The Role of GenAI in EFL: Impact on Learning Motivation and Outcome

The Role of GenAI in EFL: Impact on Learning Motivation and Outcome | 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 Article The Role of GenAI in EFL: Impact on Learning Motivation and Outcome TAO WANG, Bowen Xue This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5144171/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 The purpose of this paper is to explore the role of GenAI (generative artificial intelligence) in EFL. Specifically, this paper focuses on its impact on learning motivation and outcome. With a total of five hundred and sixty-six students in four Chinese university, the questionnaire was shown to be reliable and valid. Research has revealed that GenAI has a strong effect on EFL learning motivation and outcomes in China; that GenAI plays an important role in learners’ personalized learning, learning interest and motivation through timely feedback and strong interactivity; and GenAI can enhance learner’s English listening and speaking skills, academic writing skills, vocabulary expansion and understanding of English-speaking countries' culture. The paper also revealed that personal factors such as grade, English proficiency and language environment in which students grew up have a strong effect on English learning outcomes. The research findings widens our understanding of Chinese student’s use of artificial intelligence in second language acquisition, and also reflect Chinese student’s AI anxiety in the era of information technology. Biological sciences/Psychology Health sciences/Diseases GenAI EFL learning motivation learning outcome I. Introduction GenAI has caught educator’s attention globally[ 1 ]. Research has shown that GenAI tools have been widely used in intelligent tutoring systems, personalized curriculum design, and web-based chatbots[ 2 – 4 ]. With highly personalized learning experience, these tools have facilitated learner's learning efficiency [ 5 , 6 ]. On early study found that ChatGPT, an GenAI language model built in November 2022, has clear advantages for idea generation and data identification when doing academic research[ 7 ]. Many countries and regions have published a series of favorable policies on the application of GenAI to education[ 8 ]. For language education, GenAI is not a substitute for teachers; instead, it can be regarded as a powerful teaching and learning mode[ 9 , 10 ]. In many countries, such as America, China and India, the application of GenAI in education have significantly enhanced the efficiency and effectiveness of language education[ 11 ]. For English learners, GenAI reshapes the learning mode with a personalized learning method based on every learner's specific needs and abilities with higher learning efficiency[ 12 ]. Meanwhile, GenAI instantly assesses learner’s oral pronunciation, grammar, and vocabulary use and provides timely feedback and corrections[ 13 ]. In addition, by using AI-driven interactive learning applications to practice conversations, English learners improve their speech ability and increases the enjoyment of learning[ 14 ]. Despite the recognition of GenAI’s educational potential, systematic research on its impact on college students in terms of EFL’s learning effectiveness, motivation enhancement, and academic performance needs to be conducted. This research gap has attracted many educational scholars and practitioners. Foreign language educators, language teachers, and curriculum developers are particularly interested in the influence of GenAI on students’ academic writing skills and how to better integrate GenAI into English teaching practices[ 15 ]. However, with the global demand for GenAI, the exploration of effective teaching strategies and tools has become particularly important[ 16 , 17 ]. As an innovative teaching aid, the application and effectiveness of GenAI tools in English teaching need to be further explored. This study focuses on the effect of GenAI on EFL’s learning motivation, effectiveness and achievement. Questionnaires will be used, and a wide range of data to analyze how GenAI affects the learning effectiveness of college English learners in China will be collected. We hope that this study can provide new perspectives for second language acquisition, and support for the application of educational technology in language learning. As a result, language learners can improve the efficiency of their English learning, facilitate communication among people with different cultural backgrounds, and ultimately facilitate better integration into international communication and cooperation in the era of globalization. II. Literature review GenAI, beyond traditional teaching methods, as an innovative learning aid, has shown unique value in language learning. One Study demonstrated that GenAI enhances the efficiency and quality of English learning and brings innovation and transformation to language education[18]. Understanding the theoretical foundations can help us better utilize GenAI as a tool for EFL. 2.1 Cognitive psychology Cognitive psychology provides a framework for understanding the impact of GenAI on English learning. It offers models on how humans receive, process, and store information[19]. Empirical investigations within cognitive psychology have demonstrated the utility of memory theories, such as working memory and long-term memory constructs, in informing the AI-assisted review and memorization strategies to augment language learning effectiveness[20]. GenAI adeptly curate tailored learning resources, including reading materials, videos, and audio, calibrated to individual proficiency levels[21, 22] Moreover, GenAI has been harnessed in the oral English pronunciation correction systems and automated scoring mechanisms, which can effectively identify and rectify pronunciation errors. GenAI Apps accommodate flexible learning schedules and diverse learning environments with convenience. This personalized and convenient mode of language instruction represents a paradigm shift from traditional methods, underscoring its unparalleled efficiency and efficacy. 2.2 Self-determination theory Self-determination theory, formulated by psychologists Deci and Ryan in the 1980s, posits that all organisms, including humans, possess inherent capabilities to develop intricate systems of intrinsic motivation and to pursue three fundamental psychological needs: autonomy, competence, and relatedness[23]. GenAI tools have been found their capacity to bolster learners' engagement, efficiency, and particularly human-computer interaction, which enables learners to perceive progress and accomplishment in their educational endeavors. This, in turn, cultivated confidence and sustained motivation in learning[24]. Within the realm of education, GenAI, functioning as a facilitator of learning, autonomously curates and explores diverse educational resources and nurtures student's autonomy, competence, and sense of relevance. Moreover, GenAI engenders and sustains positive motivational and behavioral patterns[25, 26]. 2.3 Technology acceptance model Technology Acceptance Model (TAM) posits that user's attitudes toward a given technology, which influence their behavioral intentions and ultimately determine their choice[27, 28]. Introduced by Fred Davis in 1989, TAM explicates user's acceptance behavior toward information technology, drawing upon principles of rational behavior and notably expectancy theory and attitude theory. This model underscores two pivotal determinants: perceived ease of use and perceived usefulness. Perceived ease of use pertains to users' perceptions of the ease or difficulty associated with employing certain technology. Consequently, users are inclined to embrace and utilize a technology perceived as user friendly. On the other hand, perceived usefulness denotes the extent to which users believe that technology will enhance their job performance. Users are more likely to utilize technology if they perceive it as beneficial to their work or study endeavors[29]. Widely employed across information systems, human-computer interaction, and educational technology domains, TAM serves as a predictive and explanatory framework for user acceptance of diverse technological products[30]. 2.4 Krashen's input hypothesis Krashen's input hypothesis postulates that language acquisition transpires when learners are exposed to linguistic input slightly beyond their current proficiency level, encapsulated by the "i+1" principle[31]. GenAI technology adeptly tailor input to match learner's abilities and progress, ensuring exposure to linguistically challenging yet comprehensible material. Leveraging an adaptive learning system, GenAI dynamically modulates input difficulty to ensure that learners consistently operate within the optimal learning zone and facilitate natural language proficiency. Additionally, GenAI mitigates learner anxiety and bolsters motivation through personalized positive reinforcement, encouragement, and assistance. Furthermore, GenAI enriches learning experiences through gamified strategies and diminish affective filters and enhancing language acquisition efficiency. Krashen's theoretical framework elucidates GenAI's potential to enhance language acquisition[32]. As GenAI technology advances and has broader applications, its capacity to expedite language acquisition should be further explored and realized. Building upon the aforementioned theoretical underpinnings, three research questions are proposed as follows. 1.Is there any descriptive factors related to learning motivation and learning outcomes? 2.The use and understanding of GenAI have a significant positive impact on EFL’s learning motivation? 3.The use and understanding of GenAI have a significant positive impact on EFL’s learning outcomes? III. Research Design and Data Analysis 1. Statistics Description This study conducted a detailed survey on higher education students throughout China. In May to June 2024, we distributed 800 questionnaires in four Chinese university, located in Guangzhou and Changsha. 711 questionnaires were returned, with 88.88% recovery rate. Among them, 566 questionnaires were valid, for a recovery effectiveness rate of 79.61% as presented in table 1. Table 1 Statistics description Variant options frequency Percentage (%) Gender male 312 55.124 female 239 42.226 others 15 2.65 Age 18-19 99 17.491 20-21 219 38.693 22-23 188 33.216 Over 24-year-old 60 10.601 Grade freshman 55 9.717 Sophomore 106 18.728 Junior 208 36.749 Senior 197 34.806 English level None 61 10.777 CET4 100 17.668 CET6 213 37.633 TEM8 192 33.922 Language environment Non-Chinese speaking area 58 10.247 bilingual area 95 16.784 dialect 218 38.516 Mandarin 195 34.452 English learning duration (per week) ＜ 1 hour 179 31.625 1 - 3 hours 320 56.537 4 - 6 hours 46 8.127 ＞ 6 hours 21 3.71 Study or travel abroad experience never 89 15.724 Short-term (＜ 1 month) 263 46.466 Medium-term (1 to 6 months) 162 28.622 Long-term (＞ 6 months) 52 9.187 2. Methodology In this study, all methods were carried out in accordance with relevant guidelines and regulations. The experimental protocols were approved by the Institutional Review Board of the first author’s working institution. Informed consent was obtained from all subjects or their legal guardian prior to their inclusion in the study. The design of the questionnaire draws on recent research in the fields of artificial intelligence, second language acquisition, and educational psychology. It specifically references theories: Fred Davis's Technology Acceptance Model, Krashen's Input Hypothesis Theory, and Edward L. Deci and Richard M. Ryan's Self-Determination Theory. The questionnaire was structured into four dimensions: participant’s demographics, use and understanding of GenAI tools, EFL learning motivation, and learning effectiveness. The first dimension is to collect participant's demographic information, including their gender, age, grade, English level, language