9.43*** 0.28 0.05 6.21*** -0.07 0.05 -1.51 Negative low arousal -0.39 0.05 -8.34*** 0.12 0.04 2.59* Time efficacy -0.57 0.04 -12.69*** Nonreactivity 0.01 0.03 0.08 Time – efficacy × nonreactivity 0.07 0.02 2.55* R² 0.24 0.33 0.47 F 32.49*** 40.18*** 45.68*** Note: * P <0.05, *** P <0.001 Table 5. Chain mediating effect at different nonreactivity levels Estimate SE 95% CI Moderating variable Lower Upper Nonreactivity M – 1 SD -0.10 0.02 -0.14 -0.07 M -0.09 0.02 -0.13 -0.06 M + 1 SD -0.08 0.02 -0.12 -0.05 5. Discussion This study finds that AIET can have a direct negative impact on academic anxiety, which supports H1. The finding indicates that, the more an individual trusts the computational ability, output results, decision making, and judgment of an intelligent agent during their use, the more their trust will help them use the intelligent agent correctly and avoid negative academic emotions. According to control value theory, academic anxiety arises from students’ inability to control and complete their learning tasks, which may result in a low sense of achievement and value in their studies（Pekrun et al., 2010）. Meanwhile, social capital theory holds that trust is an important component of individual social capital, and those with high social capital can obtain considerable environmental information and social support, which can help them solve their problems and lessen their emotional reactions to stress（Glaeser et al., 1999）. Empirical research confirmed the close relationship between trust and anxiety. For example, researchers found a significant correlation between interpersonal relationships and communication anxiety among college students（Hawken, L. et al., 1991; Sun, 2023; Xiang et al, 2024）, and Nikumb (2009) determined that doctor–patient trust can effectively alleviate patients’ preoperative anxiety, which indicates that trust can lessen the emergence of negative emotions in individuals. Therefore, students’ AIET can improve their efficiency in using AI technology, which can help them complete their learning tasks, reduce their learning pressure, and avoid negative learning emotions. A mediation model can be established to explain the impact of AIET on academic anxiety. The data on the mediating effects prove that negative low arousal and time efficacy are mediating variables in the indirect effect of AIET on academic anxiety and thus support H2 and H3. The results show that, first, negative low arousal plays a mediating role in the impact of AIET on academic anxiety. Individuals with a low level of AIET will tend to use traditional learning methods when faced with academic pressure. When they are unable to complete their academic tasks on time, such individuals will likely adopt negative coping strategies and experience a negative low arousal emotional state. If the state persists over a long period of time, it may worsen and become a severe negative academic emotion or learning behavior, such as academic anxiety, depression, or academic procrastination. The stress-coping model suggests that emotional regulation can help individuals cope with life stress（Wills, 1988）. When individuals are exposed to external stressors and develop/gain incorrect personal cognition/experience, they will be prone to negative emotions or improper behavior. Second, time efficacy plays a mediating role in the impact of AIET on academic anxiety. Individuals with a high level of AIET will be inclined to use intelligent agents to solve their learning and life problems. Individuals’ use of AI technology can improve their learning and work efficiency, which may enhance their perception of their time use efficiency. The use of intelligent agents can also change individuals’ time use and management, which can enhance their sense of time efficacy. Planning and the reasonable use of their time can help students complete their learning tasks, reduce their learning pressure, and avoid negative emotions（Boekaerts, 1993）. Self-efficacy theory suggests that encouragement, support, and evaluation from external sources can enhance an individual’s self-efficacy, which can reduce their stress and anxiety, and help them cope with challenges effectively（Bandura, 1977）. Moreover, this study finds that negative low arousal and time efficacy exert a chain mediating effect; thus, H4 is supported. Individuals’ lack of motivation and focus can lead to low efficiency in performing learning tasks, which can reduce their confidence in their time management ability（Schunk, 1995）. Individuals in a negative low arousal state will find setting clear goals difficult or that the goals they set are not challenging. Such individuals tend to delay the start or completion of tasks, which can affect their time use perception and further weaken their sense of time management efficacy. Theory of growth mindset proposes that an individual’s psychological state can be divided into a fixed mindset and a growth mindset（Yeager & Dweck, 2020）. People with a fixed mindset firmly believe that their talents are fixed and unchanging, whereas those with a growth mindset believe that their potential can be developed and improved continuously through unremitting effort and learning. This indicates that individuals with a high level of emotional trust in intelligent agents are inclined to use AI technology to complete their learning tasks, alleviate their learning pressure, avoid being in a negative low arousal state, improve their learning time efficacy, use their learning time reasonably to complete learning tasks, and avoid learning anxiety. This study also determines that nonreactivity can moderate the relationship between time efficacy and academic anxiety; thus, H5 is supported. Cognitive evaluation theory suggests that individuals are influenced by emotional stimuli in their environment, then make primary and secondary evaluations（Watson & Spence, 2007）. When conducting a secondary evaluation, individuals will develop coping strategies that can affect their emotional experience. Specifically, those with a high level of nonreactivity will adopt proactive coping strategies to alleviate the impact of their time efficacy on their academic anxiety, which indicates that nonreactivity can reduce the risk of academic anxiety. The finding can be explained as follows: First, an individual’s high level of nonreactivity can have a positive impact on their time efficacy by increasing their self-awareness, focus, and efficiency; improving their decision-making ability; and enhancing their emotional regulation and self-regulation. For example, through mindfulness group counseling, some scholars increased the nonreactivity level of high school students and thus improved their time management tendency（Wisner, 2013）. Second, nonreactivity can improve students’ learning efficiency, because it can enable them to focus on their studies. Nonreactivity can help individuals effectively manage their emotions and improve their attention and self-acceptance, which can reduce their academic anxiety（Abedi et al., 2023）. Some studies found a significant negative correlation between school students’ level of mindfulness and anxiety（Li et al., 2025）. Moreover, some scholars used the mindfulness perception technique in acceptance commitment therapy to encourage the participants to accept their negative emotions and reduce their tendency to experience academic anxiety（Dousti et al., 2015; Hjeltnes et al., 2015）. 