Enhancing nursing education: An AI-powered Chatbots for fostering engagement and higher-order thinking skills

Enhancing nursing education: An AI-powered Chatbots for fostering engagement and higher-order thinking skills | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing nursing education: An AI-powered Chatbots for fostering engagement and higher-order thinking skills Ruowei Wang, Arumugam Raman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6372448/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background AI-powered chatbots are increasingly integrated into educational settings, offering opportunities to enhance student engagement and higher-order thinking skills (HOTS). However, limited research exists on their role in nursing education, especially in China. This study aimed to explore how AI-powered chatbots impact nursing students’ engagement and the development of HOTS, mediated by feedback quality (FBQ) and self-regulated learning (SRL). Methods A cross-sectional, quantitative research design was employed to investigate the interplay between perceived usefulness (PUC), ease of use (EoU), engagement (ENG), FBQ, SRL, and HOTS. 470 nursing students from different academic years participated in the study. Data were collected using a structured survey measuring six key constructs. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to evaluate the direct and indirect relationships within the conceptual framework. Results PUC and EoU significantly influenced ENG, which, in turn, strongly mediated the effects of FBQ and SRL on HOTS. ENG had a substantial impact on FBQ (β = 0.852) and SRL (β = 0.892), while the combined indirect effect of FBQ and SRL on HOTS (β = 0.798) demonstrated the critical role of these mediators. The study also confirmed that intuitive chatbot design and high-quality, timely feedback are essential for fostering cognitive skills. Conclusions AI-powered chatbots show promise in enhancing engagement and supporting the development of higher-order thinking skills in nursing education. The findings emphasize the need for scalable, user-friendly chatbot systems tailored to educational contexts. Future research should focus on advanced feedback algorithms, long-term impacts on clinical competency, and scalability in diverse learning environments. AI-powered chatbots Student engagement Higher-order thinking skills (HOTS) Perceived usefulness (PUC) Ease of use (EoU) Feedback quality (FBQ) Self-regulated learning (SRL) Figures Figure 1 Figure 2 Background Chatbots in education have gained significant momentum in recent years, thanks to advancements in artificial intelligence (AI) that have made these tools more interactive and adaptable for teaching and learning [ 1 ]. In healthcare education—where knowledge is vast and constantly evolving—chatbots offer a unique solution by providing students with on-demand access to resources, information, and personalized support [ 2 – 4 ]. Nursing education, in particular, benefits from these innovations, as nursing students must balance mastering theoretical concepts with acquiring practical skills to prepare for their demanding roles. In China, the demand for skilled healthcare professionals has surged due to ageing populations, rapid urbanization, and a growing burden of chronic diseases [ 5 , 6 ]. As a result, nursing education is under pressure to produce highly competent graduates who can meet these challenges. While traditional teaching methods have been effective, they often require students to quickly learn a large amount of complex knowledge and passively absorb knowledge points, and may fall short in addressing students' personalized learning needs and the cultivation of higher-order thinking skills [ 7 , 8 ]. With their ability to deliver instant, individualized feedback and support, Chatbots present a promising supplementary or auxiliary solution [ 9 – 11 ]. In addition, nursing students are faced with the continuous updating of medical and health knowledge. In this context, chatbots have proven effective in fostering self-directed learning, enhancing engagement, and promoting critical thinking skills [ 12 – 16 ]. In China, the adoption of AI chatbots in nursing education is gradually growing as educational institutions leverage technology to improve student outcomes [ 17 ]. AI-powered virtual assistants are increasingly being integrated into healthcare education to enhance student learning and engagement. For example, iFlytek, a leading AI company, has developed advanced educational tools such as the T10 AI Learning Machine, which provides personalized learning experiences and AI-assisted grading in subjects like English and medical sciences [ 18 ]. While Xiaoyi, an AI developed by iFlytek, made history by passing China’s National Medical Licensing Examination in 2017, its primary role is to assist doctors in analyzing patient information rather than directly training nursing students [ 19 ]. Additionally, AI-driven platforms are being used to support nursing education through interactive tutoring systems, personalized quizzes, and chatbot-based feedback mechanisms, helping students strengthen clinical reasoning and self-regulated learning [ 20 ]. These tools offer round-the-clock access to educational resources, addressing key challenges in medical and nursing education by providing scalable and adaptive learning solutions. Despite their potential, the integration of chatbots into nursing education in China is still relatively limited [ 21 ]. Factors such as technological infrastructure, faculty readiness, and students’ perceptions of chatbots play a significant role in determining how effectively they are adopted into curricula [ 22 – 24 ]. Cultural attitudes toward technology and education also shape how these tools are used in classrooms and clinical training environments [ 23 ]. Chatbots could transform the learning experience by providing instant feedback, personalized assistance, and easily accessible resources. Their success, however, relies heavily on the quality of their design and the way they are implemented within educational frameworks. While chatbots hold the promise of boosting student engagement and fostering higher-order thinking skills (HOTS)—such as critical thinking, problem-solving, and creativity [ 25 – 27 ]—realizing this potential requires careful study of the factors that influence their effectiveness. The success of chatbot-based learning largely depends on two key factors: perceived usefulness (PUC) and ease of use (EoU), as outlined in the Technology Acceptance Model (TAM) [ 28 ]. Perceived usefulness reflects whether students believe chatbots can enhance their academic performance and help them achieve learning goals. At the same time, ease of use focuses on how intuitive and accessible chatbots are for users with varying technical skills. Beyond these core elements, feedback quality (FBQ) and self-regulated learning (SRL) act as mediating factors that transform engagement into meaningful learning outcomes. High-quality feedback ensures students receive timely, constructive responses to refine their understanding, while self-regulated learning encourages students to plan, monitor, and reflect on their educational journeys [ 29 , 30 ]. Together, these factors create a comprehensive framework for understanding how chatbots can enhance learning experiences. Each element plays a distinct but interconnected role in improving chatbot-based learning. Perceived usefulness motivates students to engage with chatbots they believe will help them succeed academically, such as by improving problem-solving or providing easy access to resources [ 31 – 34 ]. Ease of use reduces the frustration of poorly designed systems, ensuring students can navigate chatbot interfaces effortlessly [ 35 , 36 ]. Engagement bridges usability and learning outcomes by involving students emotionally, behaviorally, and cognitively in their studies [ 37 ]. Feedback quality adds value to engagement by offering actionable, personalized insights tailored to individual learning needs [ 10 , 38 , 39 ]. Finally, self-regulated learning strengthens the link between engagement and higher-order thinking skills (HOTS) by promoting proactive and reflective learning behaviors essential for academic success [ 40 , 41 ]. Despite their potential, chatbots in education still face several challenges. One major issue is the inconsistency of feedback quality. Many chatbots struggle to provide timely or nuanced responses, hindering students’ ability to engage deeply with the content [ 42 ]. Poor interfaces can also deter students—especially those with limited technical skills—from using the tools regularly [ 10 , 43 ]. Another hurdle is that not all students have strong self-regulation skills, limiting their ability to benefit from chatbot interactions fully [ 30 ]. Moreover, chatbots often fail to deliver the sophisticated support needed to develop higher-order cognitive skills like critical thinking, analysis, and creativity [ 44 , 45 ]. To tackle these challenges, several solutions have been proposed. For instance, improving chatbot interfaces to be more user-friendly and intuitive helps increase accessibility and encourages consistent use [ 46 , 47 ]. Advanced AI-driven personalization in feedback mechanisms ensures students receive tailored responses that align with their specific learning needs [ 25 ]. Chatbots can also promote self-regulated learning by providing structured prompts that guide students in planning, monitoring, and reflecting on their progress [ 48 – 50 ]. These measures aim to close the gap between student engagement and the development of higher-order thinking skills, ensuring every interaction with a chatbot contributes to meaningful learning outcomes. However, some challenges remain. Customizing feedback to address the diverse needs of students and supporting the development of higher-order cognitive skills continues to be a struggle [ 29 ]. Scalability is another issue, as implementing personalized features often requires advanced AI models that may not be feasible for large student populations [ 10 , 51 ]. Additionally, while individual factors like PUC and EoU are well-documented, there is a lack of comprehensive models that integrate these elements and mediating factors like FBQ and SRL into a unified framework. In summary, while significant progress has been made in addressing usability and engagement challenges, there is still much to learn about fully leveraging chatbots in education [ 17 , 52 , 53 ]. Combining perceived usefulness, ease of use, feedback quality, and self-regulated learning into a unified framework is essential to maximize the impact of chatbots on student engagement and learning outcomes. Bridging these gaps will pave the way for designing effective, user-friendly, and scalable chatbot systems that adapt to diverse educational contexts. Literature Review According to the Technology Acceptance Model (TAM) introduced by F Davis [ 28 ], individuals are more likely to adopt technology when they perceive it as beneficial to their performance. Chatbots are highly useful in education because they provide timely feedback, personalized learning experiences, and immediate support, fostering deeper student engagement [ 54 – 56 ]. Studies show that when students view chatbots as tools that improve outcomes like critical thinking and problem-solving, they are more willing to engage with them [ 2 , 57 , 58 ]. This is particularly true in higher education, where chatbots promote efficiency and self-directed learning [ 58 ]. Frequent interactions with chatbots perceived as helpful also lead to greater behavioral and cognitive engagement [ 24 , 59 ]. The concept of perceived usefulness (PUC), introduced by F Davis [ 28 ] in the Technology Acceptance Model (TAM), posits that individuals are more likely to adopt and engage with a technology if they believe it enhances their performance. In the context of chatbots in education, perceived usefulness has been shown to influence student engagement significantly. Research indicates that students find chatbots useful because they provide timely feedback, personalized learning experiences, and immediate assistance, fostering deeper engagement [ 10 ]. Moreover, according to MA Almaiah, A Al-Khasawneh and A Althunibat [ 57 ], when students perceive chatbots as a tool for improving academic performance, such as critical thinking and problem-solving, their willingness to use them is boosted. In university-level educational environments, for example, chatbots make students' studies efficient and allow them to learn at their own pace [ 17 ]. Similarly, according to S Wollny, J Schneider, D Di Mitri, J Weidlich, M Rittberger and H Drachsler [ 58 ], students who perceived chatbots in a positive light and saw them as beneficial for them, then such students increased in terms of cognitive and behavioral engagement through direct use of chatbots at a high level. Ease of use (EOU), another key TAM construct, refers to how effortless and intuitive technology is to use [ 28 ]. Research shows that user-friendly chatbot interfaces reduce cognitive load, allowing students to focus on learning rather than navigating complex systems [ 60 , 61 ]. A Khamaj [ 47 ] found that intuitive chatbot designs significantly enhance engagement by encouraging repeated use and minimizing frustration. Accessible interfaces are especially beneficial for students with limited technical skills, ensuring inclusivity and broadening engagement [ 10 , 62 ]. In addition, in the context of chatbots, G-J Hwang, S-Y Wang and C-L Lai [ 63 ] observed that students who found the chatbot interface intuitive and easy to navigate were more likely to engage actively. User-friendly designs encourage repetitive use, fostering behavioral engagement while reducing frustration increases emotional engagement [ 17 ]. The interrelationship between ease of use and perceived usefulness is supported in educational