environment in which they grew up, average English learning time (per week), and overseas study or travel experience. The second dimension is to assess undergraduate’s use frequency and understanding of GenAI tools. The third dimension focuses on the undergraduate’s motivational role of GenAI tools in English learning, such as increase learning interests, improve listening and speaking skills, enlarge their vocabulary and improve intercultural communication. The fourth dimension explores how well GenAI tools improve EFL learning outcomes: English speaking, listening, reading, writing, vocabulary, and intercultural communication. All questions were evaluated on a five-point Likert scale (for GenAI1-GAi11: SD=Strongly Disagree=1, D=Disagree=2, NS=Not Sure=3, A=Agree=4, SA=Strongly Agree=5; for MTV1 to MTV13 and LO1 to LO9, Not Sure= NS=3, SA=Strongly Agree=1, A=Agree=2, NS=Not Sure=3,D=Disagree=4,SD=Strongly Disagree=5) to provide a picture of the impact of GenAI tools on higher education students' English learning. Questions of the specific measurement are shown in Table 2. Table 2 Variable definitions and assignments Dimension variant encoding variable assignment demography gender GND M=1, F=2, Other=3 age AGE 18-19 = 1, 20-21 = 2, 22-23 = 3, 24+ = 4 grade GRD Grade 1 = 1, Grade 2 = 2, Grade 3 = 3, Grade 4 = 4 English Level EXM None=1,CET4=2,CET6=3,TEM8=4 linguistic environment ENV Non-Chinese-speaking Area=1, Bilingual =2, Dialects=3, Mandarin=4 English learning Duration TIM Less than 1 hour = 1, 1 to 3 hours = 2, 4 to 6 hours = 3, more than 6 hours = 4 Study or travel abroad ABD None = 1, short-term = 2, medium-term = 3, long-term = 4 Use and understanding of GenAI Has GenAI been used GAI1 Never= 1, Occasional use = 2, Frequent use= 3, Everyday=4, Highly Dependent = 5 Use frequency GAI2 Never = 1, Several times a month = 2, Several times a day = 3, Several times a week = 4, Every day = 5 Instant feedback GAI3 SD=1, D=2, NS=3, A=4, SA=5 Learning efficiency GAI4 SD=1, D=2, NS=3, A=4, SA=5 Match between generated content and needs GAI5 SD=1, D=2, NS=3, A=4, SA=5 user-friendly interface GAI6 SD=1, D=2, NS=3, A=4, SA=5 Generating quality GAI7 SD=1, D=2, NS=3, A=4, SA=5 interaction GAI8 SD=1, D=2, NS=3, A=4, SA=5 Alternatives to Traditional Learning Methods GAI9 SD=1, D=2, NS=3, A=4, SA=5 Effectiveness of English communication skills GAI10 SD=1, D=2, NS=3, A=4, SA=5 Personalized Learning GAI11 SD=1, D=2, NS=3, A=4, SA=5 Learning motivation Effective learning method MTV1 SA=1, A=2, NS=3, D=4, SD=5 GenAI has a future MTV2 SA=1, A=2, NS=3, D=4, SD=5 Help to understand cultural differences MTV3 SA=1, A=2, NS=3, D=4, SD=5 help English learning in the long run MTV4 SA=1, A=2, NS=3, D=4, SD=5 Share with English learning partners MTV5 SA=1, A=2, NS=3, D=4, SD=5 Make English learning more Fun MTV6 SA=1, A=2, NS=3, D=4, SD=5 Adapt to the new paradigm of English learning MTV7 SA=1, A=2, NS=3, D=4, SD=5 Enhance motivation for English learning MTV8 SA=1, A=2, NS=3, D=4, SD=5 Improve self-confidence in learning English MTV9 SA=1, A=2, NS=3, D=4, SD=5 improve self-directed learning MTV10 SA=1, A=2, NS=3, D=4, SD=5 Improve the depth of English Learning MTV11 SA=1, A=2, NS=3, D=4, SD=5 Clearer goals for English language learning MTV12 SA=1, A=2, NS=3, D=4, SD=5 Enhance the emotional experience of English learning MTV13 SA=1, A=2, NS=3, D=4, SD=5 Learning outcome spoken language LO1 SA=1, A=2, NS=3, D=4, SD=5 listening LO2 SA=1, A=2, NS=3, D=4, SD=5 writing LO3 SA=1, A=2, NS=3, D=4, SD=5 reading LO4 SA=1, A=2, NS=3, D=4, SD=5 Grammar LO5 SA=1, A=2, NS=3, D=4, SD=5 vocabulary LO6 SA=1, A=2, NS=3, D=4, SD=5 Cross-culture cultures LO7 SA=1, A=2, NS=3, D=4, SD=5 critical thinking LO8 SA=1, A=2, NS=3, D=4, SD=5 innovation capacity LO9 SA=1, A=2, NS=3, D=4, SD=5 IV. Results and Data Analysis 4.1 Validity testing SPSS 20.0 was used to validate the reliability of the data for all the variables covered in the paper. 4.1.1 Normal distribution test The basic premise of the statistical analysis was that the questionnaire data followed a normal distribution, so an analysis of the mean, standard deviation, skewness, and kurtosis of the questionnaire is required. The questionnaire data conform to a normal distribution(The absolute value of the skewness coefficient＜3; the absolute value of the kurtosis coefficient＜10). The results are shown in Table 3. Table 3 Descriptive statistics Variant mean standard deviation skewness kurtosis GAI1 3.532 1.314 0.481 1.007 GAI2 3.571 1.246 -0.495 -0.883 GAI3 3.64 1.243 -0.507 -0.905 GAI4 3.592 1.26 -0.523 -0.907 GAI5 3.601 1.221 -0.519 -0.776 GAI6 3.594 1.282 -0.508 -0.976 GAI7 3.617 1.245 -0.524 -0.857 GAI8 3.549 1.287 -0.495 -0.956 GAI9 3.565 1.268 -0.494 -0.961 GAI10 3.558 1.307 -0.502 -0.975 GAI11 3.595 1.324 0.512 -1.02 MTV1 3.624 1.268 -0.561 -0.907 MTV2 3.657 1.26 -0.616 -0.765 MTV3 3.666 1.287 -0.704 -0.657 MTV4 3.618 1.246 -0.613 -0.719 MTV5 3.629 1.222 -0.558 -0.757 MTV6 3.67 1.223 -0.618 -0.707 MTV7 3.668 1.22 -0.622 -0.676 MTV8 3.67 1.214 -0.602 -0.679 MTV9 3.645 1.203 -0.472 -0.925 MTV10 3.668 1.284 -0.605 -0.868 MTV11 3.634 1.214 -0.55 -0.82 MTV12 3.641 1.232 -0.584 -0.755 MTV13 3.673 1.247 -0.632 -0.755 LO1 3.592 1.264 -0.48 -0.952 LO2 3.534 1.315 -0.481 -1.024 LO3 3.565 1.332 -0.524 -1 LO4 3.541 1.317 -0.513 -0.963 LO5 3.523 1.302 -0.49 -0.912 LO6 3.519 1.3 -0.491 -0.971 LO7 3.553 1.27 -0.499 -0.868 LO8 3.567 1.295 -0.507 -0.945 LO9 3.574 1.297 -0.491 -0.964 Table 3 shows that the absolute value of the skewness coefficient is less than 1, and the absolute value of the kurtosis coefficient is less than 3. These indicates that the data follow a normal distribution. Cronbach's α test was performed. The results are shown in Table 4. The Cronbach's α‌ coefficients for each scale indicate a high level of reliability (＞0.8）. Table 4 Cronbach's α‌ coefficients Dimension quantities Cronbach's α‌ coefficient confidence level GAI 11 0.969 high reliability MTV 13 0.972 high reliability LO 9 0.962 high reliability 2. Validity analysis Validity analysis is to measure the structural validity of the questionnaire. The evaluation indices include the KMO test, Bartlett's test of sphericity, cumulative contribution rate, and factor loading. The KMO test and Bartlett's test of sphericity are used to determine whether the data are suitable for factor analysis. The cumulative contribution rate represents the degree of cumulative validity of the common factors on the scale. The factor loading indicates the degree of correlation between the original variables and a common factor. The KMO value of the scale was 0.984. The approximate chi-square value of Bartlett's test of sphericity was 19063.113 (p<0.0001), which indicates that the scale is suitable for factor analysis. The specific test results are shown in Table 5. An eigenvalue greater than 1 was used as the criterion for factor extraction, resulting in the extraction of four main components. As shown in Table 5, the proportion of cumulative explained variance is 76.905%. The factors attributed to the 34 items correspond to the same dimensions as the initial item set. There is no cross-factor loading phenomenon, which shows that the structural validity is relatively high. Table 5 Exploratory factor analysis Table of factor loading coefficients after rotation Commonality (common factor variance) Postrotation factor loading coefficients Factor 1 Factor 2 Factor 3 Factor 4 GAI1 0.219 0.817 0.201 0.134 0.774 GAI2 0.245 0.808 0.185 0.088 0.755 GAI3 0.251 0.815 0.187 0.056 0.766 GAI4 0.269 0.802 0.215 0.04 0.764 GAI5 0.183 0.835 0.151 -0.024 0.754 GAI6 0.255 0.824 0.179 0.031 0.777 GAI7 0.239 0.82 0.169 0.042 0.76 GAI8 0.284 0.795 0.213 0.059 0.761 GAI9 0.268 0.816 0.181 0.049 0.773 GAI10 0.285 0.808 0.179 0.019 0.767 GAI11 0.262 0.827 0.163 0.052 0.782 MTV1 0.817 0.235 0.217 0.064 0.774 MTV2 0.799 0.245 0.225 0.051 0.752 MTV3 0.818 0.227 0.218 0.074 0.773 MTV4 0.791 0.265 0.219 0.049 0.747 MTV5 0.766 0.266 0.252 0.061 0.724 MTV6 0.795 0.273 0.208 0.051 0.753 MTV7 0.786 0.244 0.249 0.022 0.74 MTV8 0.765 0.296 0.216 0.047 0.722 MTV9 0.791 0.211 0.216 0.026 0.717 MTV10 0.821 0.211 0.243 0.061 0.781 MTV11 0.811 0.262 0.175 0.034 0.758 MTV12 0.788 0.243 0.25 0.051 0.745 MTV13 0.811 0.252 0.215 0.086 0.776 LO1 0.258 0.216 0.806 0.063 0.767 LO2 0.287 0.206 0.812 -0.051 0.787 LO3 0.224 0.201 0.84 0.024 0.796 LO4 0.245 0.2 0.823 0.07 0.782 LO5 0.253 0.18 0.801 0.072 0.742 LO16 0.209 0.199 0.831 0.093 0.782 LO7 0.275 0.225 0.792 0.001 0.754 LO8 0.255 0.183 0.828 0.038 0.785 LO9 0.263 0.238 0.164 0.914 0.988 cumulative interpretation variance ratio 28.903% 54.676% 74.042% 76.905% KMO value 0.984 Bartlett's test of sphericity approximate chi-square 19063.113 P 0.000*** df 528 Note: ***, **, and * represent 1%, 5%, and 10% significance levels. 4.2 Related Analysis Related analysis are used to examine the correlation between higher education student’s use and understanding of GenAI tools and learning motivation, learning outcomes. As shown in Table 6, there was a highly significant positive correlation between higher education student’s perception and use frequency of GenAI tools and their learning motivation, learning outcomes. Specifically, the correlation coefficient between higher education student’s perception and use frequency of GenAI tools and learning motivation is 0.558, with a significance level of 0.000. It indicates that higher education student’s use and understanding of GenAI tools are associated with stronger learning motivation. Similarly, the correlation coefficient between higher education student’s use and understanding of GenAI tools and learning outcomes is 0.489, with a significance level of 0.000. This suggests that greater use and understanding of GenAI tools lead to better learning outcomes. Table 6 Correlation analysis of use and understanding of GenAI with learning motivation, learning effectiveness MTV LO GenAI 0.558 (0.000***) 0.489 (0.000***) Note: ***, **, and * represent 1%, 5%, and 10% significance. Correlation analysis can verify the close relationship between higher education student’s GenAI acceptance and their learning motivation, learning outcomes. It does not mean that they have a significant relationship, therefore, their relationship needs to be further verified. 