6. Limitations and suggestions This study has some limitations. First, this work is a cross-sectional study; thus, it cannot reflect changes in the participants over time and space and does not consider their historical data, making evaluating their situation comprehensively impossible. Horizontal research can provide data at a specific point in time; however, it cannot capture dynamic relationships between variables over time and space. Correlations between variables cannot determine whether relationships will remain stable or change over time. Second, other factors can affect the relationship between AIET and academic anxiety. Future research should investigate whether other variables can affect the mechanism of action of the relationship between the two factors and provide methods and ideas for preventing college students from experiencing academic anxiety. 7. Conclusion (1) AIET exhibits a significant negative correlation with negative low arousal and academic anxiety but a significant positive correlation with time efficacy. (2) AIET can directly predict academic anxiety and indirectly predict such anxiety through the chain mediating effect of negative low arousal and time efficacy. (3) In the chain mediation, nonreactivity can have a moderating effect on the relationship between time efficacy and academic anxiety. That is, compared with the low-nonreactivity group, the negative impact of time efficacy on academic anxiety is weaker in the high-nonreactivity group. Declarations The Ethics Committee of the School of Accounting at Guangzhou Huashang College (No. IRB-SURV-2024-01) approved the study. All the respondents participated voluntarily, and informed consent was obtained before the investigation. All subjects agreed to participate in this investigation and the data confidentiality and security. Funding Guangzhou Huashang College On-campus Research Mentorship Program, Guangdong, China (Grant No.2023HSDS23); Guangzhou Huashang College Research Backbone Talent Program , Guangdong, China (Grant No. 2025HSGG10) Ethics statement The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Human Participants Ethics Committee of Guangzhou Huashang College, China. CRediT authorship contribution statement Chao Zhang: Writing -review & editing, Writing-original draft, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. Na Xu: Writing- review & editing, Supervision, Resources, Investigation. Declaration of competing interest The authors declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article. 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European Journal of Psychological Assessment, 32(3), 241–250. https://doi.org/10.1027/1015-5759/a000251 Iani, L., Lauriola, M., Chiesa, A. et al (2019). Associations Between Mindfulness and Emotion Regulation: the Key Role of Describing and Nonreactivity. Mindfulness 10(4), 366-375 . https://doi.org/10.1007/s12671-018-0981-5 Igartua J. J., & Hayes A.F. (2021). Mediation, Moderation, and Conditional Process Analysis: Concepts, Computations, and Some Common Confusions. The Spanish Journal of Psychology. 24(6), 49-59. https://doi:10.1017/SJP.2021.46 Keng, S. L., & Tong, E. M. W. (2016). Riding the tide of emotions with mindfulness: Mindfulness, affect dynamics, and the mediating role of coping. Emotion, 16(5), 706-718. https://doi.org/10.1037/emo0000165 Låftman, S.B., Raninen, J. & Östberg, V (2024). Trust in adolescence and depression and anxiety symptoms in young adulthood: findings from a Swedish cohort. BMC Res Notes 17(7), 66-76 . https://doi.org/10.1186/s13104-023-06667-7 Li, T., Zhou, D., Zhang, W. and Ju, C. (2025), The Relationship Between Mindfulness and Test Anxiety Among High School Students: The Chain Mediating Role of Emotion Regulation and Psychological Resilience. Psychology in the Schools, 10(2), 210-221. https://doi.org/10.1002/pits.70074 Liu, J. H. (2024). Research on the impact of artificial intelligence trust on human-machine collaborative decision-making quality: From the perspective of human-machine knowledge transfer and knowledge integration (Master’s thesis) Hangzhou Dianzi University. https://doi.org/10.27075/d.cnki.ghzdc.2024.000005 Maddux, J.E., Stanley, M.A., Manning, M.M. (1987). Self-Efficacy Theory and Research: Applications in Clinical and Counseling Psychology. Springer, New York, 20(7), 350-361. https://doi.org/10.1007/978-1-4613-8728-2_4 Maslej, N., Fattorini, L., Perrault, & R., Gil, Y.. (2025). Artificial Intelligence Index Report 2025 (No. arXiv:2504.07139). arXiv. https://doi.org/10.48550/arXiv.2504.07139 McAllister, D. J. (1995). Affect- and Cognition-Based Trust as Foundations for Interpersonal Cooperation in Organizations. Academy of Management Journal, 38(1), 24–59. https://doi.org/10.5465/256727 Mesurado, B., Vidal, E. M., & Mestre, A. L. (2018). Negative emotions and behaviour: The role of regulatory emotional self-efficacy. Journal of Adolescence, 64(2), 62-71. https://doi.org/10.1016/j.adolescence.2018.01.007 Middel, B. (2008). The impact of electronic health records on time efficiency of physicians and nurses: a systematic review1) . NTEB 6(7), 14–16. https://doi.org/10.1007/BF03077155 Miguel, I., Aguirre, U., & Jiang, H. (2019). Detecting Common Method Bias: Performance of the Harman's Single-Factor Test. SIGMIS Database , 50(2), 45-70. https://doi.org/10.1145/3330472.3330477 Nikumb, Vandana B., Banerjee, Amitav, Kaur, Gurleen, Chaudhury, Suprakash1(2009). Impact of doctor-patient communication on preoperative anxiety: Study at industrial township, Pimpri, Pune. Industrial Psychiatry Journal 18(1), 19-21. https://doi.org/10.4103/0972-6748.57852 Payne, A. L., Stone, C., & Bennett, R. (2022). Conceptualising and Building Trust to Enhance the Engagement and Achievement of Under-Served Students. The Journal of Continuing Higher Education, 71(2), 134–151. https://doi.org/10.1080/07377363.2021.2005759 Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic Emotions in Students’ Self-Regulated Learning and Achievement: A Program of Qualitative and Quantitative Research. Educational Psychologist, 37(2), 91–105. https://doi.org/10.1207/S15326985EP3702_4 Pekrun, R., Goetz, T., Daniels, L. M., Stupnisky, R. H., & Perry, R. P. (2010). Boredom in achievement settings: exploring control-value antecedents and performance outcomes of a neglected emotions. Journal of Educational Psychology, 102(3), 531-549. https://doi.org/10.1037/emo0000158 Pham Thi, T., & Duong, N. (2024). Investigating learning burnout and academic performance among management students: a longitudinal study in English courses. BMC Psychol 12(5), 219-230 . https://doi.org/10.1186/s40359-024-01725-6 Riley, B. K. & Dixon, A. (2024). Emotional and cognitive trust in artificial intelligence: A framework for identifying research opportunities. Current Opinion in Psychology, 58(5), 101-113. https://doi.org/10.1016/j.copsyc.2024.101833 Royuela-Colomer, E., Calvete, E . (2016). Mindfulness Facets and Depression in Adolescents: Rumination as a Mediator. Mindfulness 7(3), 1092-1102. https://doi.org/10.1007/s12671-016-0547-3 Schunk, D. H. (1995). Self-efficacy, motivation, and performance. Journal of Applied Sport Psychology, 7(2), 112–137. https://doi.org/10.1080/10413209508406961 Song, Z., & Zhao, W. (2025). Analysis of the role of self-efficacy and interpersonal relationships in the relationship between subjective