technology research. V Venkatesh and FD Davis [ 64 ] extended the Technology Acceptance Model (TAM) and confirmed through experiments that ease of use indirectly impacts engagement (ENG) by enhancing perceived usefulness. For instance, when a chatbot is easy to use, students will perceive it as beneficial, leading to higher engagement. This indirect effect highlights the importance of designing chatbots that are both user-friendly and valuable in educational settings. O Zawacki-Richter, VI Marín, M Bond and F Gouverneur [ 25 ] reinforce this perspective, demonstrating that technologies perceived as both easy to use and useful promote higher cognitive and behavioral engagement. Specifically, chatbots that meet these criteria attract initial usage and sustain long-term engagement as students integrate them into their learning routines. Perceived ease of use and perceived usefulness are fundamental constructs that directly influence chatbot adoption in educational contexts. Existing research underscores the necessity of developing functional and intuitive chatbots to maximize their impact on student engagement. As academic institutions increasingly implement AI-driven technologies, understanding these relationships is crucial for enhancing learning outcomes and fostering meaningful interactions with digital tools. High-quality feedback (FBQ) is essential for effective learning, as it helps students improve performance by clarifying goals and refining understanding [ 29 ]. Feedback quality bridges engagement and higher-order thinking skills (HOTS) in chatbot-based learning by transforming active participation into meaningful cognitive outcomes [ 65 ]. Timely, personalized, and constructive feedback has fostered skills like critical thinking and problem-solving [ 10 , 25 ]. In chatbot-facilitated learning, feedback quality mediates the relationship between engagement and higher-order thinking skills by transforming active engagement into meaningful learning experiences [ 17 ]. For example, chatbots that provide tailored responses amplify the impact of engagement on cognitive skills, enabling students to analyse, evaluate, and create effectively [ 66 ]. Self-regulated learning (SRL) refers to learners’ ability to plan, monitor, and evaluate their progress [ 30 ]. In chatbot-assisted environments, SRL mediates engagement and HOTS by channelling active participation into strategic learning behaviors [ 40 , 41 , 66 ]. Chatbots promote SRL by offering guidance and encouraging reflection. For instance, DH Chang, MP-C Lin, S Hajian and QQ Wang [ 67 ] observed that chatbots improve students’ goal-setting and self-monitoring skills, essential for critical thinking and creativity. In chatbot-assisted learning environments, SRL acts as a mediator between engagement and higher-order thinking skills by channeling active participation into strategic learning behaviors [ 68 ]. Empirical studies have shown that chatbots promote SRL through reflection and scaffolding. For example, G-J Hwang, S-Y Wang and C-L Lai [ 63 ] observed that students who interacted with chatbots demonstrated improved self-regulation and goal-setting behaviors, which in turn enhanced their critical thinking and creativity. Similarly, P Smutny and P Schreiberova [ 10 ] found that chatbots facilitate iterative learning processes, allowing students to develop and refine their problem-solving strategies and understanding over time. Higher-order thinking skills (HOTS), which include analyzing, evaluating, and creating, are crucial for academic success and problem-solving [ 69 ]. These skills, situated at the top of Bloom’s Taxonomy [ 70 ], are necessary for tackling complex problems and fostering innovation. Chatbots have shown significant potential in supporting HOTS by providing interactive and adaptive learning experiences [ 25 ]. Engagement is key to developing HOTS, as active participation encourages deeper cognitive processing. JA Fredricks, PC Blumenfeld and AH Paris [ 37 ] found that engaged students are likelier to analyse and synthesize information, core elements of critical thinking. Chatbots enhance these processes through timely feedback and scaffolding, which help students refine their understanding [ 29 ]. Moreover, creativity—an integral aspect of HOTS—is supported by chatbots through activities like brainstorming and open-ended prompts[ 26 , 27 ]. Problem-solving skills are also nurtured as chatbots simulate real-world scenarios, guiding students through iterative processes to develop solutions [ 71 ]. While existing studies have explored the relationships between PUC, EoU, engagement, feedback quality, SRL, and HOTS, gaps remain in understanding how these elements interact within an integrated framework. Most research examines individual components, such as engagement or feedback, without fully addressing their combined or mediating roles in fostering HOTS. This highlights the need for comprehensive research that investigates these relationships holistically, including both direct and indirect effects. Such research could provide actionable insights for designing and optimizing chatbot-based learning environments to maximize their impact on student outcomes. Theoretical Framework This research draws on several theoretical frameworks to examine the relationships among perceived usefulness (PUC), ease of use (EoU), engagement, feedback quality, self-regulated learning, and higher-order thinking skills (HOTS). At its core is the Technology Acceptance Model (TAM), which emphasizes that perceived usefulness and ease of use drive technology adoption and sustained engagement [ 72 ]. F Davis [ 28 ] suggests that when students view chatbots as helpful and easy to use, their engagement increases, creating opportunities for transformative learning. Building on TAM, engagement theory highlights the importance of meaningful interaction, asserting that active cognitive, emotional, and behavioral involvement connects usability perceptions with mediators like feedback quality and self-regulated learning, ultimately influencing HOTS. J Hattie and H Timperley [ 29 ]’ feedback model further explains how effective feedback enhances learning by clarifying goals, identifying areas for improvement, and encouraging deeper understanding through iterative processes. BJ Zimmerman [ 30 ]’ self-regulated learning (SRL) theory adds another layer, emphasizing how planning, monitoring, and evaluating learning processes mediate the link between engagement and cognitive outcomes. Lastly, Bloom’s revised taxonomy [ 70 ] provides a framework for understanding the development of higher-order cognitive skills, such as analyzing, evaluating, and creating—outcomes that arise from meaningful engagement, high-quality feedback, and self-regulated learning. Together, these frameworks illuminate how chatbots can facilitate advanced learning processes and outcomes. Hypothesis Based on the above discussion, this study proposed the following hypothesis: H1: Perceived usefulness of chatbots (PUC) has a positive direct effect on engagement (ENG). H2: Ease of use of chatbots (EoU) has a positive direct effect on engagement (ENG). H3: Engagement (ENG) has a positive direct effect on feedback quality (FBQ). H4: Engagement (ENG) has a positive direct effect on self-regulated learning (SRL). H5: Feedback quality (FBQ) mediates the relationship between engagement (ENG) and higher-order thinking skills (HOTS). H6: Self-regulated learning (SRL) mediates the relationship between engagement (ENG) and higher-order thinking skills (HOTS). The conceptual framework of this study was showed in Fig. 1 . Methods Study Design This study used a quantitative research design to explore the relationships among perceived usefulness (PUC), ease of use (EoU), engagement (ENG), feedback quality (FBQ), self-regulated learning (SRL), and higher-order thinking skills (HOTS). Data was collected through a cross-sectional survey, which allowed for the simultaneous measurement of these factors and the identification of their interrelationships. The study employed Structural Equation Modeling (SEM) to analyse the hypothesized relationships. This method would evaluate direct and indirect effects within the conceptual framework, providing a comprehensive understanding of how these constructs interact. Participants and sample size The target population for this study includes nursing students from various academic levels at the University X, which offers Nursing Education in China, all of whom have experience using educational chatbots as part of their coursework. Stratified random sampling was employed to ensure diverse representation, with strata based on academic year (e.g., first-year, final-year students). This approach minimized sampling bias and captured differences in students’ experiences and perspectives. A minimum sample size of 500 participants was determined following the recommendations for Structural Equation Modeling (SEM) [ 73 ], ensuring sufficient statistical power for detecting relationships and validating the model. A larger sample was targeted to account for non-responses or incomplete data. Participants were recruited through institutional email lists, learning management systems, and direct communication with faculty. Information about the study’s purpose, confidentiality measures, and voluntary participation was provided to obtain informed consent. Participants were randomly selected within each stratum using computer-generated random numbers to ensure an unbiased selection process. Instruments A structured questionnaire was developed to measure six key constructs in the study: Perceived Usefulness (PUC), Ease of Use (EoU), Engagement (ENG), Feedback Quality (FBQ), Self-Regulated Learning (SRL), and Higher-Order Thinking Skills (HOTS). The items for PUC and EoU were adapted from F Davis [ 28 ] and V Venkatesh and FD Davis [ 64 ], while ENG was measured using the Student Engagement Scale [ 37 ]. FBQ items were custom-developed based on J Hattie and H Timperley [ 29 ]’ feedback model, and SRL was assessed using BJ Zimmerman [ 30 ]’ self-regulated learning framework. HOTS was measured with rubrics aligned with Bloom’s Taxonomy [ 70 ], focusing on cognitive skills such as analysis and creation. All items were measured on a 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree) to ensure consistency and ease of response. The constructs capture specific aspects of the study, including system performance (PUC), usability (EoU), engagement levels (ENG), feedback clarity and relevance (FBQ), self-regulation strategies (SRL), and advanced cognitive skills (HOTS), ensuring a comprehensive assessment of the relationships under investigation. The summary of constructs, measurement items, and sources was shown in Table 1 , For more details on the questionnaire, please see supplementary File 1. Table 1 Summary of constructs, measurement items, and sources Construct Description Source Initial Items Retained Items Response Options Perceived Usefulness (PUC) Measures the extent to which users believe using the system improves their performance F Davis [ 28 ]; V Venkatesh and FD Davis [ 64 ] 3 3 1 = Strongly Disagree, 7 = Strongly Agree Ease of Use (EoU) Assesses how simple and intuitive users find the system to operate. F Davis [ 28 ]; V Venkatesh and FD Davis [ 64 ] 5 5 1 = Strongly Disagree, 7 = Strongly Agree Engagement (ENG) Evaluates the cognitive, emotional, and behavioral aspects of Engagement. JA Fredricks, PC Blumenfeld and AH Paris [ 37 ] 5 5 1 = Strongly Disagree, 7 = Strongly Agree Feedback Quality (FBQ) Measures the clarity, relevance, and timeliness of feedback received. J Hattie and H Timperley [ 29 ]; Custom items 4 4 1 = Strongly Disagree, 7 = Strongly Agree Self-Regulated Learning (SRL) Examines strategies like goal setting, self-monitoring, and self-reflection. BJ Zimmerman [ 30 ] 5 5 1 = Strongly Disagree, 7 = Strongly Agree Higher-Order Thinking Skills (HOTS) Captures cognitive skills like analysis, evaluation, and creation. LW Anderson and DR Krathwohl [ 70 ] 8 8 1 = Strongly Disagree, 7 = Strongly Agree Validity and Reliability To ensure content validity, a panel of experts from local nursing institutions and public universities specializing in educational technology and psychometrics reviewed the questionnaire. Construct validity will be assessed using Convergent Validity and Discriminant Validity via SmartPLS 4. In Partial Least Squares Structural Equation Modeling (PLS-SEM), construct validity is evaluated through specific tests rather than traditional Confirmatory Factor Analysis (CFA). Convergent validity will be determined by examining the Average Variance Extracted (AVE) for each construct; an AVE greater than 0.50 indicates that a construct explains over 50% of the variance in its indicators, which is considered acceptable [ 73 ]. Individual item factor loadings above 0.70 will also be deemed adequate. Reliability will be tested using Composite Reliability (CR) and Cronbach’s Alpha, with CR values above 0.70 indicating internal consistency, supported further by Cronbach’s Alpha [ 74 ]. Discriminant validity will be measured using the Fornell-Larcker criterion. The Fornell-Larcker criterion requires that the square root of a construct's AVE be greater than its correlations with other constructs [ 74 ]. Data Collection and Analysis Data was collected using an online survey platform to ensure accessibility and convenience for participants. The researchers sent 500 online survey forms to nursing students from years 1 to 4. A total of 480 forms were answered and submitted. However, 10 partially answered forms were excluded from the study. Finally, 470 participants were involved in this study, with a response rate of 94%. The data is exported to SPSS software to save. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypothesised relationships, with the conceptual model evaluated through SmartPLS 4, a powerful tool for handling complex models with multiple constructs and maximizing explained variance [ 73 ]. The Standardized Root Mean Square Residual (SRMR) was reported to assess the overall model fit. SRMR is widely recognized as an appropriate fit index in PLS-SEM [ 74 ]. Although traditional fit indices like the Comparative Fit Index (CFI) and Root Mean Square Error of Approximation (RMSEA) are standard in covariance-based SEM, SRMR is more suitable for PLS-SEM and provides insights into the adequacy of the model. Results Table 2 presents an evaluation of the reliability and validity of the measurement model, confirming that all constructs meet or exceed recommended thresholds for internal consistency and convergent validity. Factor loadings range from 0.871 to 0.958, well above the 0.70 threshold [ 73 ], indicating strong item reliability. Composite Reliability (CR) values exceed 0.90 for all constructs, reflecting excellent internal consistency, with Higher-Order Thinking Skills (HOTS) achieving the highest CR value of 0.977. Cronbach’s Alpha (α) values also demonstrate strong reliability, ranging from 0.924 to 0.973 across constructs. The Average Variance Extracted (AVE) values surpass the critical threshold of 0.50, with values ranging from 0.809 for Engagement (ENG) to 0.868 for Perceived Usefulness (PUC), confirming that the constructs explain a substantial proportion of variance in their indicators. Self-Regulated Learning (SRL) and Feedback Quality (FBQ) exhibit exceptionally high AVE values of 0.862 and 0.860, respectively, underscoring their decisive contributions to the model. These results confirm the measurement model's reliability, validity, and suitability for assessing the study’s constructs. Table 2 Loadings, composite reliability (CR), Cronbach’s alpha, average variance extracted (AVE) Construct Loadings Composite Reliability (CR) Cronbach’s Alpha (α) Average Variance Extracted (AVE) PUC 0.952 0.924 0.868 puc1 0.913 puc2 0.958 puc3 0.923 EoU 0.964 0.953 0.842 eou1 0.913 eou2 0.939 eou3 0.918 eou4 0.911 eou5 0.908 ENG 0.941 0.941 0.809 eng1 0.871 eng2 0.882 eng3 0.920 eng4 0.926 eng5 0.896 SRL 0.969 0.960 0.862 srl1 0.940 srl2 0.938 srl3 0.934 srl4 0.925 srl5 0.906 FBQ 0.961 0.945 0.860 fbq1 0.911 fbq2 0.944 fbq3 0.945 fbq4 0.908 HOTS 0.977 0.973 0.842 hots1 0.898 hots2 0.918 hots3 0.933 hots4 0.923 hots5 0.915 hots6 0.925 hots7 0.910 hots8 0.917 The Fornell-Larcker Criterion results (Table 3 ) confirm the discriminant validity of the measurement model, as the square root of the Average Variance Extracted (AVE) for each construct (diagonal values) is higher than its correlations with other constructs (off-diagonal values), in line with C Fornell and DF Larcker [ 75 ]’ recommendations. For instance, the square root of AVE for Engagement (ENG) is 0.899, exceeding its highest correlation with Self-regulated Learning (SLR) at 0.892, indicating that ENG explains more variance in its indicators than in its relationships with other constructs. Similarly, the square root of AVE for Self-Regulated Learning (SRL) is 0.929, more significant than its correlation with Feedback Quality (FBQ) at 0.899, further supporting discriminant validity. The strongest correlations appear between SLR and FBQ (0.899) and FBQ and HOTS (0.912), reflecting their interdependence in the learning process. Meanwhile, Perceived Usefulness (PUC) shows weaker correlations with HOTS (0.687) and FBQ (0.701), suggesting it exerts an indirect effect. Overall, these findings validate the distinctiveness of the constructs while emphasizing the interconnected roles of engagement, feedback quality, and self-regulated learning in promoting higher-order thinking skills. Table 3 Fornel -Larcker criterion Constructs ENG EoU FBQ HOTS PUC SRL ENG 0.899 0.804 0.852 0.840 0.711 0.892 EoU 0.804 0.918 0.773 0.749 0.829 0.833 FBQ 0.852 0.773 0.927 0.912 0.701 0.899 HOTS 0.840 0.749 0.912 0.918 0.687 0.872 PUC 0.711 0.829 0.701 0.687 0.932 0.737 SRL 0.892 0.833 0.899 0.872 0.737 0.929 Table 4 summarizes the f² analysis, highlighting the significant effects of various constructs within the model. Engagement (ENG) emerges as a central factor, exerting a very large effect on both Feedback Quality (FBQ) (f² = 2.656) and Self-Regulated Learning (SRL) (f² = 3.874), underscoring its crucial role in shaping feedback perceptions and promoting self-regulated learning behaviors. Ease of Use (EoU) shows a moderate to large effect on ENG (f² = 0.424), confirming that usability is vital in enhancing student engagement. FBQ has a moderate to large effect on Higher-Order Thinking Skills (HOTS) (f² = 0.470), emphasizing the importance of high-quality feedback in fostering critical thinking. In contrast, ENG → HOTS (f² = 0.030) and SRL → HOTS (f² = 0.025) exhibit minor direct effects, suggesting that their impact on higher-order thinking is predominantly mediated through other pathways. Similarly, Perceived Usefulness (PUC) has a negligible effect on ENG (f² = 0.019), indicating that its influence may be more supportive than direct. These results demonstrate the robustness of the model, with ENG serving as the central driver of significant outcomes. Supported by EoU, FBQ, and SRL, ENG optimizes learning outcomes and facilitates the development of critical thinking skills, highlighting its pivotal role in the learning process. Table 4 f Square, effect size and interpretations Predictor→ Outcome f² Value Effect Size Interpretation ENG → FBQ 2.656 Very Large Engagement has a powerful effect on Feedback Quality, emphasizing its critical role in improving feedback. ENG → HOTS 0.030 Small Engagement has a small direct effect on Higher-Order Thinking Skills, suggesting indirect mediation through other constructs like FBQ and SRL. ENG → SRL 3.874 Very Large Engagement has a powerful effect on Self-Regulated Learning, highlighting its importance in fostering self-regulation. EoU → ENG 0.424 Medium to Large Ease of Use has a moderate to significant effect on Engagement, indicating usability significantly influences engagement. FBQ → HOTS 0.470 Medium to Large Feedback Quality has a moderate to significant effect on Higher-Order Thinking Skills, demonstrating its role in fostering critical thinking. PUC → ENG 0.019 Small Perceived Usefulness has a negligible effect on Engagement, indicating a limited direct impact. SRL → HOTS 0.025 Small Self-Regulated Learning has a small direct effect on Higher-Order Thinking Skills, suggesting its influence is amplified through indirect pathways. Table 5 shows the results from the SmartPLS bootstrapping analysis, which confirm that all hypothesized relationships are significant and support the proposed theoretical model. Perceived Usefulness (PUC) has a small but significant positive effect on Engagement (ENG) (β = 0.143, p = 0.040). In contrast, Ease of Use (EoU) has a strong positive effect on ENG (β = 0.685, p = 0.000), highlighting the critical roles of usability and perceived utility in driving user engagement. ENG significantly influences both Feedback Quality (FBQ) (β = 0.852, p = 0.000) and Self-Regulated Learning (SRL) (β = 0.892, p = 0.000), underscoring its pivotal role in enhancing feedback quality and fostering self-regulated learning behaviors. Mediation effects were also significant, with ENG → FBQ → HOTS showing a slight but notable effect (β = 0.154, p = 0.019) and ENG → SRL → HOTS demonstrating a slightly more substantial effect (β = 0.169, p = 0.036). These findings confirm the central role of engagement in improving learning outcomes both directly and indirectly, mediated through feedback quality and self-regulation learning. Overall, the results validate the theoretical framework and emphasize the interconnected roles of usability, engagement, feedback, and self-regulation learning in fostering higher-order thinking skills. Table 5 Measurement model, p-value and decision Hypothesis Path Path Coefficient (β) p-value Decision H1 PUC → ENG (Perceived Usefulness → Engagement) 0.143 0.040 Supported (Significant) H2 EoU → ENG (Ease of Use → Engagement) 0.685 0.000 Supported (Significant) H3 ENG → FBQ (Engagement → Feedback Quality) 0.852 0.000 Supported (Significant) H4 ENG → SRL (Engagement → Self-Regulated Learning) 0.892 0.000 Supported (Significant) H5 ENG → FBQ → HOTS (Mediation Effect) 0.154 0.019 Supported (Significant) H6 ENG → SRL → HOTS (Mediation Effect) 0.169 0.036 Supported (Significant) Figure 2 illustrates the structural model and the relationships among Perceived Usefulness (PUC), Ease of Use (EoU), Engagement (ENG), Feedback Quality (FBQ), Self-Regulated Learning (SRL), and Higher-Order Thinking Skills (HOTS). Path coefficients (β) reveal significant relationships, with EoU → ENG (β = 0.685) showing a strong effect, while PUC → ENG (β = 0.143) has a more minor but significant impact. Engagement plays a central role, exerting substantial effects on FBQ (β = 0.852) and SRL (β = 0.892), mediating its influence on HOTS. Indirect effects through FBQ (β = 0.154) and SRL (β = 0.169) amplify the total effect of ENG on HOTS (β = 0.952), confirming partial mediation. The R² values reflect strong explanatory power, with HOTS (R² = 0.850) primarily explained by ENG, FBQ, and SRL, while ENG significantly influences FBQ (R² = 0.727) and SRL (R² = 0.795). Indicator loadings, such as PUC1 = 0.913 and ENG3 = 0.920, exceed the 0.70 threshold, confirming the reliability of the measurement model. The figure highlights the pivotal role of ENG in driving learning outcomes, with FBQ and SRL amplifying its impact on HOTS. These findings validate the model’s strong predictive power and alignment with the theoretical framework. Table 6 presents the mediation analysis, confirming that the relationship between Engagement (ENG) and Higher-Order Thinking Skills (HOTS) is partially mediated by both Feedback Quality (FBQ) and Self-Regulated Learning (SRL). The direct effect of ENG on HOTS is β = 0.154 (p = 0.019), while the indirect effect through FBQ and SRL is substantial (β = 0.798), resulting in a total effect of β = 0.952. This indicates that while ENG has a direct influence on HOTS, a significant portion of its impact is mediated by these constructs, confirming partial mediation. Specifically, the pathway ENG → FBQ → HOTS has an indirect effect of β = 0.154, highlighting the significant role of feedback quality in mediating engagement’s effect on higher-order thinking. Similarly, the pathway ENG → SRL → HOTS exhibits a slightly stronger indirect effect of β = 0.169, emphasizing the critical role of self-regulated learning as a mediator. Between the two mediators, SRL demonstrates a slightly greater influence than FBQ. The analysis also reveals full mediation for predictors like Perceived Usefulness (PUC) and Ease of Use (EoU) on HOTS. The pathway PUC → ENG → HOTS shows an indirect effect of β = 0.022, while EoU → ENG → HOTS has a stronger indirect effect of β = 0.106, both entirely mediated through engagement. These findings underscore the pivotal roles of FBQ and SRL in enhancing HOTS, while constructs like PUC and EoU influence HOTS indirectly through engagement. The results validate the interconnected pathways in the model, highlighting the importance of feedback quality and self-regulation in fostering cognitive outcomes. Additionally, model fit indices were evaluated to assess how well the structural equation model (SEM) aligns with the observed data, ensuring the relationships specified in the model accurately reflect the theoretical framework. Table 6 Mediation analysis matrix Path Direct Effect (β) Indirect Effect (β) Total Effect (β) Decision ENG → HOTS 0.154 0.798 (via FBQ & SRL) 0.952 Partial Mediation ENG → FBQ → HOTS - 0.154 0.154 Supported (Mediation) ENG → SRL → HOTS - 0.169 0.169 Supported (Mediation) PUC → ENG → HOTS - 0.143 × 0.154 = 0.022 0.022 Full Mediation EoU → ENG → HOTS - 0.685 × 0.154 = 0.106 0.106 Full Mediation ENG → FBQ 0.852 - 0.852 Direct Effect ENG → SRL 0.892 - 0.892 Direct Effect FBQ → HOTS 0.629 - 0.629 Direct Effect SRL → HOTS 0.169 - 0.169 Direct Effect Table 7 compares the model fit indices for the saturated (perfectly fitting) model and the estimated model. The SRMR (Standardized Root Mean Square Residual) for the estimated model is 0.063, within the acceptable range (below 0.08), indicating a reasonable fit, though slightly worse than the saturated model (0.033). The d_ULS (1.841) and d_G (0.967) values for the estimated model are higher than those of the saturated model (0.498 and 0.809, respectively), suggesting minor model misspecifications. The Chi-square value for the estimated model (2345.283) is also higher than that of the saturated model (2157.606), reflecting some deviation between the observed and model-implied covariance matrices. Lastly, the NFI (Normed Fit Index) for the estimated model is 0.883, slightly below the commonly accepted threshold of 0.90. While these results indicate that the model has an acceptable fit, they also highlight areas where refinement could improve its alignment with the data. Table 7 Model fit indices: comparison of saturated and estimated models Fit Index Saturated Model Estimated Model SRMR 0.033 0.063 d_ULS 0.498 1.841 d_G 0.809 0.967 Chi-Square 2157.606 2345.283 NFI 0.893 0.883 Discussion This study highlights the transformative potential of thoughtfully designed AI-powered chatbots in enhancing nursing education, particularly by fostering engagement and higher-order thinking skills (HOTS). Consistent with the Technology Acceptance Model (TAM), Perceived Usefulness (PUC) and Ease of Use (EoU) emerged as crucial drivers of Engagement (ENG). Nursing students who found chatbots intuitive and beneficial demonstrated greater motivation and active participation in their learning, underscoring the importance of user-centric designs tailored to meet the unique demands of nursing education, such as intuitive interfaces and practical clinical applications. Aligned with TAM [ 28 , 64 ], the positive effects of PUC and EoU on engagement reflect how intuitive and functional chatbot designs can support