4.3 Regression Analysis According to the proposed hypotheses 1 to 3, this study employed regression analysis to test these effects. Learning effectiveness and learning outcomes were treated as dependent variables, while gender, age, grade, English examination, language environment, English learning duration, overseas study and travel experience, and higher education student’s use and understanding of GenAI tools were regarded as independent variables. Regression analyses were conducted using two models. The results are presented in Table 7. Table 7 The effects of undergraduate ’ s use and understanding of GenAI tools on learning motivation and learning outcome variant MTV LO β t β t GenAI 0.078 2.173 0.115 2.662 GND -0.035 -1.283 -0.062 -1.919 AGE -0.063 -2.332 -0.054 -1.677 GRD 0.317 8.736 0.18 4.133 EXM 0.318 8.627 0.26 5.896 ENV 0.205 5.576 0.214 4.851 TIM 0.059 2.155 0.094 2.874 ABD 0.058 2.167 0.108 3.355 F 106.152*** 52.722*** Adjustment of R² 0.598 0.423 Note: ***, **, and * represent 1%, 5%, and 10% significance. Model 1 examined GenAI tools’ effect on higher education student’s learning motivation. The adjusted R²value was 0.598, and the F was 106.152, It reaches statistical significance ( p < 0.001), which indicates that the predictor variables explained 59.8% of the variance in learning motivation. Specifically, the regression coefficient for higher education student’s use and understanding of GenAI tools was 0.078, with t value of 2.173 and p value of 0.030**. It verifies Hypothesis H1 in this study: students who frequently use GenAI are significantly more motivated than those who are not. This finding supports the Technology Acceptance Model, particularly the perceived usefulness factor, which influences user's attitudes and behavioral intentions toward technology[33]. If students find GenAI tools easy and convenient to use, they tend to use them for their learning. Ease of use reduces resistance to technology and lowers the threshold for usage, which enhance their learning motivation. When learners perceive that GenAI tools improve learning efficiency and effectiveness through personalized recommendations and interactive learning, they find these tools to be more useful[34]. Model 2 focused on the use and understanding of GenAI tools on student’s learning outcomes. It shows an adjusted R²value of 0.423 and F of 52.722 ( p < 0.001), which indicates that the predictor variables explained 47.1% of the variance. The regression coefficient for student’s use and understanding of GenAI tools was 0.115, with t value of 2.662 and p value of 0.008***. These verifies Hypothesis H2: students who frequently uses GenAI tools achieve better English learning outcomes than those who uses GenAI tools with lower frequency. According to self-determination theory, GenAI tools provide personalized learning plans and self-paced learning paths. Hence, students are more motivated to learn when they feel in control of their learning. Active learning improves their learning outcomes. GenAI tools offer instant feedback and adaptive learning contents, which helps learners identify and strengthen their weaknesses and build on their strengths. As learners achieve their goals with GenAI tool’s assistance, their sense of self-confidence can be improved. Then their learning effectiveness will be reinforced because they take the initiative to learn. Additionally, GenAI tools help students to get involved in the social interactions. For example, it helps students to cross the language barriers through language translation and promote international communication and cooperation. At the meantime, dialogue robots provide a safe environment for people to better express their thoughts and emotions both in the oral and written form[35]. These social interactions enhance student’s sense of relatedness and community and boost their learning motivation and effectiveness. By fulfilling the psychological needs of autonomy, competence, and relatedness, GenAI tools promote student’s intrinsic learning motivation and improve learning effectiveness[36]. V. Discussion Research Question 1: Is there any descriptive factors related to learning motivation and learning outcomes? As presented in Table 7 , the grade level, English language proficiency, and language environment of upbringing significantly affected EFL learning motivation and learning outcomes. Higher grades were correlated with stronger positive effects of GenAI on motivation, learning outcomes. And the moderating effect of the language environment was particularly significant; learners from non-Chinese-speaking areas achieved the best learning outcomes. English proficiency also moderated learning achievement significantly, with higher English levels correlating with better learning effectiveness. Gender significantly affected learning achievement and outcomes, but had a non-significant effect on motivation. Additionally, age, frequency of weekly English use, and overseas study and travel experience positively influenced motivation, learning outcomes. These results highlight the important moderating roles of personal factors such as age, English proficiency, and the language environment in which one grows up. Research Question 2: The use and understanding of GenAI have a significant positive impact on EFL’s learning motivation? The study revealed that GenAI tools significantly enhance both intrinsic and extrinsic motivation by providing personalized learning plans, instant feedback, and interactive learning environments. This aligns with the core concepts of the TAM, where perceived usefulness is a key factor influencing learner's acceptance and use of new technologies. The perceived usefulness of GenAI tools enhances learner's motivation by increasing learning efficiency and effectiveness. Additionally, self-determination theory supports this study, indicating that GenAI tools meet learner's needs for autonomy, competence, and relevance, thereby stimulating intrinsic motivation [ 37 ]. According to Stephen Krashen's input hypothesis, learners benefit from linguistic input that is slightly above their current level, known as comprehensible input. GenAI tools provide students with personalized learning experience and tailor language input to the student's own ability and progress. This personalized input aligns with the learner’s “i + 1” level, ensuring that they consistently receive materials that challenge them just beyond their current proficiency. To improve student’s ability, GenAI tools assist them quickly to understand and correct their mistakes by offering immediate feedback. GenAI tools dynamically adjusts their difficulty of learning materials based on learner’s interactions and performance to ensure continuous exposure to appropriately challenging input. Additionally, GenAI tools maintain learner engagement through various ways, such as online courses, apps, or virtual assistants to provide ongoing comprehensible input. GenAI tools are designed to be interactive and enjoyable, increasing learner motivation and engagement. According to the input hypothesis, language acquisition is more effective when learners are interested in and actively engaged in language input. GenAI tools, like ChatGPT, Claude and Gemini, offer rich learning resources, including video, audio, and reading materials and provide a diverse range of language inputs to help learners grasp languages from multiple perspectives and contexts. While generative artificial intelligence brings a lot of convenience to learning, it also inevitably has some side effects,for instance, the formation of dependence psychology. Over-reliance on GenAI may weaken student’s autonomous learning ability and intrinsic learning motivation. when students are used to getting answers quickly with the help of artificial intelligence tools, they may reduce the in-depth thinking and active exploration of problems, resulting in less initiative to learn. Taking EFL academic writing as an example, if students frequently use GenAI to generate contents, they may gradually lose the ability to think and express themselves independently. In the long run, when facing learning tasks, students may first think of using external tools, instead of relying on their own knowledge and ability to solve problems, which will have a negative impact on learning motivation. Research Question 3: The use and understanding of GenAI have a significant positive impact on EFL’s learning outcomes? The results showed that GenAI tools positively impact learning outcomes, especially listening comprehension, speaking, reading comprehension, and writing skills, particularly in their vocabulary[ 24 , 38 , 39 ]. The personalized learning paths and instant feedback mechanisms of GenAI tools allow learners to receive customized content and guidance according to their progress and abilities, enhancing learning efficiency. The interactivity and enjoyment of GenAI tools further increase learner engagement, making the learning process more vivid and engaging, thus improving learning effectiveness[ 40 ]. Furthermore, GenAI tools help learners apply their knowledge in real-life communication scenarios, enhancing their learning performances[ 41 ]. While GenAI in education shows great potential for optimizing the learning experience and improving educational effectiveness, it is crucial to guide students to use GenAI critically and consider the ethical implications of GenAI technology in education [ 42 ]. Likewise, it may limit creativity and cause AI anxiety. When students over-rely on AI-generated ideas and solutions, their own innovative thinking and creativity may be inhibited. For example, in the process of artistic creation or writing, if students always refer to the works generated by generative artificial intelligence, they may fall into the trap of imitation. And it is difficult to form a unique style and creativity, which will affect the learning effect and personal development. At the same time, GenAI can generate information with high pace, but this can also lead to information overload, causing anxiety and stress. Too much information makes students feel at a loss, and they do not know how to screen and deal with it. In addition, students may worry that they cannot master all the knowledge in face of technology explosion and result in AI anxiety and further reduces the learning effect. VI. Conclusion This study explores the role of GenAI tools in L2 acquisition, with a focus on their impact on learning motivation and learning outcomes. It also examined the moderating effects of individual factors such as gender, age, and English learning level. The findings suggest that the use of GenAI tools significantly enhances learners' motivation and outcomes. These results align with the literature on the positive effects of GenAI applications in education. The results of this study have important implications for educational practices. Educational institutions and teachers can use GenAI tools to enhance the language learning experience and improve learning efficiency. GenAI tools can provide learners with personalized learning paths and stimulate their interest and motivation through interactive and engaging learning environments. Additionally, educational policymakers should consider integrating GenAI tools into language teaching to promote educational innovation and improve learning effectiveness. The application of GenAI tools in language learning also has broader socioeconomic implications[ 43 ]. By providing a personalized and efficient learning experience, GenAI tools help to narrow the gap in the distribution of educational resources and improve educational equity [ 44 ]. Furthermore, the widespread adoption of GenAI tools can help individuals develop globally competitive language skills and promote international exchange and cooperation [ 45 ]. While this study strongly supports the positive role of GenAI tools in English language learning, several limitations must be noted. At first, the questionnaire data rely on respondent’s self-reports, so that they may be subject to inaccuracies or memory bias. Secondly, although variables such as gender, age, and grade were controlled for. Other variables, such as individual professional background, native language, and learning styles, may have affected the results. The study may have been conducted in specific settings or with specific learner types, which limited the generalizability of the results. Finally, the use of GenAI tools may not have been consistently controlled. With different learners possibly using GenAI in various ways, the results may be affected. Future research should consider these factors for a more comprehensive understanding. Declarations Data Availability All methods were carried out in accordance with relevant guidelines and regulations. The data underlying the results presented in the study are avail-able within the manuscript. Conflicts of Interest There is no potential conflict of interest in our paper, and allauthors have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not be-en published or submitted for publication elsewhere. References Jordan, M.I. and T.M. Mitchell, Machine learning: Trends, perspectives, and prospects. Science, 2015. 349(6245): p. 255-260. Lameras, P. and S. Arnab, Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education. Information, 2022. 13(1): p. 14. Roll, I. and R. Wylie, Evolution and Revolution in Artificial Intelligence in Education. 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Mandal, English teaching practice based on artificial intelligence technology. Journal of Intelligent & Fuzzy Systems, 2019. 