family socioeconomic status and college students’ trust character: a case study of a university in Shaanxi Province, China. Sci Rep, 15(9), 11543-11545. https://doi.org/10.1038/s41598-025-96358-z Sun, Y. (2023). The Relationship Between College Students’ Interpersonal Relationship and Mental Health: Multiple Mediating Effect of Safety Awareness and College Planning. Psychology Research and Behavior Management, 16(5), 261-270. https://doi.org/10.2147/PRBM.S396301 Véliz, C., Prunkl, C., Phillips-Brown, M., & Lechterman, T. M. (2021). We might be afraid of black-box algorithms. Journal of Medical Ethics, 47(5), 339–340. https://doi.org/10.1136/medethics-2021-107462 Voelker, R. (2000). Academic Anxiety. JAMA, 284(12), 1506–1506. https://doi.org/10.1001/jama.284.12.1506 Wang, Q., Kou, Z., Du, Y., Wang, K., & Xu, Y. (2022). Academic Procrastination and Negative Emotions Among Adolescents During the COVID-19 Pandemic: The Mediating and Buffering Effects of Online-Shopping Addiction. Frontiers in Psychology, 12(10), 101-111. https://doi.org/10.3389/fpsyg.2021.789505 Watson, L., Spence, M. T. (2007), Causes and consequences of emotions on consumer behaviour: A review and integrative cognitive appraisal theory. European Journal of Marketing, 41 (5) , 487–511. https://doi.org/10.1108/03090560710737570 Wills, T. A. (1988). Stress and Coping in Early Adolescence: Relationships to Substance Use in Urban School Samples. In Child Health Psychology. Psychology Press. Wisner, B. L., & Norton, C. L. (2013). Capitalizing on Behavioral and Emotional Strengths of Alternative High School Students Through Group Counseling to Promote Mindfulness Skills. The Journal for Specialists in Group Work, 38(3), 207–224. https://doi.org/10.1080/01933922.2013.803504 Xiang, J., Gao, J., & Gao, Y. (2024). The effect of subjective exercise experience on anxiety disorder in university freshmen: The chain-mediated role of self-efficacy and interpersonal relationship. Frontiers in Psychology, 15(4). https://doi.org/10.3389/fpsyg.2024.1292203 Yeager, D. S., & Dweck, C. S. (2020). What can be learned from growth mindset controversies? American Psychologist, 75(9), 1269–1284. https://doi.org/10.1037/amp0000794 Zhao, S. Y., & Cai, T. S. (2012). Revision of the Academic Emotions Questionnaire (AEQ) Chinese Version in Chinese college students. Chinese Journal of Clinical Psychology, 20(4), 448–450+447. https://doi.org/10.16128/j.cnki.1005-3611.2012.04.024 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Introduction","content":"\u003cp\u003eIn 2024, the scale of the global artificial intelligence (AI) core industry reached US \u003cspan\u003e$\u003c/span\u003e864.3\u0026nbsp;billion, an increase of 22.1%, over US \u003cspan\u003e$\u003c/span\u003e707.8\u0026nbsp;billion in 2023, and the average annual growth rate in the past four years (2021\u0026ndash;2024) was 21.5%. The industrial chain runs through the entire system, from the bottom technology link to the upper application link, including semiconductor chips, algorithm research and development, data acquisition and processing, platform construction, and industrial application. In addition, thanks tothe integration of large model, quantum computing and intelligent computing, generative AI is widely used. In 2025, the scale of the global AI core industry will exceed 1trillion dollars(Maslej et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAI technology has economic benefits for social development and can exert a profound impact on college education. For example, generative AI, which is represented by ChatGPT, has become an important tool for college students\u0026rsquo; daily learning. The large AI language model can not only generate text, pictures, and videos, based on college student users\u0026rsquo; needs, but also improve their learning experience and make academic research highly interesting. However, the AI technology \u0026ldquo;algorithm black box\u0026rdquo; bottleneck has prevented users from understanding the basis of intelligent system decision making on a timely basis, which may cause them to doubt the output (Dur\u0026aacute;n \u0026amp; Jongsma, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; V\u0026eacute;liz et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, human beings\u0026rsquo; trust in intelligent agents can exert an important impact on their learning. Previous studies focused mainly on the impact of interpersonal trust on academic achievement, academic efficacy, and academic engagement and did not examine the impact of people\u0026rsquo;s trust in machines on academic emotions, such as academic anxiety and academic burnout (Payne et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Benlahcene et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Based on the new era of AI technology, this study explores how college students\u0026rsquo; trust in intelligent agents can affect their academic anxiety to increase their interest in learning and enhance their learning quality and provide a reference for educators\u0026rsquo; teaching.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003e\u003cstrong\u003e2.1 Artificial intelligence emotional trust and academic anxiety\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcademic anxiety refers to the emotional state of excessive tension, worry, and fear in the face of academic tasks or competition (Voelker, 2000). Meanwhile, AIET refers to an individual\u0026rsquo;s internal belief in an intelligent agent in the process of human\u0026ndash;computer interaction. Such a belief is less affected by the objective world and more dependent on the personal feelings of the individual and the intuition of the intelligent agent (Riley, 2024). According to theory of interpersonal relationships, emotional trust is formed through social interaction with others and expressed as confidence in others\u0026rsquo; ability to benefit oneself (McAllister, 1995). Emotional trust is perceptual in appearance and focuses on one\u0026rsquo;s trust in others\u0026rsquo; intentions and emotions. Previous studies found a close relationship between trust and anxiety (AlRuthia et al, 2020; L\u0026aring;ftman et al, 2024). However, other studies viewed trust and anxiety as two opposing concepts; that is, anxiety may arise from lack of trust. Such studies showed that the higher the emotional trust of college students in AI technology, the lower their degree of academic burnout, and the lower their probability of experiencing academic anxiety. Therefore, this study proposes the following hypothesis:\u003c/p\u003e\n\u003cp\u003eHypothesis 1 (H1): AIET can affect academic anxiety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Mediating role of negative low arousal and time efficacy in the relationship between AIET and academic anxiety\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to directly affecting academic anxiety, AIET may indirectly influence academic anxiety through negative low arousal. Negative low arousal refers to an individual\u0026rsquo;s negative emotions and relatively low overall arousal level (including physiological activation and psychological excitement), which is manifested mainly as boredom, helplessness, and depression (Govaerts \u0026amp; Gr\u0026eacute;goire, 2008). Such a state may lead to slowed behavior, reduced social and activity participation, and an overall decline in the quality