nursing students in managing high academic demands. Chatbots that combine ease of use with practical functionality make learning more efficient and accessible, emphasizing the need for interfaces designed specifically for the challenges of nursing education. While engagement strongly influences Feedback Quality (FBQ) and Self-Regulated Learning (SRL), its direct effect on HOTS is minimal. This finding confirms that engagement alone cannot drive deep learning but must be paired with meaningful strategies to achieve cognitive and professional growth [ 37 , 76 – 78 ]. Chatbots should therefore go beyond simple interactions by offering personalized feedback and scaffolds that support critical thinking, clinical decision-making, and reflective practices essential for professional competency. Both FBQ and SRL are vital mediators that transform engagement into HOTS. High-quality feedback—timely, clear, and relevant—helps nursing students critically analyze their performance and refine essential clinical skills [ 29 , 79 ]. Similarly, SRL equips students with critical skills like goal-setting, self-monitoring, and reflection [ 30 , 41 ]. These findings highlight the importance of chatbots that foster autonomous learning and provide structured, adaptive support for nursing students navigating the rigorous demands of their education. Despite their promise, AI-powered chatbots in nursing education face challenges such as inconsistent feedback quality and variability in students’ self-regulation abilities [ 10 , 42 ]. Nursing students in China, often constrained by limited resources, limited access to clinical training, and scalability issues[ 22 ], stand to benefit from innovations like adaptive feedback algorithms and personalized learning paths[ 22 , 25 ]. Scalable, culturally tailored solutions are key to maximizing the impact of chatbot-based learning. Limitations This study has several limitations. First, its cross-sectional design limits the ability to infer causality. Second, all data were self-reported, which may introduce response bias. Third, although stratified sampling was employed, the sample was limited to students from one university in China, which may affect the generalizability of the results. Finally, the study relied solely on quantitative data; future research could benefit from mixed-method approaches to gain deeper insights into students’ experiences with chatbot-based learning. Conclusions AI-powered chatbots hold significant promise as virtual learning assistants for nursing students in China. Their effectiveness, however, is influenced by several factors, including design quality, implementation context, and individual learner characteristics. This study highlights the pivotal roles of PUC and EoU in fostering student engagement. In turn, engagement enhances FBQ and SRL, both of which serve as key mediators in the development of HOTS. Nevertheless, engagement alone is not sufficient to promote deep learning. To maximize their educational impact, chatbots must be designed to deliver timely, personalized feedback and effectively support self-regulation [ 29 , 76 , 77 ]. Addressing persistent challenges such as feedback variability and limited scalability is critical to optimizing chatbot-based learning environments. Future research should prioritize the development of advanced feedback algorithms, ensure alignment of chatbot functions with authentic clinical scenarios, and incorporate longitudinal and experimental designs to assess their long-term impact on professional competence and patient care outcomes. By overcoming current limitations and leveraging AI advancements, chatbots have the potential to transform nursing education, equipping students with the critical thinking and problem-solving skills needed to thrive in an evolving healthcare landscape. Declarations Acknowledgements The authors would like to thank all nursing students who participated in this study, Mr. Huo Xincun, Ms. Zhai Shuna and all of the experts who provided validation, support, and recommendations. Author’s contributions RW: Conceptualization, Methodology, Investigation, Data Curation, Writing, Original Draft, Writing - Review & Editing, Supervision, Project administration. AR: Conceptualization, Methodology, Validation, Data Curation, Writing, Original Draft, Writing - Review & Editing, Supervision, Project administration. All authors read and approved the final manuscript. Funding This research was supported by the Shandong Province Undergraduate Teaching Reform Project (M2022022). Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The ethical clearance for this study was obtained from Shandong Xiehe University's Ethics Committee (reference LLSC-KY03-2025004). The study was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Information about the study’s purpose, confidentiality measures, and voluntary participation was provided, and informed consent was obtained from all participants. Clinical trial number: not applicable Consent for publication Not applicable. Competing Interests The authors declare that they have no competing interests. References Ma YZ, Wang J, Li X, Sun W, Wu H, Liu T, Zhao QH, Xiao MC. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6372448","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452456174,"identity":"260c7d3a-eb9e-4c85-a2da-0cca7a2035b1","order_by":0,"name":"Ruowei Wang","email":"","orcid":"","institution":"Shandong Xiehe University","correspondingAuthor":false,"prefix":"","firstName":"Ruowei","middleName":"","lastName":"Wang","suffix":""},{"id":452456176,"identity":"b88168ac-dc4a-4599-bc21-a3a9c31954c2","order_by":1,"name":"Arumugam Raman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDADfgjFTIIWyQaStRgcIFYL/+wzppt5ftlFG9/IMd3AUGGd2CB2xgCvFolzOWa3efuSc7fdyDG7wXAmPbFBOge/FoYzPEAtPcwQLYxthwlrkYdoqc/dPAOk5R8RWgxAWnh+HM7dIAHS0kCEFsMzbGU35zYcz51x5lnZjYRj6cZt0mkFeLXInWHeduPNn+rc/vbkbTc+1FjL9ksnb8CrBQwY26CMBCBmY+AgEGJg8AeFx/6ACC2jYBSMglEwggAA0PFKgoX1//8AAAAASUVORK5CYII=","orcid":"","institution":"Universiti Utara Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Arumugam","middleName":"","lastName":"Raman","suffix":""}],"badges":[],"createdAt":"2025-04-04 00:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6372448/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6372448/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82295891,"identity":"961d1a2b-05ed-43e6-9780-611d9af13f8f","added_by":"auto","created_at":"2025-05-08 19:28:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70534,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework of the study\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: PUC- Perceived usefulness of chatbots, EoU- Ease of use of chatbots, ENG- Engagement with chatbots, FBQ- Feedback quality, SRL- Self-regulated learning, HOTS- Higher-order thinking skills\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6372448/v1/3736677aa00d99df946211fc.png"},{"id":82295884,"identity":"5b89046d-2dc8-4ecd-a898-f77108d83822","added_by":"auto","created_at":"2025-05-08 19:28:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74557,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Equation Model\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6372448/v1/6095120d3b76fe710f1e4737.png"},{"id":82296380,"identity":"6f59d34a-8888-4592-97c2-ad012c88ca78","added_by":"auto","created_at":"2025-05-08 19:44:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1185568,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6372448/v1/b66f1f1e-62f3-441d-a4d3-0f5d74e491b7.pdf"},{"id":82295885,"identity":"871c5ead-f0bc-4eb0-9fc5-c8158b89b2bc","added_by":"auto","created_at":"2025-05-08 19:28:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19179,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1ResearchQuestionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-6372448/v1/21fd6b59b45da0f839fad343.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing nursing education: An AI-powered Chatbots for fostering engagement and higher-order thinking skills","fulltext":[{"header":"Background","content":"\u003cp\u003eChatbots in education have gained significant momentum in recent years, thanks to advancements in artificial intelligence (AI) that have made these tools more interactive and adaptable for teaching and learning [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In healthcare education\u0026mdash;where knowledge is vast and constantly evolving\u0026mdash;chatbots offer a unique solution by providing students with on-demand access to resources, information, and personalized support [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nursing education, in particular, benefits from these innovations, as nursing students must balance mastering theoretical concepts with acquiring practical skills to prepare for their demanding roles.\u003c/p\u003e \u003cp\u003eIn China, the demand for skilled healthcare professionals has surged due to ageing populations, rapid urbanization, and a growing burden of chronic diseases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As a result, nursing education is under pressure to produce highly competent graduates who can meet these challenges. While traditional teaching methods have been effective, they often require students to quickly learn a large amount of complex knowledge and passively absorb knowledge points, and may fall short in addressing students' personalized learning needs and the cultivation of higher-order thinking skills [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. With their ability to deliver instant, individualized feedback and support, Chatbots present a promising supplementary or auxiliary solution [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, nursing students are faced with the continuous updating of medical and health knowledge. In this context, chatbots have proven effective in fostering self-directed learning, enhancing engagement, and promoting critical thinking skills [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In China, the adoption of AI chatbots in nursing education is gradually growing as educational institutions leverage technology to improve student outcomes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAI-powered virtual assistants are increasingly being integrated into healthcare education to enhance student learning and engagement. For example, iFlytek, a leading AI company, has developed advanced educational tools such as the T10 AI Learning Machine, which provides personalized learning experiences and AI-assisted grading in subjects like English and medical sciences [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. While Xiaoyi, an AI developed by iFlytek, made history by passing China\u0026rsquo;s National Medical Licensing Examination in 2017, its primary role is to assist doctors in analyzing patient information rather than directly training nursing students [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, AI-driven platforms are being used to support nursing education through interactive tutoring systems, personalized quizzes, and chatbot-based feedback mechanisms, helping students strengthen clinical reasoning and self-regulated learning [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These tools offer round-the-clock access to educational resources, addressing key challenges in medical and nursing education by providing scalable and adaptive learning solutions.\u003c/p\u003e \u003cp\u003eDespite their potential, the integration of chatbots into nursing education in China is still relatively limited [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Factors such as technological infrastructure, faculty readiness, and students\u0026rsquo; perceptions of chatbots play a significant role in determining how effectively they are adopted into curricula [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Cultural attitudes toward technology and education also shape how these tools are used in classrooms and clinical training environments [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Chatbots could transform the learning experience by providing instant feedback, personalized assistance, and easily accessible resources. Their success, however, relies heavily on the quality of their design and the way they are implemented within educational frameworks. While chatbots hold the promise of boosting student engagement and fostering higher-order thinking skills (HOTS)\u0026mdash;such as critical thinking, problem-solving, and creativity [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u0026mdash;realizing this potential requires careful study of the factors that influence their effectiveness.\u003c/p\u003e \u003cp\u003eThe success of chatbot-based learning largely depends on two key factors: perceived usefulness (PUC) and ease of use (EoU), as outlined in the Technology Acceptance Model (TAM) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Perceived usefulness reflects whether students believe chatbots can enhance their academic performance and help them achieve learning goals. At the same time, ease of use focuses on how intuitive and accessible chatbots are for users with varying technical skills. Beyond these core elements, feedback quality (FBQ) and self-regulated learning (SRL) act as mediating factors that transform engagement into meaningful learning outcomes. High-quality feedback ensures students receive timely, constructive responses to refine their understanding, while self-regulated learning encourages students to plan, monitor, and reflect on their educational journeys [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Together, these factors create a comprehensive framework for understanding how chatbots can enhance learning experiences.