37(3): p. 3381-3391. Lai, K.K. and H.H. Chen, A comparative study on the effects of a VR and PC visual novel game on vocabulary learning. Computer Assisted Language Learning, 2023. 36(3): p. 312-345. Bhutoria, A., Personalized education and Artificial Intelligence in the United States, China, and India: A systematic review using a Human-In-The-Loop model. Computers and Education: Artificial Intelligence, 2022. 3: p. 100068. Hsu, T., et al., Effects of a Pair Programming Educational Robot-Based Approach on Students’ Interdisciplinary Learning of Computational Thinking and Language Learning. Frontiers in Psychology, 2022. 13. Wu, R. and Z. Yu, DoAI chatbots improve students learning outcomes? Evidence from a meta‐analysis. British Journal of Educational Technology, 2024. 55(1): p. 10-33. Belda-Medina, J. and V. Kokošková, Integrating chatbots in education: insights from the Chatbot-Human Interaction Satisfaction Model (CHISM). International Journal of Educational Technology in Higher Education, 2023. 20(1). Herodotou, C., et al., Empowering online teachers through predictive learning analytics. British Journal of Educational Technology, 2019. 50(6): p. 3064-3079. Celik, I., Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 2023. 138: p. 107468. Derakhshan, A., F. Moradi and M. Nazari, Conceptualising the role of practice level in language teacher identity construction: An identities‐in‐practice study. International Journal of Applied Linguistics, 2024. 34(2): p. 797-811. Cath, C., Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2018. 376(2133): p. 20180080. Embretson, S. and J. Gorin, Improving Construct Validity with Cognitive Psychology Principles. Journal of Educational Measurement, 2001. 38(4): p. 343-368. Roediger, H.L. and M.A. Pyc, Inexpensive techniques to improve education: Applying cognitive psychology to enhance educational practice. Journal of Applied Research in Memory and Cognition, 2012. 1(4): p. 242-248. Dekeyser, R. and R. Criado, Automatization, Skill Acquisition, and Practice in Second Language Acquisition. 2012. O'Malley, J.M., A.U. Chamot and C. Walker, Some Applications of Cognitive Theory to Second Language Acquisition. Studies in Second Language Acquisition, 1987. 9(3): p. 287-306. Printer, L., Positive emotions and intrinsic motivation: A self-determination theory perspective on using co-created stories in the language acquisition classroom. Language Teaching Research, 2023. Wei, P., X. Wang and H. Dong, The impact of automated writing evaluation on second language writing skills of Chinese EFL learners: a randomized controlled trial. Frontiers in Psychology, 2023. 14. McEown, M.S. and W.L.Q. Oga-Baldwin, Self-determination for all language learners: New applications for formal language education. System, 2019. 86: p. 102124. Noels, K.A., et al., Why Are You Learning a Second Language? Motivational Orientations and Self‐Determination Theory. Language Learning, 2003. 53(S1): p. 33-64. Liu, H., L. Wang and M.J. Koehler, Exploring the intention‐behavior gap in the technology acceptance model: A mixed‐methods study in the context of foreign‐language teaching in China. British Journal of Educational Technology, 2019. 50(5): p. 2536-2556. Warner, J.A., X. Koufteros and A. Verghese, Learning Computerese. Educational and Psychological Measurement, 2014. 74(6): p. 991-1017. Hess, T.J., A.L. McNab and K.A. Basoglu, Reliability Generalization of Perceived Ease of Use, Perceived Usefulness, and Behavioral Intentions. MIS Quarterly, 2014. 38(1): p. 1-28. Liu, G. and C. Ma, Measuring EFL learners’ use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching, 2024. 18(2): p. 125-138. Payne, M., Exploring Stephen Krashen's ‘i+1’ acquisition model in the classroom. Linguistics and Education, 2011. 22(4): p. 419-429. Yuan, Y., An empirical study of the efficacy of AI chatbots for English as a foreign language learning in primary education. Interactive Learning Environments, 2023: p. 1-16. Kim, J., K. Merrill Jr. and C. Collins, AI as a friend or assistant: The mediating role of perceived usefulness in social AI vs. functional AI. Telematics and Informatics, 2021. 64: p. 101694. An, X., et al., Modeling students’ perceptions of artificial intelligence assisted language learning. Computer Assisted Language Learning, 2023: p. 1-22. Huang, A.Y.Q., O.H.T. Lu and S.J.H. Yang, Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 2023. 194: p. 104684. Lin, P., et al., Modeling the structural relationship among primary students’ motivation to learn artificial intelligence. Computers and Education: Artificial Intelligence, 2021. 2: p. 100006. Criollo-C, S., et al., Exploring the technological acceptance of a mobile learning tool used in the teaching of an indigenous language. PeerJ Computer Science, 2021. 7: p. e550. Derakhshan, A. and J. Fathi, Growth mindset, self-efficacy, and self-regulation: A symphony of success in L2 speaking. System, 2024. 123: p. 103320. Lai, K.K. and H.H. Chen, A comparative study on the effects of a VR and PC visual novel game on vocabulary learning. Computer Assisted Language Learning, 2023. 36(3): p. 312-345. Khazaie, S. and A. Derakhshan, Extending embodied cognition through robot's augmented reality in English for medical purposes classrooms. English for Specific Purposes, 2024. 75: p. 15-36. Vincent-Lancrin, S. and R. van der Vlies, Trustworthy artificial intelligence (AI) in education. 2020. Crawford, J., et al., When artificial intelligence substitutes humans in higher education: the cost of loneliness, student success, and retention. Studies in Higher Education, 2024. 49(5): p. 883-897. Memarian, B. and T. Doleck, ChatGPT in education: Methods, potentials, and limitations. Computers in Human Behavior: Artificial Humans, 2023. 1(2): p. 100022. Robinson, S.C., Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national public policy strategies for artificial intelligence (AI). Technology in Society, 2020. 63: p. 101421. Knox, J., Artificial intelligence and education in China. Learning, Media and Technology, 2020. 45(3): p. 298-311. 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Introduction","content":"\u003cp\u003eGenAI has caught educator\u0026rsquo;s attention globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Research has shown that GenAI tools have been widely used in intelligent tutoring systems, personalized curriculum design, and web-based chatbots[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. With highly personalized learning experience, these tools have facilitated learner's learning efficiency [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. On early study found that ChatGPT, an GenAI language model built in November 2022, has clear advantages for idea generation and data identification when doing academic research[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Many countries and regions have published a series of favorable policies on the application of GenAI to education[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor language education, GenAI is not a substitute for teachers; instead, it can be regarded as a powerful teaching and learning mode[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In many countries, such as America, China and India, the application of GenAI in education have significantly enhanced the efficiency and effectiveness of language education[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor English learners, GenAI reshapes the learning mode with a personalized learning method based on every learner's specific needs and abilities with higher learning efficiency[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Meanwhile, GenAI instantly assesses learner\u0026rsquo;s oral pronunciation, grammar, and vocabulary use and provides timely feedback and corrections[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, by using AI-driven interactive learning applications to practice conversations, English learners improve their speech ability and increases the enjoyment of learning[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the recognition of GenAI\u0026rsquo;s educational potential, systematic research on its impact on college students in terms of EFL\u0026rsquo;s learning effectiveness, motivation enhancement, and academic performance needs to be conducted. This research gap has attracted many educational scholars and practitioners. Foreign language educators, language teachers, and curriculum developers are particularly interested in the influence of GenAI on students\u0026rsquo; academic writing skills and how to better integrate GenAI into English teaching practices[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, with the global demand for GenAI, the exploration of effective teaching strategies and tools has become particularly important[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. As an innovative teaching aid, the application and effectiveness of GenAI tools in English teaching need to be further explored.\u003c/p\u003e \u003cp\u003eThis study focuses on the effect of GenAI on EFL\u0026rsquo;s learning motivation, effectiveness and achievement. Questionnaires will be used, and a wide range of data to analyze how GenAI affects the learning effectiveness of college English learners in China will be collected. We hope that this study can provide new perspectives for second language acquisition, and support for the application of educational technology in language learning. As a result, language learners can improve the efficiency of their English learning, facilitate communication among people with different cultural backgrounds, and ultimately facilitate better integration into international communication and cooperation in the era of globalization.\u003c/p\u003e"},{"header":"II. Literature review","content":"\u003cp\u003eGenAI, beyond traditional teaching methods, as an innovative learning aid, has shown unique value in language learning. One Study demonstrated that GenAI enhances the efficiency and quality of English learning and brings innovation and transformation to language education[18]. Understanding the theoretical foundations can help us better utilize GenAI as a tool for EFL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Cognitive psychology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCognitive psychology provides a framework for understanding the impact of GenAI on English learning. It offers models on how humans receive, process, and store information[19]. Empirical investigations within cognitive psychology have demonstrated the utility of memory theories, such as working memory and long-term memory constructs, in informing the AI-assisted review and memorization strategies to augment language learning effectiveness[20]. GenAI adeptly curate tailored learning resources, including reading materials, videos, and audio, calibrated to individual proficiency levels[21, 22]\u0026nbsp;Moreover, GenAI has been harnessed in the oral English pronunciation correction systems and automated scoring mechanisms, which can effectively identify and rectify pronunciation errors. GenAI Apps accommodate flexible learning schedules and diverse learning environments with convenience. This personalized and convenient mode of language instruction represents a paradigm shift from traditional methods, underscoring its unparalleled efficiency and efficacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Self-determination theory\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSelf-determination theory, formulated by psychologists Deci and Ryan in the 1980s, posits that all organisms, including humans, possess inherent capabilities to develop intricate systems of intrinsic motivation and to pursue three fundamental psychological needs: autonomy, competence, and relatedness[23]. GenAI tools have been found their capacity to bolster learners\u0026apos; engagement, efficiency, and particularly human-computer interaction, which enables learners to perceive progress and accomplishment in their educational endeavors. This, in turn, cultivated confidence and sustained motivation in learning[24]. Within the realm of education, GenAI, functioning as a facilitator of learning, autonomously curates and explores diverse educational resources and nurtures student\u0026apos;s autonomy, competence, and sense of relevance. Moreover, GenAI engenders and sustains positive motivational and behavioral patterns[25, 26].