of life. Social support theory posits that trust is an important component of social support and plays a pivotal role in establishing and maintaining social relationships and providing support (Gottlieb \u0026amp; Bergen, 2010). Previous studies found that the more the social support received by students on campus, the easier the mobilization of their positive emotions(Hirsch \u0026amp; Barton, 2011; Balk et al, 1993 ). Meanwhile, learning emotion theory suggests a significant correlation between negative low arousal and negative high arousal emotions（Pekrun et al., 2002）. Persistent negative low arousal may lead to emotional issues, which may trigger strong negative emotions under certain conditions and transition from negative low arousal to negative high arousal. For example, long-term boredom and depression may transform into intense anger or anxiety from a certain event. Some studies on academic emotions among college students observed a negative pattern; that is, when students experience burnout and lose interest in learning during the learning process, they may feel exhausted and develop anxiety toward learning（Pham Thi \u0026amp; Duong, 2024）. Therefore, this study proposes the following hypothesis:\u003c/p\u003e\n\u003cp\u003eHypothesis 2 (H2): Negative low arousal is a mediating variable in the relationship between AIET and academic anxiety.\u003c/p\u003e\n\u003cp\u003eMoreover, AIET may indirectly affect academic anxiety through time efficacy. Time efficacy refers to an individual\u0026rsquo;s confidence and belief in their ability to manage their time（Middel, 2008）. Interpersonal trust may exert a positive impact on self-efficacy (Song \u0026amp; Zhao, 2025), because trust can create a positive and supportive atmosphere that can help individuals remain in a happy and optimistic psychological state. Such a positive psychological state can help individuals cope effectively with stress and challenges and thus increase their sense of self-efficacy. However, time efficacy can have a negative impact on academic anxiety. Social cognitive theory suggests that self-efficacy can affect an individual\u0026rsquo;s emotions through cognitive, motivational, and affective factors（Maddux et al., 1987）. Previous studies found that time efficacy can have a negative impact on negative emotions（Mesurado et al., 2018）. That is, individuals who exhibit high time efficacy can typically cope with time uncertainty, demonstrate stable emotions, and are less susceptible to external factors. In addition, individuals who exhibit a high sense of time efficacy tend to adopt positive coping strategies and use of their time reasonably to solve their problems when faced with difficulties and challenges, which may reduce their likelihood of experiencing negative emotions. Therefore, this study proposes the following hypothesis:\u003c/p\u003e\n\u003cp\u003eHypothesis 3 (H3): Time efficacy is a mediating variable in the relationship between AIET and academic anxiety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Chain mediating effect of negative low arousal and time efficacy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf negative low arousal and time efficacy can mediate the relationship between AIET and academic anxiety, then what is the relationship between negative low arousal and time efficacy? When individuals are in a state of negative low arousal, they will lack interest in learning tasks, which can lead to scattered attention and difficulty concentrating to complete such tasks. Such an inefficient state can directly affect individuals\u0026rsquo; time efficacy and render them unable to complete expected tasks within a limited amount of time. For example, Hirvonen et al.（2020）\u0026nbsp;found that negative low arousal in primary school students can significantly predict their learning efficacy. At the same time, individuals who are in a state of negative low arousal may lack the motivation to develop and implement effective time management strategies and may be unable to plan their time reasonably, set priorities, or use time-planning tools to improve their work efficiency. Such a decline in an individual\u0026rsquo;s time management ability will further reduce their time efficacy. Furthermore, researchers observed that negative low arousal can positively affect academic procrastination among middle school students（Cao et al., 2025; Wang et al., 2025）. Therefore, this study proposes the following hypothesis:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHypothesis 4 (H4): Negative low arousal and time efficacy will have a chain mediating effect in the relationship between AIET and academic anxiety .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Moderating effect of nonreactivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the process of the indirect effect of AIET on academic anxiety, moderating variables may affect the mediating effect on the relationship between the factors, such as nonreactivity. Nonreactivity is a coping style that involves not immediately exhibiting a habitual response in the face of external stimuli and an important element of individual mindfulness, which is a positive psychological quality（Iani et al., 2019）. Individuals can effectively manage and regulate their emotions through mindfulness and thus reduce the impact of emotional fluctuations and negative emotions. Previous studies reported that individuals\u0026rsquo; level of mindfulness is significantly negatively correlated with their negative emotions (e.g., anxiety, depression, or anger)（Hill \u0026amp; Updegraff, 2012; Keng \u0026amp; Tong, 2016）. Individuals who demonstrate high time efficacy can adopt a positive coping style to adjust their emotions and reduce the impact of negative emotions in the face of learning pressure. Therefore, this study proposes the following hypothesis:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHypothesis 5 (H5): Nonreactivity can regulate the relationship between time efficacy and academic anxiety in the chain mediation.\u003c/p\u003e\n\u003cp\u003eIn summary, though previous studies observed the impact of interpersonal trust on college students\u0026rsquo; negative emotions, research has yet to determine whether human trust in intelligent agents can affect individuals\u0026rsquo; negative emotions, as well as the role of negative low arousal and time efficacy as a mediator and that of nonreactivity as a moderator. The hypothesis model is presented in Figure 1.\u003c/p\u003e"},{"header":"3. Methods","content":"\u003cp\u003e\u003cstrong\u003e3.1 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 450 college students in Guangdong Province, China were invited through random sampling to participate in this study. After 31 invalid questionnaires (questionnaires with regular answers) were excluded, the collected questionnaires totaled 419 (for a recovery rate of 93.11%). Among the participants who provided valid responses, 70 were male and 349 were female. Moreover, 163 participants were from an urban area, and 256 participants were from a rural area. In addition, among the participants, 69 were only children and 350 were non-only children. The average age of the participants was 20.93 years, with a standard deviation of 2.08 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.2.1 Artificial intelligence emotional trust scale\u003c/p\u003e\n\u003cp\u003eThe AIET scale revised by Liu, J. H.