\u003c/p\u003e \u003cp\u003eEach element plays a distinct but interconnected role in improving chatbot-based learning. Perceived usefulness motivates students to engage with chatbots they believe will help them succeed academically, such as by improving problem-solving or providing easy access to resources [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Ease of use reduces the frustration of poorly designed systems, ensuring students can navigate chatbot interfaces effortlessly [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Engagement bridges usability and learning outcomes by involving students emotionally, behaviorally, and cognitively in their studies [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Feedback quality adds value to engagement by offering actionable, personalized insights tailored to individual learning needs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Finally, self-regulated learning strengthens the link between engagement and higher-order thinking skills (HOTS) by promoting proactive and reflective learning behaviors essential for academic success [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite their potential, chatbots in education still face several challenges. One major issue is the inconsistency of feedback quality. Many chatbots struggle to provide timely or nuanced responses, hindering students\u0026rsquo; ability to engage deeply with the content [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Poor interfaces can also deter students\u0026mdash;especially those with limited technical skills\u0026mdash;from using the tools regularly [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Another hurdle is that not all students have strong self-regulation skills, limiting their ability to benefit from chatbot interactions fully [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Moreover, chatbots often fail to deliver the sophisticated support needed to develop higher-order cognitive skills like critical thinking, analysis, and creativity [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo tackle these challenges, several solutions have been proposed. For instance, improving chatbot interfaces to be more user-friendly and intuitive helps increase accessibility and encourages consistent use [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Advanced AI-driven personalization in feedback mechanisms ensures students receive tailored responses that align with their specific learning needs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Chatbots can also promote self-regulated learning by providing structured prompts that guide students in planning, monitoring, and reflecting on their progress [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These measures aim to close the gap between student engagement and the development of higher-order thinking skills, ensuring every interaction with a chatbot contributes to meaningful learning outcomes.\u003c/p\u003e \u003cp\u003eHowever, some challenges remain. Customizing feedback to address the diverse needs of students and supporting the development of higher-order cognitive skills continues to be a struggle [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Scalability is another issue, as implementing personalized features often requires advanced AI models that may not be feasible for large student populations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Additionally, while individual factors like PUC and EoU are well-documented, there is a lack of comprehensive models that integrate these elements and mediating factors like FBQ and SRL into a unified framework.\u003c/p\u003e \u003cp\u003eIn summary, while significant progress has been made in addressing usability and engagement challenges, there is still much to learn about fully leveraging chatbots in education [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Combining perceived usefulness, ease of use, feedback quality, and self-regulated learning into a unified framework is essential to maximize the impact of chatbots on student engagement and learning outcomes. Bridging these gaps will pave the way for designing effective, user-friendly, and scalable chatbot systems that adapt to diverse educational contexts.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eAccording to the Technology Acceptance Model (TAM) introduced by F Davis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], individuals are more likely to adopt technology when they perceive it as beneficial to their performance. Chatbots are highly useful in education because they provide timely feedback, personalized learning experiences, and immediate support, fostering deeper student engagement [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Studies show that when students view chatbots as tools that improve outcomes like critical thinking and problem-solving, they are more willing to engage with them [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. This is particularly true in higher education, where chatbots promote efficiency and self-directed learning [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Frequent interactions with chatbots perceived as helpful also lead to greater behavioral and cognitive engagement [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe concept of perceived usefulness (PUC), introduced by F Davis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] in the Technology Acceptance Model (TAM), posits that individuals are more likely to adopt and engage with a technology if they believe it enhances their performance. In the context of chatbots in education, perceived usefulness has been shown to influence student engagement significantly. Research indicates that students find chatbots useful because they provide timely feedback, personalized learning experiences, and immediate assistance, fostering deeper engagement [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, according to MA Almaiah, A Al-Khasawneh and A Althunibat [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], when students perceive chatbots as a tool for improving academic performance, such as critical thinking and problem-solving, their willingness to use them is boosted. In university-level educational environments, for example, chatbots make students' studies efficient and allow them to learn at their own pace [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similarly, according to S Wollny, J Schneider, D Di Mitri, J Weidlich, M Rittberger and H Drachsler [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], students who perceived chatbots in a positive light and saw them as beneficial for them, then such students increased in terms of cognitive and behavioral engagement through direct use of chatbots at a high level.\u003c/p\u003e \u003cp\u003eEase of use (EOU), another key TAM construct, refers to how effortless and intuitive technology is to use [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Research shows that user-friendly chatbot interfaces reduce cognitive load, allowing students to focus on learning rather than navigating complex systems [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. A Khamaj [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] found that intuitive chatbot designs significantly enhance engagement by encouraging repeated use and minimizing frustration. Accessible interfaces are especially beneficial for students with limited technical skills, ensuring inclusivity and broadening engagement [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In addition, in the context of chatbots, G-J Hwang, S-Y Wang and C-L Lai [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] observed that students who found the chatbot interface intuitive and easy to navigate were more likely to engage actively. User-friendly designs encourage repetitive use, fostering behavioral engagement while reducing frustration increases emotional engagement [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe interrelationship between ease of use and perceived usefulness is supported in educational technology research. V Venkatesh and FD Davis [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] extended the Technology Acceptance Model (TAM) and confirmed through experiments that ease of use indirectly impacts engagement (ENG) by enhancing perceived usefulness. For instance, when a chatbot is easy to use, students will perceive it as beneficial, leading to higher engagement. This indirect effect highlights the importance of designing chatbots that are both user-friendly and valuable in educational settings. O Zawacki-Richter, VI Mar\u0026iacute;n, M Bond and F Gouverneur [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] reinforce this perspective, demonstrating that technologies perceived as both easy to use and useful promote higher cognitive and behavioral engagement. Specifically, chatbots that meet these criteria attract initial usage and sustain long-term engagement as students integrate them into their learning routines. Perceived ease of use and perceived usefulness are fundamental constructs that directly influence chatbot adoption in educational contexts. Existing research underscores the necessity of developing functional and intuitive chatbots to maximize their impact on student engagement. As academic institutions increasingly implement AI-driven technologies, understanding these relationships is crucial for enhancing learning outcomes and fostering meaningful interactions with digital tools.\u003c/p\u003e \u003cp\u003eHigh-quality feedback (FBQ) is essential for effective learning, as it helps students improve performance by clarifying goals and refining understanding [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Feedback quality bridges engagement and higher-order thinking skills (HOTS) in chatbot-based learning by transforming active participation into meaningful cognitive outcomes [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Timely, personalized, and constructive feedback has fostered skills like critical thinking and problem-solving [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In chatbot-facilitated learning, feedback quality mediates the relationship between engagement and higher-order thinking skills by transforming active engagement into meaningful learning experiences [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For example, chatbots that provide tailored responses amplify the impact of engagement on cognitive skills, enabling students to analyse, evaluate, and create effectively [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSelf-regulated learning (SRL) refers to learners\u0026rsquo; ability to plan, monitor, and evaluate their progress [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In chatbot-assisted environments, SRL mediates engagement and HOTS by channelling active participation into strategic learning behaviors [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Chatbots promote SRL by offering guidance and encouraging reflection. For instance, DH Chang, MP-C Lin, S Hajian and QQ Wang [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] observed that chatbots improve students\u0026rsquo; goal-setting and self-monitoring skills, essential for critical thinking and creativity. In chatbot-assisted learning environments, SRL acts as a mediator between engagement and higher-order thinking skills by channeling active participation into strategic learning behaviors [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Empirical studies have shown that chatbots promote SRL through reflection and scaffolding. For example, G-J Hwang, S-Y Wang and C-L Lai [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] observed that students who interacted with chatbots demonstrated improved self-regulation and goal-setting behaviors, which in turn enhanced their critical thinking and creativity. Similarly, P Smutny and P Schreiberova [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] found that chatbots facilitate iterative learning processes, allowing students to develop and refine their problem-solving strategies and understanding over time.\u003c/p\u003e \u003cp\u003eHigher-order thinking skills (HOTS), which include analyzing, evaluating, and creating, are crucial for academic success and problem-solving [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. These skills, situated at the top of Bloom\u0026rsquo;s Taxonomy [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], are necessary for tackling complex problems and fostering innovation. Chatbots have shown significant potential in supporting HOTS by providing interactive and adaptive learning experiences [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Engagement is key to developing HOTS, as active participation encourages deeper cognitive processing. JA Fredricks, PC Blumenfeld and AH Paris [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] found that engaged students are likelier to analyse and synthesize information, core elements of critical thinking. Chatbots enhance these processes through timely feedback and scaffolding, which help students refine their understanding [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, creativity\u0026mdash;an integral aspect of HOTS\u0026mdash;is supported by chatbots through activities like brainstorming and open-ended prompts[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Problem-solving skills are also nurtured as chatbots simulate real-world scenarios, guiding students through iterative processes to develop solutions [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile existing studies have explored the relationships between PUC, EoU, engagement, feedback quality, SRL, and HOTS, gaps remain in understanding how these elements interact within an integrated framework. Most research examines individual components, such as engagement or feedback, without fully addressing their combined or mediating roles in fostering HOTS. This highlights the need for comprehensive research that investigates these relationships holistically, including both direct and indirect effects. Such research could provide actionable insights for designing and optimizing chatbot-based learning environments to maximize their impact on student outcomes.