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Technology acceptance model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTechnology Acceptance Model (TAM) posits that user\u0026apos;s attitudes toward a given technology, which influence their behavioral intentions and ultimately determine their choice[27, 28]. Introduced by Fred Davis in 1989, TAM explicates user\u0026apos;s acceptance behavior toward information technology, drawing upon principles of rational behavior and notably expectancy theory and attitude theory. This model underscores two pivotal determinants: perceived ease of use and perceived usefulness. Perceived ease of use pertains to users\u0026apos; perceptions of the ease or difficulty associated with employing certain technology. Consequently, users are inclined to embrace and utilize a technology perceived as user friendly. On the other hand, perceived usefulness denotes the extent to which users believe that technology will enhance their job performance. Users are more likely to utilize technology if they perceive it as beneficial to their work or study endeavors[29]. Widely employed across information systems, human-computer interaction, and educational technology domains, TAM serves as a predictive and explanatory framework for user acceptance of diverse technological products[30].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Krashen\u0026apos;s input hypothesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKrashen\u0026apos;s input hypothesis postulates that language acquisition transpires when learners are exposed to linguistic input slightly beyond their current proficiency level, encapsulated by the \u0026quot;i+1\u0026quot; principle[31]. GenAI technology adeptly tailor input to match learner\u0026apos;s abilities and progress, ensuring exposure to linguistically challenging yet comprehensible material. Leveraging an adaptive learning system, GenAI dynamically modulates input difficulty to ensure that learners consistently operate within the optimal learning zone and facilitate natural language proficiency. Additionally, GenAI mitigates learner anxiety and bolsters motivation through personalized positive reinforcement, encouragement, and assistance. Furthermore, GenAI enriches learning experiences through gamified strategies and diminish affective filters and enhancing language acquisition efficiency. Krashen\u0026apos;s theoretical framework elucidates GenAI\u0026apos;s potential to enhance language acquisition[32]. As GenAI technology advances and has broader applications, its capacity to expedite language acquisition should be further explored and realized.\u003c/p\u003e\n\u003cp\u003eBuilding upon the aforementioned theoretical underpinnings, three research questions are proposed as follows.\u003c/p\u003e\n\u003cp\u003e1.Is there any descriptive factors related to learning motivation and learning outcomes?\u003c/p\u003e\n\u003cp\u003e2.The use and understanding of GenAI have a significant positive impact on EFL\u0026rsquo;s learning motivation?\u003c/p\u003e\n\u003cp\u003e3.The use and understanding of GenAI have a significant positive impact on EFL\u0026rsquo;s learning outcomes?\u003c/p\u003e"},{"header":"III. Research Design and Data Analysis","content":"\u003cp\u003e\u003cstrong\u003e1. Statistics Description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study conducted a detailed survey on higher education students throughout China. In May to June 2024, we distributed 800 questionnaires in four Chinese university, located in Guangzhou and Changsha. 711 questionnaires were returned, with 88.88% recovery rate. Among them, 566 questionnaires were valid, for a recovery effectiveness rate of 79.61% as presented in table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Statistics description\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"94%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31%;\"\u003e\n \u003cp\u003eVariant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eoptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003efrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 31%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e55.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e42.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eothers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 31%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003e18-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e17.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003e20-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e38.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003e22-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e33.216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eOver 24-year-old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e10.601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 31%;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003efreshman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e9.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eSophomore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e18.728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eJunior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e36.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e34.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 31%;\"\u003e\n \u003cp\u003eEnglish level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e10.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eCET4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e17.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eCET6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e37.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eTEM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e33.922\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 31%;\"\u003e\n \u003cp\u003eLanguage environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eNon-Chinese speaking area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e10.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003ebilingual area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e16.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003edialect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e38.516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eMandarin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e34.452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 31%;\"\u003e\n \u003cp\u003eEnglish learning duration\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(per week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003e＜\u0026nbsp;1 hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e31.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003e1 - 3 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e56.537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003e4 - 6 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e8.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003e＞\u0026nbsp;6 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 31%;\"\u003e\n \u003cp\u003eStudy or travel abroad\u003c/p\u003e\n \u003cp\u003eexperience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e15.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eShort-term\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(＜\u0026nbsp;1 month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e46.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eMedium-term\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1 to 6 months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e28.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33%;\"\u003e\n \u003cp\u003eLong-term\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(＞\u0026nbsp;6 months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e9.187\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\u003e2. Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, all methods were carried out in accordance with relevant guidelines and regulations. The experimental protocols were approved by the Institutional Review Board of the first author\u0026rsquo;s working institution. Informed consent was obtained from all subjects or their legal guardian prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003eThe design of the questionnaire draws on recent research in the fields of artificial intelligence, second language acquisition, and educational psychology. It specifically references theories: Fred Davis\u0026apos;s Technology Acceptance Model, Krashen\u0026apos;s Input Hypothesis Theory, and Edward L. Deci and Richard M. Ryan\u0026apos;s Self-Determination Theory. The questionnaire was structured into four dimensions: participant\u0026rsquo;s demographics, use and understanding of GenAI tools, EFL learning motivation, and learning effectiveness.\u003c/p\u003e\n\u003cp\u003eThe first dimension is to collect participant\u0026apos;s demographic information, including their gender, age, grade, English level, language environment in which they grew up, average English learning time (per week), and overseas study or travel experience. The second dimension is to assess undergraduate\u0026rsquo;s use frequency and understanding of GenAI tools. The third dimension focuses on the undergraduate\u0026rsquo;s motivational role of GenAI tools in English learning, such as increase learning interests, improve listening and speaking skills, enlarge their vocabulary and improve intercultural communication. The fourth dimension explores how well GenAI tools improve EFL learning outcomes: English speaking, listening, reading, writing, vocabulary, and intercultural communication.\u003c/p\u003e\n\u003cp\u003eAll questions were evaluated on a five-point Likert scale (for GenAI1-GAi11: SD=Strongly Disagree=1, D=Disagree=2, NS=Not Sure=3, A=Agree=4, SA=Strongly Agree=5; for MTV1 to MTV13 and LO1 to LO9, Not Sure= NS=3, SA=Strongly Agree=1, A=Agree=2, NS=Not Sure=3,D=Disagree=4,SD=Strongly Disagree=5) to provide a picture of the impact of GenAI tools on higher education students\u0026apos; English learning. Questions of the specific measurement are shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Variable definitions and assignments\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003eDimension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003evariant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eencoding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003evariable assignment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003edemography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eM=1, F=2, Other=3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eAGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003e18-19 = 1, 20-21 = 2, 22-23 = 3, 24+ = 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003egrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eGrade 1 = 1, Grade 2 = 2, Grade 3 = 3, Grade 4 = 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eEnglish Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eEXM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eNone=1,CET4=2,CET6=3,TEM8=4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003elinguistic environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eENV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eNon-Chinese-speaking Area=1, Bilingual =2, Dialects=3, Mandarin=4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eEnglish learning\u0026nbsp;Duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eTIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eLess than 1 hour = 1, 1 to 3 hours = 2, 4 to 6 hours = 