(2024), which is composed of six items, was used in this study. A sample item is, \u0026ldquo;When the task is difficult, I think I can rely on artificial intelligence.\u0026rdquo; The scale uses a seven-point Likert scale scoring method that ranges from 1 (\u003cem\u003ecompletely disagree\u003c/em\u003e) to 7 (\u003cem\u003ecompletely agree\u003c/em\u003e), and the higher the total score, the higher the AIET level. In this study, the Cronbach\u0026rsquo;s \u0026alpha; coefficient of the scale is 0.85.\u003c/p\u003e\n\u003cp\u003e3.2.2 Negative low arousal scale\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe negative low arousal scale from the Multidimensional State Boredom Questionnaire, edited by Hunter et al. (2016), was used in this study. The scale is composed of five items, such as \u0026ldquo;For me, everything is repetitive and boring.\u0026rdquo; The scale employs a seven-point Likert scale scoring method that ranges from 1 (\u003cem\u003ecompletely disagree\u003c/em\u003e) to 7 (\u003cem\u003ecompletely agree\u003c/em\u003e), with a high total score indicating a high level of negative low arousal. In this study, the Cronbach\u0026rsquo;s \u0026alpha; coefficient of the scale is 0.86.\u003c/p\u003e\n\u003cp\u003e3.2.3 Time efficacy scale\u003c/p\u003e\n\u003cp\u003eThe time efficacy scale in the college student learning time management strategy questionnaire revised by Chen, L. (2012) was used in this study. The scale consists of seven items, such as \u0026ldquo;I often have no deadline for completing my own work.\u0026rdquo; The scale uses a five-point Likert scale scoring system that ranges from 1 (\u003cem\u003ecompletely disagree\u003c/em\u003e) to 5 (\u003cem\u003ecompletely agree\u003c/em\u003e), with a high total score indicating a high level of time efficacy. In this study, the Cronbach\u0026rsquo;s alpha coefficient of the scale is 0.87.\u003c/p\u003e\n\u003cp\u003e3.2.4 Academic anxiety scale\u003c/p\u003e\n\u003cp\u003eThe academic anxiety scale for college students revised by Zhao, S. Y., \u0026amp; Cai, T. S. (2012) was used in this study. The scale consists of seven items, such as \u0026ldquo;I feel nervous and uneasy before exams.\u0026rdquo; The scale employs a five-point Likert scale scoring system that ranges from 1 (\u003cem\u003ecompletely disagree\u003c/em\u003e) to 5 (\u003cem\u003ecompletely agree\u003c/em\u003e), with a high total score indicating a high level of academic anxiety. In this study, the Cronbach\u0026rsquo;s \u0026alpha; coefficient is 0.85.\u003c/p\u003e\n\u003cp\u003e3.2.5 Nonreactivity scale\u003c/p\u003e\n\u003cp\u003eThe nonreactivity scale from the Chinese version of the mindfulness five-factor questionnaire revised by Deng (2011) was used in this study. The scale is composed of seven items, such as \u0026ldquo;In difficult situations, I will pause for a moment and not respond immediately.\u0026rdquo; The scale uses a five-point Likert scale scoring method that ranges from 1 (\u003cem\u003ecompletely disagree\u003c/em\u003e) to 5 (\u003cem\u003ecompletely agree\u003c/em\u003e), with a high total score indicating a high level of nonreactivity. In this study, the Cronbach\u0026rsquo;s \u0026alpha; coefficient of the scale is 0.88.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were employed to describe the collected data by using SPSS 24.0, followed by Pearson correlation analysis. PROCESS macro Model 6, which was developed by Igartua \u0026amp; Hayes (2021), was used to test the chain mediating effect; Model 87 was used to test the moderating effect; and the bias-corrected percentile bootstrap method (repeated sampling 5,000 times) was used to test the 95% confidence intervals.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003e\u003cstrong\u003e4.1 Common-method variance test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data from the questionnaire survey may contain common-method bias. In this study, common-method bias was detected by performing Harman\u0026rsquo;s single-factor test（Miguel et al., 2019）. The test results show that 10 factors have an eigenvalue that exceeds 1, and the variation contributed by the first factor accounts for only 29.52%, which is below the threshold of 40.00%. Therefore, the data in this study are not significantly affected by common-method bias.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Descriptive statistics and correlation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson correlation analysis was conducted on the variables, and the results are shown in Table 1, where AIET demonstrates a significant negative correlation with negative low arousal and academic anxiety (\u003cem\u003er\u003c/em\u003e=-0.45, -0.38, P\u0026lt;0.001) but a significant positive correlation (\u003cem\u003er\u003c/em\u003e=0.46, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) with time efficacy. Moreover, negative low arousal exhibits a significant negative correlation with time efficacy (\u003cem\u003er\u003c/em\u003e=-0.51, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) but a significant positive correlation with academic anxiety (\u003cem\u003er\u003c/em\u003e=0.44, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Furthermore, time efficacy demonstrates a significant negative correlation with academic anxiety (\u003cem\u003er\u003c/em\u003e=-0.67, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Means, standard deviations, and correlations\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cimg width=\"32\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1770958551.png\" alt=\"image\" height=\"19\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1.\u0026nbsp;Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2.\u0026nbsp;Place of origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3.\u0026nbsp;Only child status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.41***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4.\u0026nbsp;AIET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; 26.04\u0026plusmn;4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5.\u0026nbsp;Negative low arousal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.13\u0026plusmn;5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.20***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.22***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.45***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e6.