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical Framework\u003c/h2\u003e \u003cp\u003eThis research draws on several theoretical frameworks to examine the relationships among perceived usefulness (PUC), ease of use (EoU), engagement, feedback quality, self-regulated learning, and higher-order thinking skills (HOTS). At its core is the Technology Acceptance Model (TAM), which emphasizes that perceived usefulness and ease of use drive technology adoption and sustained engagement [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. F Davis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] suggests that when students view chatbots as helpful and easy to use, their engagement increases, creating opportunities for transformative learning. Building on TAM, engagement theory highlights the importance of meaningful interaction, asserting that active cognitive, emotional, and behavioral involvement connects usability perceptions with mediators like feedback quality and self-regulated learning, ultimately influencing HOTS. J Hattie and H Timperley [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u0026rsquo; feedback model further explains how effective feedback enhances learning by clarifying goals, identifying areas for improvement, and encouraging deeper understanding through iterative processes. BJ Zimmerman [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u0026rsquo; self-regulated learning (SRL) theory adds another layer, emphasizing how planning, monitoring, and evaluating learning processes mediate the link between engagement and cognitive outcomes.\u003c/p\u003e \u003cp\u003eLastly, Bloom\u0026rsquo;s revised taxonomy [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] provides a framework for understanding the development of higher-order cognitive skills, such as analyzing, evaluating, and creating\u0026mdash;outcomes that arise from meaningful engagement, high-quality feedback, and self-regulated learning. Together, these frameworks illuminate how chatbots can facilitate advanced learning processes and outcomes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHypothesis\u003c/h3\u003e\n\u003cp\u003eBased on the above discussion, this study proposed the following hypothesis:\u003c/p\u003e \u003cp\u003eH1: Perceived usefulness of chatbots (PUC) has a positive direct effect on engagement (ENG).\u003c/p\u003e \u003cp\u003eH2: Ease of use of chatbots (EoU) has a positive direct effect on engagement (ENG).\u003c/p\u003e \u003cp\u003eH3: Engagement (ENG) has a positive direct effect on feedback quality (FBQ).\u003c/p\u003e \u003cp\u003eH4: Engagement (ENG) has a positive direct effect on self-regulated learning (SRL).\u003c/p\u003e \u003cp\u003eH5: Feedback quality (FBQ) mediates the relationship between engagement (ENG) and higher-order thinking skills (HOTS).\u003c/p\u003e \u003cp\u003eH6: Self-regulated learning (SRL) mediates the relationship between engagement (ENG) and higher-order thinking skills (HOTS).\u003c/p\u003e \u003cp\u003eThe conceptual framework of this study was showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study used a quantitative research design to explore the relationships among perceived usefulness (PUC), ease of use (EoU), engagement (ENG), feedback quality (FBQ), self-regulated learning (SRL), and higher-order thinking skills (HOTS). Data was collected through a cross-sectional survey, which allowed for the simultaneous measurement of these factors and the identification of their interrelationships. The study employed Structural Equation Modeling (SEM) to analyse the hypothesized relationships. This method would evaluate direct and indirect effects within the conceptual framework, providing a comprehensive understanding of how these constructs interact.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and sample size\u003c/h3\u003e\n\u003cp\u003eThe target population for this study includes nursing students from various academic levels at the University X, which offers Nursing Education in China, all of whom have experience using educational chatbots as part of their coursework. Stratified random sampling was employed to ensure diverse representation, with strata based on academic year (e.g., first-year, final-year students). This approach minimized sampling bias and captured differences in students\u0026rsquo; experiences and perspectives. A minimum sample size of 500 participants was determined following the recommendations for Structural Equation Modeling (SEM) [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], ensuring sufficient statistical power for detecting relationships and validating the model. A larger sample was targeted to account for non-responses or incomplete data. Participants were recruited through institutional email lists, learning management systems, and direct communication with faculty. Information about the study\u0026rsquo;s purpose, confidentiality measures, and voluntary participation was provided to obtain informed consent. Participants were randomly selected within each stratum using computer-generated random numbers to ensure an unbiased selection process.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInstruments\u003c/h2\u003e \u003cp\u003eA structured questionnaire was developed to measure six key constructs in the study: Perceived Usefulness (PUC), Ease of Use (EoU), Engagement (ENG), Feedback Quality (FBQ), Self-Regulated Learning (SRL), and Higher-Order Thinking Skills (HOTS). The items for PUC and EoU were adapted from F Davis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and V Venkatesh and FD Davis [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], while ENG was measured using the Student Engagement Scale [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. FBQ items were custom-developed based on J Hattie and H Timperley [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u0026rsquo; feedback model, and SRL was assessed using BJ Zimmerman [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u0026rsquo; self-regulated learning framework. HOTS was measured with rubrics aligned with Bloom\u0026rsquo;s Taxonomy [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], focusing on cognitive skills such as analysis and creation. All items were measured on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree to 7\u0026thinsp;=\u0026thinsp;Strongly Agree) to ensure consistency and ease of response. The constructs capture specific aspects of the study, including system performance (PUC), usability (EoU), engagement levels (ENG), feedback clarity and relevance (FBQ), self-regulation strategies (SRL), and advanced cognitive skills (HOTS), ensuring a comprehensive assessment of the relationships under investigation. The summary of constructs, measurement items, and sources was shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, For more details on the questionnaire, please see supplementary File 1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of constructs, measurement items, and sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInitial Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRetained Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResponse Options\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Usefulness (PUC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasures the extent to which users believe using the system improves their performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF Davis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; V Venkatesh and FD Davis [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 7\u0026thinsp;=\u0026thinsp;Strongly Agree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEase of Use (EoU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssesses how simple and intuitive users find the system to operate.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF Davis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; V Venkatesh and FD Davis [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 7\u0026thinsp;=\u0026thinsp;Strongly Agree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngagement (ENG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvaluates the cognitive, emotional, and behavioral aspects of Engagement.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJA Fredricks, PC Blumenfeld and AH Paris [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 7\u0026thinsp;=\u0026thinsp;Strongly Agree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeedback Quality (FBQ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasures the clarity, relevance, and timeliness of feedback received.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJ Hattie and H Timperley [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; Custom items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 7\u0026thinsp;=\u0026thinsp;Strongly Agree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Regulated Learning (SRL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExamines strategies like goal setting, self-monitoring, and self-reflection.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBJ Zimmerman [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 7\u0026thinsp;=\u0026thinsp;Strongly Agree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher-Order Thinking Skills (HOTS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaptures cognitive skills like analysis, evaluation, and creation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLW Anderson and DR Krathwohl [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 7\u0026thinsp;=\u0026thinsp;Strongly Agree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidity and Reliability\u003c/h3\u003e\n\u003cp\u003eTo ensure content validity, a panel of experts from local nursing institutions and public universities specializing in educational technology and psychometrics reviewed the questionnaire. Construct validity will be assessed using Convergent Validity and Discriminant Validity via SmartPLS 4. In Partial Least Squares Structural Equation Modeling (PLS-SEM), construct validity is evaluated through specific tests rather than traditional Confirmatory Factor Analysis (CFA). Convergent validity will be determined by examining the Average Variance Extracted (AVE) for each construct; an AVE greater than 0.50 indicates that a construct explains over 50% of the variance in its indicators, which is considered acceptable [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Individual item factor loadings above 0.70 will also be deemed adequate. Reliability will be tested using Composite Reliability (CR) and Cronbach\u0026rsquo;s Alpha, with CR values above 0.70 indicating internal consistency, supported further by Cronbach\u0026rsquo;s Alpha [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Discriminant validity will be measured using the Fornell-Larcker criterion. The Fornell-Larcker criterion requires that the square root of a construct's AVE be greater than its correlations with other constructs [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eData Collection and Analysis\u003c/h3\u003e\n\u003cp\u003eData was collected using an online survey platform to ensure accessibility and convenience for participants. The researchers sent 500 online survey forms to nursing students from years 1 to 4. A total of 480 forms were answered and submitted. However, 10 partially answered forms were excluded from the study. Finally, 470 participants were involved in this study, with a response rate of 94%. The data is exported to SPSS software to save. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypothesised relationships, with the conceptual model evaluated through SmartPLS 4, a powerful tool for handling complex models with multiple constructs and maximizing explained variance [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. The Standardized Root Mean Square Residual (SRMR) was reported to assess the overall model fit. SRMR is widely recognized as an appropriate fit index in PLS-SEM [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Although traditional fit indices like the Comparative Fit Index (CFI) and Root Mean Square Error of Approximation (RMSEA) are standard in covariance-based SEM, SRMR is more suitable for PLS-SEM and provides insights into the adequacy of the model.