3, more than 6 hours = 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eStudy or travel abroad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eABD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eNone = 1, short-term = 2, medium-term = 3, long-term = 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003eUse and understanding of GenAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eHas\u0026nbsp;GenAI\u0026nbsp;been used\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGAI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eNever= 1, Occasional use = 2, Frequent use= 3,\u0026nbsp;Everyday=4,\u0026nbsp;Highly\u0026nbsp;Dependent\u0026nbsp;= 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eUse\u0026nbsp;frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGAI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eNever = 1, Several times a month = 2, Several times a day = 3, Several times a week = 4, Every day = 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eInstant feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGAI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSD=1, D=2, NS=3, A=4, SA=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eLearning efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGAI4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSD=1, D=2, NS=3, A=4, SA=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eMatch\u0026nbsp;between\u0026nbsp;generated content and needs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGAI5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSD=1, D=2, NS=3, A=4, SA=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003euser-friendly interface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGAI6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSD=1, D=2, NS=3, A=4, SA=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eGenerating quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGAI7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSD=1, D=2, NS=3, A=4, SA=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003einteraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGAI8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSD=1, D=2, NS=3, A=4, SA=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eAlternatives to Traditional Learning Methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGAI9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSD=1, D=2, NS=3, A=4, SA=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eEffectiveness of English communication skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGAI10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSD=1, D=2, NS=3, A=4, SA=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003ePersonalized Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eGAI11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSD=1, D=2, NS=3, A=4, SA=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003eLearning\u003c/p\u003e\n \u003cp\u003emotivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eEffective learning method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eGenAI\u0026nbsp;has a future\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eHelp to understand cultural differences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003ehelp English learning in the long run\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eShare with English\u0026nbsp;learning\u0026nbsp;partners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eMake\u0026nbsp;English\u0026nbsp;learning\u0026nbsp;more Fun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eAdapt to the new paradigm of English learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eEnhance\u0026nbsp;motivation for English\u0026nbsp;learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eImprove self-confidence in learning English\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eimprove self-directed learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eImprove the\u0026nbsp;depth of English Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eClearer goals for English language learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eEnhance\u0026nbsp;the\u0026nbsp;emotional\u0026nbsp;experience of English\u0026nbsp;learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eMTV13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003eLearning\u003c/p\u003e\n \u003cp\u003eoutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003espoken language\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eLO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003elistening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eLO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003ewriting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eLO3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003ereading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eLO4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eGrammar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eLO5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003evocabulary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eLO6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003eCross-culture\u0026nbsp;cultures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eLO7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003ecritical thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eLO8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1,\u0026nbsp;A=2, NS=3,\u0026nbsp;D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.7817%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.2324%;\"\u003e\n \u003cp\u003einnovation capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3239%;\"\u003e\n \u003cp\u003eLO9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.662%;\"\u003e\n \u003cp\u003eSA=1, A=2, NS=3, D=4, SD=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"IV. Results and Data Analysis","content":"\u003cp\u003e\u003cstrong\u003e4.1 Validity testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS 20.0 was used to validate the reliability of the data for all the variables covered in the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.1 Normal distribution test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe basic premise of the statistical analysis was that the questionnaire data followed a normal distribution, so an analysis of the mean, standard deviation, skewness, and kurtosis of the questionnaire is required. The questionnaire data conform to a normal distribution(The absolute value of the skewness coefficient＜3; the absolute value of the kurtosis coefficient＜10). The results are shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Descriptive statistics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eVariant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003estandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eskewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003ekurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eGAI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eGAI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eGAI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eGAI4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eGAI5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eGAI6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eGAI7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eGAI8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eGAI9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eGAI10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eGAI11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMTV13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLO3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLO4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLO5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLO6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLO7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLO8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLO9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e-0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 shows that the absolute value of the skewness coefficient is less than 1, and the absolute value of the kurtosis coefficient is less than 3. These indicates that the data follow a normal distribution.\u003c/p\u003e\n\u003cp\u003eCronbach\u0026apos;s\u0026nbsp;\u0026alpha;\u0026nbsp;test was performed. The results are shown in Table 4. The Cronbach\u0026apos;s\u0026nbsp;\u0026alpha;\u0026zwnj;\u0026nbsp;coefficients for each scale indicate a high level of reliability (＞0.8）.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Cronbach\u0026apos;s\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026alpha;\u0026zwnj;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecoefficients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eDimension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003equantities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eCronbach\u0026apos;s\u0026nbsp;\u0026alpha;\u0026zwnj;\u0026nbsp;coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003econfidence level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eGAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ehigh reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eMTV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ehigh reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eLO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ehigh reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2. Validity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eValidity analysis is to measure the structural validity of the questionnaire. The evaluation indices include the KMO test, Bartlett\u0026apos;s test of sphericity, cumulative contribution rate, and factor loading. The KMO test and Bartlett\u0026apos;s test of sphericity are used to determine whether the data are suitable for factor analysis. The cumulative contribution rate represents the degree of cumulative validity of the common factors on the scale. The factor loading indicates the degree of correlation between the original variables and a common factor. The KMO value of the scale was 0.984. The approximate chi-square value of Bartlett\u0026apos;s test of sphericity was 19063.113 (p\u0026lt;0.0001), which indicates that the scale is suitable for factor analysis. The specific test results are shown in Table 5. An eigenvalue greater than 1 was used as the criterion for factor extraction, resulting in the extraction of four main components. As shown in Table 5, the proportion of cumulative explained variance is 76.905%. The factors attributed to the 34 items correspond to the same dimensions as the initial item set. There is no cross-factor loading phenomenon, which shows that the structural validity is relatively high.