\u0026nbsp;Time efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.11\u0026plusmn;5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.10*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.46***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.51***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e7. Academic anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.30\u0026plusmn;5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.10*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.38***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.44***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.67***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Analysis of chain mediating effect\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStandardized operations were performed on the collected data, and PROCESS Model 6 was employed to evaluate the chain mediating effect. In the analysis, gender, place of origin, and only child status were used as control variables to examine the mediating effect of negative low arousal and time efficacy on the role of AIET in academic anxiety（Gillen-O\u0026rsquo;Neel et al., 2011; Fernandez \u0026nbsp;et al., 2023）. Repeated sampling was conducted 5,000 times on the standardized data from the 419 questionnaires by using bootstrap 95% confidence intervals, with calculated bias correction. The results of the multiple regression analysis indicate that AIET has a significant direct predictive effect on academic anxiety (\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.46, P\u0026lt;0.001); however, after the mediating variable was added, the direct predictive effect became insignificant (\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.10, P\u0026gt;0.05). In addition, AIET has a significant negative predictive effect on negative low arousal (\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.57, P\u0026lt;0.001), negative low arousal has a significant negative predictive effect on time efficacy (\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.31, P\u0026lt;0.001), and time efficacy has a significant negative predictive effect on academic anxiety (\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.63, P\u0026lt;0.001; Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe mediating effect indicates that negative low arousal and time efficacy play a fully mediating role in the impact of AIET on academic anxiety. The mediating effect can be observed in three paths: (1) AIET \u0026rarr; negative low arousal \u0026rarr; academic anxiety, (2) AIET \u0026rarr; time efficacy \u0026rarr; academic anxiety, and (3) AIET \u0026rarr; negative low arousal \u0026rarr; time efficacy \u0026rarr; academic anxiety. The 95% confidence intervals of the three paths do not include 0; thus, the mediating effect is significant (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Analysis of chain mediating effect on relationship between AIET and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eacademic anxiety\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient and significance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eR\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAcademic anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18.89***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePlace of origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOnly child status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAIET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-8.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNegative low arousal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32.49***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePlace of origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-2.25*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOnly child status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-2.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAIET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-9.43***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTime efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.18***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePlace of origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOnly child status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAIET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.20***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNegative low arousal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-8.34***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAcademic anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e59.19***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePlace of origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOnly child status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAIET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNegative low arousal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.44*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTime efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-13.13***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Mediating effects\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; 95% \u003cem\u003eCI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Percentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal indirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAIET \u0026rarr; negative low arousal \u0026rarr; academic anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAIET \u0026rarr; time efficacy \u0026rarr; academic anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e54.05%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;AIET \u0026rarr; negative low arousal \u0026rarr; time efficacy \u0026rarr; academic anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Moderating effect analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data collected in this study were standardized and analyzed to determine their moderating effect by using PROCESS Model 87. The moderating effect of nonreactivity was examined while controlling for gender, place of origin, and only child status（Galla et al., 2020; Royuela-Colomer \u0026amp; Calvete, 2016）. The results show that the interaction between time efficacy and nonreactivity is significant (\u003cem\u003e\u0026beta;\u003c/em\u003e=0.07, P\u0026lt;0.05), which indicates that nonreactivity moderates the relationship between time efficacy and academic anxiety (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA simple slope test was conducted to further explore how nonreactivity can regulate the relationship between time efficacy and academic anxiety, with the critical value being the mean plus or minus one standard deviation. The participants were divided into a high- and low-nonreactivity group, and an interaction diagram was created (Figure 2). In the low-nonreactivity group (M-1 SD), time efficacy has a strong negative predictive effect on academic anxiety (\u003cem\u003eB\u003csub\u003esimple\u003c/sub\u003e\u003c/em\u003e=-0.63, \u003cem\u003et\u003c/em\u003e=-12.91, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). However, in the high-nonreactivity group (M+1 SD), the negative predictive effect of time efficacy on academic anxiety is weak (\u003cem\u003eB\u003csub\u003esimple\u003c/sub\u003e\u003c/em\u003e=-0.50,\u003cem\u003e\u0026nbsp;t\u003c/em\u003e=-9.16, \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001). A comparison of the absolute values of the simple slopes shows that, as the nonreactivity level increases, the chain mediating effect of negative low arousal and time efficacy on the relationship between AIET and academic anxiety decreases (Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Analysis of moderated mediating effect on relationship between AIET and academic anxiety\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEquation 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEquation 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEquation 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative low arousal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime efficacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic anxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePlace of origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.25*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOnly child status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAIET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-9.43***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNegative low arousal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-8.34***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.59*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTime efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-12.69***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNonreactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTime \u0026ndash; efficacy \u0026times; nonreactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.55*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e32.49***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e40.18***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e45.68***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Chain mediating effect at different nonreactivity levels\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerating variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNonreactivity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM \u0026ndash; 1 SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eM + 1 SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study finds that AIET can have a direct negative impact on academic anxiety, which supports H1. The finding indicates that, the more an individual trusts the computational ability, output results, decision making, and judgment of an intelligent agent during their use, the more their trust will help them use the intelligent agent correctly and avoid negative academic emotions. According to control value theory, academic anxiety arises from students\u0026rsquo; inability to control and complete their learning tasks, which may result in a low sense of achievement and value in their studies（Pekrun et al., 2010）. Meanwhile, social capital theory holds that trust is an important component of individual social capital, and those with high social capital can obtain considerable environmental information and social support, which can help them solve their problems and lessen their emotional reactions to stress（Glaeser et al., 1999）. Empirical research confirmed the close relationship between trust and anxiety. For example, researchers found a significant correlation between interpersonal relationships and communication anxiety among college students（Hawken, L. et al., 1991; Sun, 2023; Xiang et al, 2024）, and Nikumb (2009) determined that doctor\u0026ndash;patient trust can effectively alleviate patients\u0026rsquo; preoperative anxiety, which indicates that trust can lessen the emergence of negative emotions in individuals. Therefore, students\u0026rsquo; AIET can improve their efficiency in using AI technology, which can help them complete their learning tasks, reduce their learning pressure, and avoid negative learning emotions.\u003c/p\u003e\n\u003cp\u003eA mediation model can be established to explain the impact of AIET on academic anxiety. The data on the mediating effects prove that negative low arousal and time efficacy are mediating variables in the indirect effect of AIET on academic anxiety and thus support H2 and H3. The results show that, first, negative low arousal plays a mediating role in the impact of AIET on academic anxiety. Individuals with a low level of AIET will tend to use traditional learning methods when faced with academic pressure. When they are unable to complete their academic tasks on time, such individuals will likely adopt negative coping strategies and experience a negative low arousal emotional state. If the state persists over a long period of time, it may worsen and become a severe negative academic emotion or learning behavior, such as academic anxiety, depression, or academic procrastination. The stress-coping model suggests that emotional regulation can help individuals cope with life stress（Wills, 1988）. When individuals are exposed to external stressors and develop/gain incorrect personal cognition/experience, they will be prone to negative emotions or improper behavior. Second, time efficacy plays a mediating role in the impact of AIET on academic anxiety. Individuals with a high level of AIET will be inclined to use intelligent agents to solve their learning and life problems. Individuals\u0026rsquo; use of AI technology can improve their learning and work efficiency, which may enhance their perception of their time use efficiency. The use of intelligent agents can also change individuals\u0026rsquo; time use and management, which can enhance their sense of time efficacy. Planning and the reasonable use of their time can help students complete their learning tasks, reduce their learning pressure, and avoid negative emotions（Boekaerts, 1993）. Self-efficacy theory suggests that encouragement, support, and evaluation from external sources can enhance an individual\u0026rsquo;s self-efficacy, which can reduce their stress and anxiety, and help them cope with challenges effectively（Bandura, 1977）.\u003c/p\u003e\n\u003cp\u003eMoreover, this study finds that negative low arousal and time efficacy exert a chain mediating effect; thus, H4 is supported. Individuals\u0026rsquo; lack of motivation and focus can lead to low efficiency in performing learning tasks, which can reduce their confidence in their time management ability（Schunk, 1995）. Individuals in a negative low arousal state will find setting clear goals difficult or that the goals they set are not challenging. Such individuals tend to delay the start or completion of tasks, which can affect their time use perception and further weaken their sense of time management efficacy. Theory of growth mindset proposes that an individual\u0026rsquo;s psychological state can be divided into a fixed mindset and a growth mindset（Yeager \u0026amp; Dweck, 2020）. People with a fixed mindset firmly believe that their talents are fixed and unchanging, whereas those with a growth mindset believe that their potential can be developed and improved continuously through unremitting effort and learning. This indicates that individuals with a high level of emotional trust in intelligent agents are inclined to use AI technology to complete their learning tasks, alleviate their learning pressure, avoid being in a negative low arousal state, improve their learning time efficacy, use their learning time reasonably to complete learning tasks, and avoid learning anxiety.