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents an evaluation of the reliability and validity of the measurement model, confirming that all constructs meet or exceed recommended thresholds for internal consistency and convergent validity. Factor loadings range from 0.871 to 0.958, well above the 0.70 threshold [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], indicating strong item reliability. Composite Reliability (CR) values exceed 0.90 for all constructs, reflecting excellent internal consistency, with Higher-Order Thinking Skills (HOTS) achieving the highest CR value of 0.977. Cronbach\u0026rsquo;s Alpha (α) values also demonstrate strong reliability, ranging from 0.924 to 0.973 across constructs. The Average Variance Extracted (AVE) values surpass the critical threshold of 0.50, with values ranging from 0.809 for Engagement (ENG) to 0.868 for Perceived Usefulness (PUC), confirming that the constructs explain a substantial proportion of variance in their indicators. Self-Regulated Learning (SRL) and Feedback Quality (FBQ) exhibit exceptionally high AVE values of 0.862 and 0.860, respectively, underscoring their decisive contributions to the model. These results confirm the measurement model's reliability, validity, and suitability for assessing the study\u0026rsquo;s constructs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLoadings, composite reliability (CR), Cronbach\u0026rsquo;s alpha, average variance extracted (AVE)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComposite\u003c/p\u003e \u003cp\u003eReliability (CR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s\u003c/p\u003e \u003cp\u003eAlpha (α)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage Variance\u003c/p\u003e \u003cp\u003eExtracted (AVE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epuc1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epuc2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epuc3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEoU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeou1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeou2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeou3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeou4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeou5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeng1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeng2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeng3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeng4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeng5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esrl1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esrl2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esrl3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esrl4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esrl5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efbq1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efbq2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efbq3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efbq4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehots1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehots2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehots3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehots4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehots5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehots6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehots7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehots8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Fornell-Larcker Criterion results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) confirm the discriminant validity of the measurement model, as the square root of the Average Variance Extracted (AVE) for each construct (diagonal values) is higher than its correlations with other constructs (off-diagonal values), in line with C Fornell and DF Larcker [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u0026rsquo; recommendations. For instance, the square root of AVE for Engagement (ENG) is 0.899, exceeding its highest correlation with Self-regulated Learning (SLR) at 0.892, indicating that ENG explains more variance in its indicators than in its relationships with other constructs. Similarly, the square root of AVE for Self-Regulated Learning (SRL) is 0.929, more significant than its correlation with Feedback Quality (FBQ) at 0.899, further supporting discriminant validity. The strongest correlations appear between SLR and FBQ (0.899) and FBQ and HOTS (0.912), reflecting their interdependence in the learning process. Meanwhile, Perceived Usefulness (PUC) shows weaker correlations with HOTS (0.687) and FBQ (0.701), suggesting it exerts an indirect effect. Overall, these findings validate the distinctiveness of the constructs while emphasizing the interconnected roles of engagement, feedback quality, and self-regulated learning in promoting higher-order thinking skills.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFornel -Larcker criterion\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEoU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFBQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHOTS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSRL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEoU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the f\u0026sup2; analysis, highlighting the significant effects of various constructs within the model. Engagement (ENG) emerges as a central factor, exerting a very large effect on both Feedback Quality (FBQ) (f\u0026sup2; = 2.656) and Self-Regulated Learning (SRL) (f\u0026sup2; = 3.874), underscoring its crucial role in shaping feedback perceptions and promoting self-regulated learning behaviors. Ease of Use (EoU) shows a moderate to large effect on ENG (f\u0026sup2; = 0.424), confirming that usability is vital in enhancing student engagement.\u003c/p\u003e \u003cp\u003eFBQ has a moderate to large effect on Higher-Order Thinking Skills (HOTS) (f\u0026sup2; = 0.470), emphasizing the importance of high-quality feedback in fostering critical thinking. In contrast, ENG \u0026rarr; HOTS (f\u0026sup2; = 0.030) and SRL \u0026rarr; HOTS (f\u0026sup2; = 0.025) exhibit minor direct effects, suggesting that their impact on higher-order thinking is predominantly mediated through other pathways. Similarly, Perceived Usefulness (PUC) has a negligible effect on ENG (f\u0026sup2; = 0.019), indicating that its influence may be more supportive than direct.\u003c/p\u003e \u003cp\u003eThese results demonstrate the robustness of the model, with ENG serving as the central driver of significant outcomes. Supported by EoU, FBQ, and SRL, ENG optimizes learning outcomes and facilitates the development of critical thinking skills, highlighting its pivotal role in the learning process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ef Square, effect size and interpretations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u0026rarr; Outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ef\u0026sup2; Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENG \u0026rarr; FBQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Large\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEngagement has a powerful effect on Feedback Quality, emphasizing its critical role in improving feedback.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENG \u0026rarr; HOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEngagement has a small direct effect on Higher-Order Thinking Skills, suggesting indirect mediation through other constructs like FBQ and SRL.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENG \u0026rarr; SRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Large\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEngagement has a powerful effect on Self-Regulated Learning, highlighting its importance in fostering self-regulation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEoU \u0026rarr; ENG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium to Large\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEase of Use has a moderate to significant effect on Engagement, indicating usability significantly influences engagement.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBQ \u0026rarr; HOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium to Large\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFeedback Quality has a moderate to significant effect on Higher-Order Thinking Skills, demonstrating its role in fostering critical thinking.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUC \u0026rarr; ENG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerceived Usefulness has a negligible effect on Engagement, indicating a limited direct impact.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRL \u0026rarr; HOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSelf-Regulated Learning has a small direct effect on Higher-Order Thinking Skills, suggesting its influence is amplified through indirect pathways.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results from the SmartPLS bootstrapping analysis, which confirm that all hypothesized relationships are significant and support the proposed theoretical model. Perceived Usefulness (PUC) has a small but significant positive effect on Engagement (ENG) (β\u0026thinsp;=\u0026thinsp;0.143, p\u0026thinsp;=\u0026thinsp;0.040). In contrast, Ease of Use (EoU) has a strong positive effect on ENG (β\u0026thinsp;=\u0026thinsp;0.685, p\u0026thinsp;=\u0026thinsp;0.000), highlighting the critical roles of usability and perceived utility in driving user engagement. ENG significantly influences both Feedback Quality (FBQ) (β\u0026thinsp;=\u0026thinsp;0.852, p\u0026thinsp;=\u0026thinsp;0.000) and Self-Regulated Learning (SRL) (β\u0026thinsp;=\u0026thinsp;0.892, p\u0026thinsp;=\u0026thinsp;0.000), underscoring its pivotal role in enhancing feedback quality and fostering self-regulated learning behaviors. Mediation effects were also significant, with ENG \u0026rarr; FBQ \u0026rarr; HOTS showing a slight but notable effect (β\u0026thinsp;=\u0026thinsp;0.154, p\u0026thinsp;=\u0026thinsp;0.019) and ENG \u0026rarr; SRL \u0026rarr; HOTS demonstrating a slightly more substantial effect (β\u0026thinsp;=\u0026thinsp;0.169, p\u0026thinsp;=\u0026thinsp;0.036). These findings confirm the central role of engagement in improving learning outcomes both directly and indirectly, mediated through feedback quality and self-regulation learning. Overall, the results validate the theoretical framework and emphasize the interconnected roles of usability, engagement, feedback, and self-regulation learning in fostering higher-order thinking skills.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement model, p-value and decision\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePath Coefficient (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePUC \u0026rarr; ENG (Perceived Usefulness \u0026rarr; Engagement)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported (Significant)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEoU \u0026rarr; ENG (Ease of Use \u0026rarr; Engagement)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported (Significant)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENG \u0026rarr; FBQ (Engagement \u0026rarr; Feedback Quality)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported (Significant)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENG \u0026rarr; SRL (Engagement \u0026rarr; Self-Regulated Learning)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported (Significant)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENG \u0026rarr; FBQ \u0026rarr; HOTS (Mediation Effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported (Significant)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENG \u0026rarr; SRL \u0026rarr; HOTS (Mediation Effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported (Significant)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the structural model and the relationships among Perceived Usefulness (PUC), Ease of Use (EoU), Engagement (ENG), Feedback Quality (FBQ), Self-Regulated Learning (SRL), and Higher-Order Thinking Skills (HOTS). Path coefficients (β) reveal significant relationships, with EoU \u0026rarr; ENG (β\u0026thinsp;=\u0026thinsp;0.685) showing a strong effect, while PUC \u0026rarr; ENG (β\u0026thinsp;=\u0026thinsp;0.143) has a more minor but significant impact. Engagement plays a central role, exerting substantial effects on FBQ (β\u0026thinsp;=\u0026thinsp;0.852) and SRL (β\u0026thinsp;=\u0026thinsp;0.892), mediating its influence on HOTS. Indirect effects through FBQ (β\u0026thinsp;=\u0026thinsp;0.154) and SRL (β\u0026thinsp;=\u0026thinsp;0.169) amplify the total effect of ENG on HOTS (β\u0026thinsp;=\u0026thinsp;0.952), confirming partial mediation. The R\u0026sup2; values reflect strong explanatory power, with HOTS (R\u0026sup2; = 0.850) primarily explained by ENG, FBQ, and SRL, while ENG significantly influences FBQ (R\u0026sup2; = 0.727) and SRL (R\u0026sup2; = 0.795). Indicator loadings, such as PUC1\u0026thinsp;=\u0026thinsp;0.913 and ENG3\u0026thinsp;=\u0026thinsp;0.920, exceed the 0.70 threshold, confirming the reliability of the measurement model. The figure highlights the pivotal role of ENG in driving learning outcomes, with FBQ and SRL amplifying its impact on HOTS. These findings validate the model\u0026rsquo;s strong predictive power and alignment with the theoretical framework.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the mediation analysis, confirming that the relationship between Engagement (ENG) and Higher-Order Thinking Skills (HOTS) is partially mediated by both Feedback Quality (FBQ) and Self-Regulated Learning (SRL). The direct effect of ENG on HOTS is β\u0026thinsp;=\u0026thinsp;0.154 (p\u0026thinsp;=\u0026thinsp;0.019), while the indirect effect through FBQ and SRL is substantial (β\u0026thinsp;=\u0026thinsp;0.798), resulting in a total effect of β\u0026thinsp;=\u0026thinsp;0.952. This indicates that while ENG has a direct influence on HOTS, a significant portion of its impact is mediated by these constructs, confirming partial mediation.\u003c/p\u003e \u003cp\u003eSpecifically, the pathway ENG \u0026rarr; FBQ \u0026rarr; HOTS has an indirect effect of β\u0026thinsp;=\u0026thinsp;0.154, highlighting the significant role of feedback quality in mediating engagement\u0026rsquo;s effect on higher-order thinking. Similarly, the pathway ENG \u0026rarr; SRL \u0026rarr; HOTS exhibits a slightly stronger indirect effect of β\u0026thinsp;=\u0026thinsp;0.169, emphasizing the critical role of self-regulated learning as a mediator. Between the two mediators, SRL demonstrates a slightly greater influence than FBQ.\u003c/p\u003e \u003cp\u003eThe analysis also reveals full mediation for predictors like Perceived Usefulness (PUC) and Ease of Use (EoU) on HOTS. The pathway PUC \u0026rarr; ENG \u0026rarr; HOTS shows an indirect effect of β\u0026thinsp;=\u0026thinsp;0.022, while EoU \u0026rarr; ENG \u0026rarr; HOTS has a stronger indirect effect of β\u0026thinsp;=\u0026thinsp;0.106, both entirely mediated through engagement.