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Exploratory factor analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"109%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 47.3756%;\"\u003e\n \u003cp\u003eTable of factor loading coefficients after rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003eCommonality (common factor variance)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 40.743%;\"\u003e\n \u003cp\u003ePostrotation factor loading coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eFactor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eGAI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eGAI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eGAI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eGAI4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eGAI5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eGAI6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eGAI7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eGAI8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eGAI9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eGAI10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eGAI11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eMTV13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eLO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eLO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eLO3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eLO4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eLO5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eLO16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eLO7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eLO8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003eLO9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6393%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.2517%;\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4294%;\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003ecumulative interpretation\u003c/p\u003e\n \u003cp\u003evariance ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.9421%;\"\u003e\n \u003cp\u003e28.903%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9489%;\"\u003e\n \u003cp\u003e54.676%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e74.042%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1131%;\"\u003e\n \u003cp\u003e76.905%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5007%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003eKMO value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 38.1373%;\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003eBartlett\u0026apos;s test of sphericity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.9421%;\"\u003e\n \u003cp\u003eapproximate chi-square\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 29.1952%;\"\u003e\n \u003cp\u003e19063.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.9421%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 29.1952%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.6326%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2194%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.9421%;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 29.1952%;\"\u003e\n \u003cp\u003e528\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: ***, **, and * represent 1%, 5%, and 10% significance levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Related Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelated analysis are used to examine the correlation between higher education student\u0026rsquo;s use and understanding of GenAI tools and learning motivation, learning outcomes. As shown in Table 6, there was a highly significant positive correlation between higher education student\u0026rsquo;s perception and use frequency of GenAI tools and their learning motivation, learning outcomes. Specifically, the correlation coefficient between higher education student\u0026rsquo;s perception and use frequency of GenAI tools and learning motivation is 0.558, with a significance level of 0.000. It indicates that higher education student\u0026rsquo;s use and understanding of GenAI tools are associated with stronger learning motivation. Similarly, the correlation coefficient between higher education student\u0026rsquo;s use and understanding of GenAI tools and learning outcomes is 0.489, with a significance level of 0.000. This suggests that greater use and understanding of GenAI tools lead to better learning outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 Correlation analysis of use and understanding of GenAI with learning motivation, learning effectiveness\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"58%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.7347%;\"\u003e\n \u003cp\u003eMTV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eLO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003eGenAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.7347%;\"\u003e\n \u003cp\u003e0.558 (0.000***)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003e0.489 (0.000***)\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: ***, **, and * represent 1%, 5%, and 10% significance.\u003c/p\u003e\n\u003cp\u003eCorrelation analysis can verify the close relationship between higher education student\u0026rsquo;s GenAI acceptance and their learning motivation, learning outcomes. It does not mean that they have a significant relationship, therefore, their relationship needs to be further verified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the proposed hypotheses 1 to 3, this study employed regression analysis to test these effects. Learning effectiveness and learning outcomes were treated as dependent variables, while gender, age, grade, English examination, language environment, English learning duration, overseas study and travel experience, and higher education student\u0026rsquo;s use and understanding of GenAI tools were regarded as independent variables. Regression analyses were conducted using two models. The results are presented in Table 7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7 The effects of undergraduate\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003es use and understanding of GenAI tools on learning motivation and learning outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"57%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 22%;\"\u003e\n \u003cp\u003evariant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 38%;\"\u003e\n \u003cp\u003eMTV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 38%;\"\u003e\n \u003cp\u003eLO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003eGenAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e2.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e2.662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003eGND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e-1.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e-1.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003eAGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e-2.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e-1.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003eGRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e8.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e4.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003eEXM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e8.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e5.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003eENV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e5.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e4.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003eTIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e2.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e2.874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003eABD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e2.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e3.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 38.7755%;\"\u003e\n \u003cp\u003e106.152***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 38.7755%;\"\u003e\n \u003cp\u003e52.722***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003eAdjustment of R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 38.7755%;\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 38.7755%;\"\u003e\n \u003cp\u003e0.423\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: ***, **, and * represent 1%, 5%, and 10% significance.\u003c/p\u003e\n\u003cp\u003eModel 1 examined GenAI tools\u0026rsquo;\u0026nbsp;effect on higher education student\u0026rsquo;s learning motivation. The adjusted R\u0026sup2;value was 0.598, and the F was 106.152, It reaches statistical significance (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), which indicates that the predictor variables explained 59.8% of the variance in learning motivation. Specifically, the regression coefficient for higher education student\u0026rsquo;s use and understanding of GenAI tools was 0.078, with \u003cem\u003et\u0026nbsp;\u003c/em\u003evalue of 2.173 and \u003cem\u003ep\u003c/em\u003e value of 0.030**. It verifies Hypothesis H1 in this study: students who frequently use GenAI are significantly more motivated than those who are not. This finding supports the Technology Acceptance Model, particularly the perceived usefulness factor, which influences user\u0026apos;s attitudes and behavioral intentions toward technology[33]. If students find GenAI tools easy and convenient to use, they tend to use them for their learning. Ease of use reduces resistance to technology and lowers the threshold for usage, which enhance their learning motivation. When learners perceive that GenAI tools improve learning efficiency and effectiveness through personalized recommendations and interactive learning, they find these tools to be more useful[34].\u003c/p\u003e\n\u003cp\u003eModel 2 focused on the use and understanding of GenAI tools on student\u0026rsquo;s learning outcomes. It shows an adjusted R\u0026sup2;value of 0.423 and F of 52.722 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), which indicates that the predictor variables explained 47.1% of the variance. The regression coefficient for student\u0026rsquo;s use and understanding of GenAI tools was 0.115, with \u003cem\u003et\u003c/em\u003e value of 2.662 and \u003cem\u003ep\u003c/em\u003e value of 0.008***. These verifies Hypothesis H2: students who frequently uses GenAI tools achieve better English learning outcomes than those who uses GenAI tools with lower frequency. According to self-determination theory, GenAI tools provide personalized learning plans and self-paced learning paths. Hence, students are more motivated to learn when they feel in control of their learning. Active learning improves their learning outcomes. GenAI tools offer instant feedback and adaptive learning contents, which helps learners identify and strengthen their weaknesses and build on their strengths. As learners achieve their goals with GenAI tool\u0026rsquo;s assistance, their sense of self-confidence can be improved. Then their learning effectiveness will be reinforced because they take the initiative to learn. Additionally, GenAI tools help students to get involved in the social interactions. For example, it helps students to cross the language barriers through language translation and promote international communication and cooperation. At the meantime, dialogue robots provide a safe environment for people to better express their thoughts and emotions both in the oral and written form[35]. These social interactions enhance student\u0026rsquo;s sense of relatedness and community and boost their learning motivation and effectiveness. By fulfilling the psychological needs of autonomy, competence, and relatedness, GenAI tools promote student\u0026rsquo;s intrinsic learning motivation and improve learning effectiveness[36].\u003c/p\u003e"},{"header":"V. Discussion","content":"\u003cp\u003e \u003cb\u003eResearch Question 1: Is there any descriptive factors related to learning motivation and learning outcomes?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs presented in Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the grade level, English language proficiency, and language environment of upbringing significantly affected EFL learning motivation and learning outcomes. Higher grades were correlated with stronger positive effects of GenAI on motivation, learning outcomes. And the moderating effect of the language environment was particularly significant; learners from non-Chinese-speaking areas achieved the best learning outcomes. English proficiency also moderated learning achievement significantly, with higher English levels correlating with better learning effectiveness. Gender significantly affected learning achievement and outcomes, but had a non-significant effect on motivation. Additionally, age, frequency of weekly English use, and overseas study and travel experience positively influenced motivation, learning outcomes. These results highlight the important moderating roles of personal factors such as age, English proficiency, and the language environment in which one grows up.