\u003c/p\u003e\n\u003cp\u003eThis study also determines that nonreactivity can moderate the relationship between time efficacy and academic anxiety; thus, H5 is supported. Cognitive evaluation theory suggests that individuals are influenced by emotional stimuli in their environment, then make primary and secondary evaluations（Watson \u0026amp; Spence, 2007）. When conducting a secondary evaluation, individuals will develop coping strategies that can affect their emotional experience. Specifically, those with a high level of nonreactivity will adopt proactive coping strategies to alleviate the impact of their time efficacy on their academic anxiety, which indicates that nonreactivity can reduce the risk of academic anxiety. The finding can be explained as follows: First, an individual\u0026rsquo;s high level of nonreactivity can have a positive impact on their time efficacy by increasing their self-awareness, focus, and efficiency; improving their decision-making ability; and enhancing their emotional regulation and self-regulation. For example, through mindfulness group counseling, some scholars increased the nonreactivity level of high school students and thus improved their time management tendency（Wisner, 2013）. Second, nonreactivity can improve students\u0026rsquo; learning efficiency, because it can enable them to focus on their studies. Nonreactivity can help individuals effectively manage their emotions and improve their attention and self-acceptance, which can reduce their academic anxiety（Abedi et al., 2023）. Some studies found a significant negative correlation between school students\u0026rsquo; level of mindfulness and anxiety（Li et al., 2025）. Moreover, some scholars used the mindfulness perception technique in acceptance commitment therapy to encourage the participants to accept their negative emotions and reduce their tendency to experience academic anxiety（Dousti et al., 2015; Hjeltnes et al., 2015）.\u003c/p\u003e"},{"header":"6. Limitations and suggestions","content":"\u003cp\u003eThis study has some limitations. First, this work is a cross-sectional study; thus, it cannot reflect changes in the participants over time and space and does not consider their historical data, making evaluating their situation comprehensively impossible. Horizontal research can provide data at a specific point in time; however, it cannot capture dynamic relationships between variables over time and space. Correlations between variables cannot determine whether relationships will remain stable or change over time. Second, other factors can affect the relationship between AIET and academic anxiety. Future research should investigate whether other variables can affect the mechanism of action of the relationship between the two factors and provide methods and ideas for preventing college students from experiencing academic anxiety.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003e(1) AIET exhibits a significant negative correlation with negative low arousal and academic anxiety but a significant positive correlation with time efficacy. (2) AIET can directly predict academic anxiety and indirectly predict such anxiety through the chain mediating effect of negative low arousal and time efficacy. (3) In the chain mediation, nonreactivity can have a moderating effect on the relationship between time efficacy and academic anxiety. That is, compared with the low-nonreactivity group, the negative impact of time efficacy on academic anxiety is weaker in the high-nonreactivity group.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eThe Ethics Committee of the School of Accounting at Guangzhou Huashang College (No. IRB-SURV-2024-01) approved the study. All the respondents participated voluntarily, and informed consent was obtained before the investigation. All subjects agreed to participate in this investigation and the data confidentiality and security.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuangzhou Huashang College On-campus Research Mentorship Program, Guangdong, China (Grant No.2023HSDS23); Guangzhou Huashang College Research Backbone Talent Program , Guangdong, China (Grant No. 2025HSGG10)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Human Participants Ethics Committee of Guangzhou Huashang College, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChao Zhang: Writing -review \u0026amp; editing, Writing-original draft, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.\u003c/p\u003e\n\u003cp\u003eNa Xu: Writing- review \u0026amp; editing, Supervision, Resources, Investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbedi, E., Yousefi, E., Khajepour, L. and Jokar, S. (2023). The relationship between mindfulness and academic adjustment in students: Investigating the mediating role of academic hope emotion and academic anxiety emotion. Iranian Journal of Learning and Memory, 6(21), 68-79. doi: 10.22034/iepa.2023.391510.1415\u003c/li\u003e\n\u003cli\u003eAlRuthia, Y., Alwhaibi, M., Almalag, H., Almosabhi, L., Almuhaya, M., Sales, I., Albassam, A. A., Alharbi, F. A., Mansy, W., Bashatah, A. S., \u0026amp; Asiri, Y. (2020). 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Chinese Journal of Clinical Psychology, 20(4), 448\u0026ndash;450+447. https://doi.org/10.16128/j.cnki.1005-3611.2012.04.024\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"college student, artificial intelligence emotional trust, academic anxiety, negative low arousal, time efficacy, nonreactivity","lastPublishedDoi":"10.21203/rs.3.rs-8695381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8695381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003e- This study aims to determine the influence of artificial intelligence emotional trust (AIET) on the academic anxiety of college students.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003e- A questionnaire was used to survey a sample of 419 college students.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003e- Results show that (1) AIET is significantly negatively correlated with negative low arousal and academic anxiety (\u003cem\u003er\u003c/em\u003e=-0.45, -0.38, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) but positively correlated with time efficacy (\u003cem\u003er\u003c/em\u003e=0.46, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Moreover, negative low arousal is significantly negatively correlated with time efficacy (\u003cem\u003er\u003c/em\u003e=-0.51, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) but significantly positively correlated with academic anxiety (\u003cem\u003er\u003c/em\u003e=0.44, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). In addition, a significant negative correlation exists between time efficacy and academic anxiety (\u003cem\u003er\u003c/em\u003e=-0.67, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). (2) Negative low arousal and sense of time efficacy are chain mediators in the effect of AIET on academic anxiety. (3) In the chain mediation, nonreactivity plays a moderating role in the relationship between time efficacy and academic anxiety.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003e- This research reveals the psychological factors that can lead to academic anxiety among college students. This research is beneficial to college educators to understand the formation of unhealthy learning behaviors among college students and has reference significance to college students to help them avoid serious mental health problems.\u003c/p\u003e","manuscriptTitle":"Influence of Artificial Intelligence Emotional Trust on Academic Anxiety among College Students: A Moderated Chain Mediation Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 04:59:54","doi":"10.21203/rs.3.rs-8695381/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"12010388-d33b-495b-aaef-4b0e908aa308","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62322836,"name":"Psychology"},{"id":62322837,"name":"Educational Psychology"}],"tags":[],"updatedAt":"2026-02-13T04:59:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 04:59:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8695381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8695381","identity":"rs-8695381","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}