\u003c/p\u003e \u003cp\u003eThese findings underscore the pivotal roles of FBQ and SRL in enhancing HOTS, while constructs like PUC and EoU influence HOTS indirectly through engagement. The results validate the interconnected pathways in the model, highlighting the importance of feedback quality and self-regulation in fostering cognitive outcomes. Additionally, model fit indices were evaluated to assess how well the structural equation model (SEM) aligns with the observed data, ensuring the relationships specified in the model accurately reflect the theoretical framework.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation analysis matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect Effect (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect Effect (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Effect (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENG \u0026rarr; HOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.798 (via FBQ \u0026amp; SRL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePartial Mediation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENG \u0026rarr; FBQ \u0026rarr; HOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported (Mediation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENG \u0026rarr; SRL \u0026rarr; HOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported (Mediation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUC \u0026rarr; ENG \u0026rarr; HOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.143 \u0026times; 0.154\u0026thinsp;=\u0026thinsp;0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull Mediation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEoU \u0026rarr; ENG \u0026rarr; HOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.685 \u0026times; 0.154\u0026thinsp;=\u0026thinsp;0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull Mediation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENG \u0026rarr; FBQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENG \u0026rarr; SRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBQ \u0026rarr; HOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRL \u0026rarr; HOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e compares the model fit indices for the saturated (perfectly fitting) model and the estimated model. The SRMR (Standardized Root Mean Square Residual) for the estimated model is 0.063, within the acceptable range (below 0.08), indicating a reasonable fit, though slightly worse than the saturated model (0.033). The d_ULS (1.841) and d_G (0.967) values for the estimated model are higher than those of the saturated model (0.498 and 0.809, respectively), suggesting minor model misspecifications.\u003c/p\u003e \u003cp\u003eThe Chi-square value for the estimated model (2345.283) is also higher than that of the saturated model (2157.606), reflecting some deviation between the observed and model-implied covariance matrices. Lastly, the NFI (Normed Fit Index) for the estimated model is 0.883, slightly below the commonly accepted threshold of 0.90. While these results indicate that the model has an acceptable fit, they also highlight areas where refinement could improve its alignment with the data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel fit indices: comparison of saturated and estimated models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaturated Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimated Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed_ULS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed_G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2157.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2345.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights the transformative potential of thoughtfully designed AI-powered chatbots in enhancing nursing education, particularly by fostering engagement and higher-order thinking skills (HOTS). Consistent with the Technology Acceptance Model (TAM), Perceived Usefulness (PUC) and Ease of Use (EoU) emerged as crucial drivers of Engagement (ENG). Nursing students who found chatbots intuitive and beneficial demonstrated greater motivation and active participation in their learning, underscoring the importance of user-centric designs tailored to meet the unique demands of nursing education, such as intuitive interfaces and practical clinical applications.\u003c/p\u003e \u003cp\u003eAligned with TAM [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], the positive effects of PUC and EoU on engagement reflect how intuitive and functional chatbot designs can support nursing students in managing high academic demands. Chatbots that combine ease of use with practical functionality make learning more efficient and accessible, emphasizing the need for interfaces designed specifically for the challenges of nursing education.\u003c/p\u003e \u003cp\u003eWhile engagement strongly influences Feedback Quality (FBQ) and Self-Regulated Learning (SRL), its direct effect on HOTS is minimal. This finding confirms that engagement alone cannot drive deep learning but must be paired with meaningful strategies to achieve cognitive and professional growth [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Chatbots should therefore go beyond simple interactions by offering personalized feedback and scaffolds that support critical thinking, clinical decision-making, and reflective practices essential for professional competency.\u003c/p\u003e \u003cp\u003eBoth FBQ and SRL are vital mediators that transform engagement into HOTS. High-quality feedback\u0026mdash;timely, clear, and relevant\u0026mdash;helps nursing students critically analyze their performance and refine essential clinical skills [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Similarly, SRL equips students with critical skills like goal-setting, self-monitoring, and reflection [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These findings highlight the importance of chatbots that foster autonomous learning and provide structured, adaptive support for nursing students navigating the rigorous demands of their education.\u003c/p\u003e \u003cp\u003eDespite their promise, AI-powered chatbots in nursing education face challenges such as inconsistent feedback quality and variability in students\u0026rsquo; self-regulation abilities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Nursing students in China, often constrained by limited resources, limited access to clinical training, and scalability issues[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], stand to benefit from innovations like adaptive feedback algorithms and personalized learning paths[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Scalable, culturally tailored solutions are key to maximizing the impact of chatbot-based learning.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, its cross-sectional design limits the ability to infer causality. Second, all data were self-reported, which may introduce response bias. Third, although stratified sampling was employed, the sample was limited to students from one university in China, which may affect the generalizability of the results. Finally, the study relied solely on quantitative data; future research could benefit from mixed-method approaches to gain deeper insights into students\u0026rsquo; experiences with chatbot-based learning.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAI-powered chatbots hold significant promise as virtual learning assistants for nursing students in China. Their effectiveness, however, is influenced by several factors, including design quality, implementation context, and individual learner characteristics. This study highlights the pivotal roles of PUC and EoU in fostering student engagement. In turn, engagement enhances FBQ and SRL, both of which serve as key mediators in the development of HOTS.\u003c/p\u003e \u003cp\u003eNevertheless, engagement alone is not sufficient to promote deep learning. To maximize their educational impact, chatbots must be designed to deliver timely, personalized feedback and effectively support self-regulation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Addressing persistent challenges such as feedback variability and limited scalability is critical to optimizing chatbot-based learning environments.\u003c/p\u003e \u003cp\u003eFuture research should prioritize the development of advanced feedback algorithms, ensure alignment of chatbot functions with authentic clinical scenarios, and incorporate longitudinal and experimental designs to assess their long-term impact on professional competence and patient care outcomes. By overcoming current limitations and leveraging AI advancements, chatbots have the potential to transform nursing education, equipping students with the critical thinking and problem-solving skills needed to thrive in an evolving healthcare landscape.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all nursing students who participated in this study, Mr. Huo Xincun, Ms. Zhai Shuna and all of the experts who provided validation, support, and recommendations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRW: Conceptualization, Methodology, Investigation, Data Curation, Writing, Original Draft, Writing - Review \u0026amp; Editing, Supervision, Project administration. AR: Conceptualization, Methodology, Validation, Data Curation, Writing, Original Draft, Writing - Review \u0026amp; Editing, Supervision, Project administration. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Shandong Province Undergraduate Teaching Reform Project (M2022022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical clearance for this study was obtained from Shandong Xiehe University's Ethics Committee (reference LLSC-KY03-2025004). The study was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Information about the study’s purpose, confidentiality measures, and voluntary participation was provided, and informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMa YZ, Wang J, Li X, Sun W, Wu H, Liu T, Zhao QH, Xiao MC. 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BMC Med Educ. 2020;20(2):460.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AI-powered chatbots, Student engagement, Higher-order thinking skills (HOTS), Perceived usefulness (PUC), Ease of use (EoU), Feedback quality (FBQ), Self-regulated learning (SRL)","lastPublishedDoi":"10.21203/rs.3.rs-6372448/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6372448/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAI-powered chatbots are increasingly integrated into educational settings, offering opportunities to enhance student engagement and higher-order thinking skills (HOTS). However, limited research exists on their role in nursing education, especially in China. This study aimed to explore how AI-powered chatbots impact nursing students\u0026rsquo; engagement and the development of HOTS, mediated by feedback quality (FBQ) and self-regulated learning (SRL).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional, quantitative research design was employed to investigate the interplay between perceived usefulness (PUC), ease of use (EoU), engagement (ENG), FBQ, SRL, and HOTS. 470 nursing students from different academic years participated in the study. Data were collected using a structured survey measuring six key constructs. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to evaluate the direct and indirect relationships within the conceptual framework.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePUC and EoU significantly influenced ENG, which, in turn, strongly mediated the effects of FBQ and SRL on HOTS. ENG had a substantial impact on FBQ (β\u0026thinsp;=\u0026thinsp;0.852) and SRL (β\u0026thinsp;=\u0026thinsp;0.892), while the combined indirect effect of FBQ and SRL on HOTS (β\u0026thinsp;=\u0026thinsp;0.798) demonstrated the critical role of these mediators. The study also confirmed that intuitive chatbot design and high-quality, timely feedback are essential for fostering cognitive skills.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAI-powered chatbots show promise in enhancing engagement and supporting the development of higher-order thinking skills in nursing education. The findings emphasize the need for scalable, user-friendly chatbot systems tailored to educational contexts. Future research should focus on advanced feedback algorithms, long-term impacts on clinical competency, and scalability in diverse learning environments.\u003c/p\u003e","manuscriptTitle":"Enhancing nursing education: An AI-powered Chatbots for fostering engagement and higher-order thinking skills","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 19:28:06","doi":"10.21203/rs.3.rs-6372448/v1","editorialEvents":[{"type":"communityComments","content":3},{"type":"editorInvitedReview","content":"","date":"2026-05-01T11:24:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22105891335090037740600242102060526058","date":"2026-04-30T20:31:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147437236372667681209530621939284379290","date":"2026-04-28T06:53:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T06:35:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193621824298198273736719040031342538030","date":"2026-04-28T06:20:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-17T00:00:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77055792231556634486781695027769602983","date":"2025-05-10T12:42:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-05T08:26:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T12:13:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-14T10:20:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-13T12:24:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-04-13T12:23:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67f48eed-8b1a-4b3f-9531-0de33fc2ae94","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-08T19:28:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-08 19:28:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6372448","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6372448","identity":"rs-6372448","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}