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch Question 2: The use and understanding of GenAI have a significant positive impact on EFL\u0026rsquo;s learning motivation?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study revealed that GenAI tools significantly enhance both intrinsic and extrinsic motivation by providing personalized learning plans, instant feedback, and interactive learning environments. This aligns with the core concepts of the TAM, where perceived usefulness is a key factor influencing learner's acceptance and use of new technologies. The perceived usefulness of GenAI tools enhances learner's motivation by increasing learning efficiency and effectiveness. Additionally, self-determination theory supports this study, indicating that GenAI tools meet learner's needs for autonomy, competence, and relevance, thereby stimulating intrinsic motivation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to Stephen Krashen's input hypothesis, learners benefit from linguistic input that is slightly above their current level, known as comprehensible input. GenAI tools provide students with personalized learning experience and tailor language input to the student's own ability and progress. This personalized input aligns with the learner\u0026rsquo;s \u0026ldquo;i\u0026thinsp;+\u0026thinsp;1\u0026rdquo; level, ensuring that they consistently receive materials that challenge them just beyond their current proficiency. To improve student\u0026rsquo;s ability, GenAI tools assist them quickly to understand and correct their mistakes by offering immediate feedback. GenAI tools dynamically adjusts their difficulty of learning materials based on learner\u0026rsquo;s interactions and performance to ensure continuous exposure to appropriately challenging input. Additionally, GenAI tools maintain learner engagement through various ways, such as online courses, apps, or virtual assistants to provide ongoing comprehensible input. GenAI tools are designed to be interactive and enjoyable, increasing learner motivation and engagement. According to the input hypothesis, language acquisition is more effective when learners are interested in and actively engaged in language input. GenAI tools, like ChatGPT, Claude and Gemini, offer rich learning resources, including video, audio, and reading materials and provide a diverse range of language inputs to help learners grasp languages from multiple perspectives and contexts.\u003c/p\u003e \u003cp\u003eWhile generative artificial intelligence brings a lot of convenience to learning, it also inevitably has some side effects,for instance, the formation of dependence psychology. Over-reliance on GenAI may weaken student\u0026rsquo;s autonomous learning ability and intrinsic learning motivation. when students are used to getting answers quickly with the help of artificial intelligence tools, they may reduce the in-depth thinking and active exploration of problems, resulting in less initiative to learn. Taking EFL academic writing as an example, if students frequently use GenAI to generate contents, they may gradually lose the ability to think and express themselves independently. In the long run, when facing learning tasks, students may first think of using external tools, instead of relying on their own knowledge and ability to solve problems, which will have a negative impact on learning motivation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch Question 3: The use and understanding of GenAI have a significant positive impact on EFL\u0026rsquo;s learning outcomes?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results showed that GenAI tools positively impact learning outcomes, especially listening comprehension, speaking, reading comprehension, and writing skills, particularly in their vocabulary[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The personalized learning paths and instant feedback mechanisms of GenAI tools allow learners to receive customized content and guidance according to their progress and abilities, enhancing learning efficiency. The interactivity and enjoyment of GenAI tools further increase learner engagement, making the learning process more vivid and engaging, thus improving learning effectiveness[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, GenAI tools help learners apply their knowledge in real-life communication scenarios, enhancing their learning performances[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. While GenAI in education shows great potential for optimizing the learning experience and improving educational effectiveness, it is crucial to guide students to use GenAI critically and consider the ethical implications of GenAI technology in education [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLikewise, it may limit creativity and cause AI anxiety. When students over-rely on AI-generated ideas and solutions, their own innovative thinking and creativity may be inhibited. For example, in the process of artistic creation or writing, if students always refer to the works generated by generative artificial intelligence, they may fall into the trap of imitation. And it is difficult to form a unique style and creativity, which will affect the learning effect and personal development. At the same time, GenAI can generate information with high pace, but this can also lead to information overload, causing anxiety and stress. Too much information makes students feel at a loss, and they do not know how to screen and deal with it. In addition, students may worry that they cannot master all the knowledge in face of technology explosion and result in AI anxiety and further reduces the learning effect.\u003c/p\u003e"},{"header":"VI. Conclusion","content":"\u003cp\u003eThis study explores the role of GenAI tools in L2 acquisition, with a focus on their impact on learning motivation and learning outcomes. It also examined the moderating effects of individual factors such as gender, age, and English learning level. The findings suggest that the use of GenAI tools significantly enhances learners' motivation and outcomes. These results align with the literature on the positive effects of GenAI applications in education.\u003c/p\u003e \u003cp\u003eThe results of this study have important implications for educational practices. Educational institutions and teachers can use GenAI tools to enhance the language learning experience and improve learning efficiency. GenAI tools can provide learners with personalized learning paths and stimulate their interest and motivation through interactive and engaging learning environments. Additionally, educational policymakers should consider integrating GenAI tools into language teaching to promote educational innovation and improve learning effectiveness.\u003c/p\u003e \u003cp\u003eThe application of GenAI tools in language learning also has broader socioeconomic implications[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. By providing a personalized and efficient learning experience, GenAI tools help to narrow the gap in the distribution of educational resources and improve educational equity [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, the widespread adoption of GenAI tools can help individuals develop globally competitive language skills and promote international exchange and cooperation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile this study strongly supports the positive role of GenAI tools in English language learning, several limitations must be noted. At first, the questionnaire data rely on respondent\u0026rsquo;s self-reports, so that they may be subject to inaccuracies or memory bias. Secondly, although variables such as gender, age, and grade were controlled for. Other variables, such as individual professional background, native language, and learning styles, may have affected the results. The study may have been conducted in specific settings or with specific learner types, which limited the generalizability of the results. Finally, the use of GenAI tools may not have been consistently controlled. With different learners possibly using GenAI in various ways, the results may be affected. Future research should consider these factors for a more comprehensive understanding.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData\u0026nbsp;Availability\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations. The\u0026nbsp;data\u0026nbsp;underlying\u0026nbsp;the\u0026nbsp;results\u0026nbsp;presented\u0026nbsp;in\u0026nbsp;the\u0026nbsp;study\u0026nbsp;are\u0026nbsp;avail-able within\u0026nbsp;the\u0026nbsp;manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflicts\u0026nbsp;of\u0026nbsp;Interest\u003c/p\u003e\n\u003cp\u003eThere is no potential conflict of interest in our paper, and allauthors have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not be-en published or submitted for publication elsewhere.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJordan, M.I. and T.M. Mitchell, Machine learning: Trends, perspectives, and prospects. Science, 2015. 349(6245): p. 255-260.\u003c/li\u003e\n \u003cli\u003eLameras, P. and S. Arnab, Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education. Information, 2022. 13(1): p. 14.\u003c/li\u003e\n \u003cli\u003eRoll, I. and R. Wylie, Evolution and Revolution in Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 2016. 26(2): p. 582-599.\u003c/li\u003e\n \u003cli\u003eSrinivasan, V., AI \u0026amp; learning: A preferred future. Computers and Education: Artificial Intelligence, 2022. 3: p. 100062.\u003c/li\u003e\n \u003cli\u003eCope, B., M. Kalantzis and D. 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Learning, Media and Technology, 2020. 45(3): p. 298-311.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"GenAI, EFL, learning motivation, learning outcome","lastPublishedDoi":"10.21203/rs.3.rs-5144171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5144171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe purpose of this paper is to explore the role of GenAI (generative artificial intelligence) in EFL. Specifically, this paper focuses on its impact on learning motivation and outcome. With a total of five hundred and sixty-six students in four Chinese university, the questionnaire was shown to be reliable and valid. Research has revealed that GenAI has a strong effect on EFL learning motivation and outcomes in China; that GenAI plays an important role in learners\u0026rsquo; personalized learning, learning interest and motivation through timely feedback and strong interactivity; and GenAI can enhance learner\u0026rsquo;s English listening and speaking skills, academic writing skills, vocabulary expansion and understanding of English-speaking countries' culture. The paper also revealed that personal factors such as grade, English proficiency and language environment in which students grew up have a strong effect on English learning outcomes. The research findings widens our understanding of Chinese student\u0026rsquo;s use of artificial intelligence in second language acquisition, and also reflect Chinese student\u0026rsquo;s AI anxiety in the era of information technology.\u003c/p\u003e","manuscriptTitle":"The Role of GenAI in EFL: Impact on Learning Motivation and Outcome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 08:00:33","doi":"10.21203/rs.3.rs-5144171/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":"5af280f0-9382-487f-a399-83d729ceed63","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39014122,"name":"Biological sciences/Psychology"},{"id":39014123,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2025-12-16T17:08:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 08:00:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5144171","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5144171","identity":"rs-5144171","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}