Search Results (447)

With the deep integration of AI and IoT technologies, smartwatches have become core terminals for health management. However, research on the use mechanisms among “digital native” college students remains limited. Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and selected constructs from the Health Action Process Approach (HAPA), this study uncovers the drivers and barriers of youths’ smartwatch health function adoption to propose targeted design strategies. A mixed-methods approach was employed, collecting semi-structured questionnaire data from 226 Chinese college students. Quantitative analysis was conducted (n = 106) using Partial Least Squares Structural Equation Modeling (PLS-SEM), complemented by qualitative text mining of open-ended feedback from non-users and churned users. The model demonstrated robust predictive power, supporting five hypotheses. Habit and action planning emerged as core antecedents of use intention, which significantly promoted actual use behavior. Effort expectancy acted as a baseline hygiene factor positively influencing performance expectancy. Qualitative findings confirmed that insufficient sensor accuracy and “health data anxiety” are critical psychological barriers. Validating the integrated model’s effectiveness, we propose three strategic interventions: enhancing data precision to build trust, implementing tiered pricing, and designing anxiety-alleviating visual interfaces, offering theoretical and empirical foundations for optimizing smart health products. Full article

Background: Assessing food and nutrient intake is an important yet challenging component of nutrition research, particularly in populations at higher risk for dietary underreporting. Objective: To evaluate the feasibility, acceptability, and preliminary measurement characteristics of augmenting interviewer-administered 24 h dietary recalls with a photo-based mobile food record application (mCC: my Circadian Clock). Design: This was a randomized cross-over feasibility study in which each participant completed two sets of three 24 h dietary recalls. One set consisted of standard interviewer-administered recalls, while the other incorporated dietary intake captured via the mCC app during the 24 h preceding the recall to guide the interview. Participants: Participants (n = 10) were adults aged 18–65 years with obesity (BMI > 30 kg/m²) and less than a college-level education, recruited from a general community setting. Main Outcome Measures: Primary feasibility outcomes included recall adherence, protocol completion, participant burden, and usability of the mobile application. Secondary and exploratory outcomes included average energy intake (kcal/day), number of food items and eating occasions reported, Healthy Eating Index (HEI)-2015 scores, and recall duration. Statistical Analyses: Descriptive statistics and paired t-tests were used to explore differences between methods; analyses were considered exploratory and hypothesis-generating. Results: All enrolled participants completed every scheduled recall, resulting in 100% adherence and protocol completion. Most participants (70%) rated the mCC app as easy or very easy to use, although 60% reported greater burden with the Augmented Recalls. Average energy intake was 274 kcal/day lower with the augmented method compared with Standard Recalls (95% CI: −597, 50; p = 0.09), with no clear differences observed in reported food items, eating occasions, HEI-2015 scores, or recall duration. Conclusions: Augmenting interviewer-administered 24 h dietary recalls with a photo-based mobile food record is feasible and acceptable in adults with obesity, though it did not demonstrate clear improvements in dietary intake capture in this small feasibility sample. These findings provide practical guidance for refining technology-assisted recall protocols and informing the design of future, adequately powered studies. Full article

The development of new energy vehicles (NEVs) is key to the green and high-quality upgrading of China’s automotive industry, with the penetration rate of domestic NEV passenger cars exceeding 50%. However, deepening industry–university–research (IUR) collaborative innovation to break core technological bottlenecks remains a critical challenge. To address the limitations of existing studies—mostly focusing on dyadic interactions or hypothetical numerical simulations—this study constructs a novel tripartite evolutionary game model of NEV enterprises, university–research institutions, and the government, fully incorporating the industry’s unique attributes of high technological complexity, industrial integration, and innovation risk. Innovatively, we calibrate and verify the model using actual operation data from the Yancheng Institute of Technology–Yueda New Energy Vehicle College, bridging the gap between traditional theoretical simulation and industrial practice. The quantitative findings show that: a 40–60% balanced benefit distribution and matching cost-sharing mechanism are the core conditions for the system to reach an evolutionarily stable state; when the achievement transformation coefficient exceeds 50%, the convergence rate of stable cooperation willingness between both parties increases by over 40%; a moderate government subsidy intensity of 55% effectively accelerates the system’s positive evolution, with the incentive effect of subsidies diminishing rapidly in the mature collaboration stage; and robust collaborative innovation technology can reduce government intervention demand by more than 60%. This study enriches the theory of NEV IUR collaborative innovation, breaks the limitations of traditional research frameworks, and provides actionable references for promoting the high-quality development of the NEV industry. Full article

With the rapid technological advancements, persistent retention disparities, and career stability concerns among Generation Z learners, higher education in the United States needs a re-examination of student success. Student support efforts and previous student-centered frameworks require re-examination in light of the current socio-cultural context. In response, this paper proposes the CIRCLE model. This conceptual model is faculty-driven and includes evidence-based practices that predict successful outcomes by benefiting students’ socio-emotional factors. The model stems from an integrated conceptual framework that synthesizes established student success theories, contemporary research on faculty–student relationships, and digital integration in higher education. Traditional student-centered theories are merged with contemporary digital integration models and applied to the realities of Generation Z and first-generation college students. From this, the author delivers a clear, context-responsive plan for faculty supporting a diverse cohort of learners, as we all live in today’s post-pandemic, digitally immersed world. Full article

To evaluate the effectiveness of an adaptive physical education (APE) program using VR technologies, we studied the physical development and vitality indicators of college female students in two groups: those with and without functional health limitations (N = 70 each). Students with disabilities were randomly divided into experimental and control groups of 35 people each. The experimental group participated in physical education classes using VR. The health assessment included heart rate, blood pressure, and subjective health assessments. Physical development was assessed by the biological age index (BAI) by Voitenko, static balance duration, and breath-holding time. Psychological activity was assessed using the Mindfulness Attention Awareness Scale (MAAS), the Rezapkina Vitality Test, and the Subjective Vitality Scale (SVS). The delayed effect was assessed using a questionnaire. Students with functional impairments initially demonstrated a significant decrease in overall vitality, physical activity duration, and more negative health self-perception. After the virtual reality sessions, they showed a restoration of physical development and vitality indicators to levels close to healthy, as well as a decrease in BAI. The delayed effect was confirmed after three months. The use of virtual reality technologies in the APE program effectively improves the physical development and activity (vitality) of students with functional health impairments. Full article

Digital technologies are widely recognized for their potential to support active learning and foster higher-order cognitive skills, including critical thinking. However, limited research has examined the extent to which students are directly involved in selecting digital tools that shape their learning. This study investigates teachers’ ability to engage students in the selection and pedagogical use of digital technologies, with attention to practices supporting active, personalized learning and critical thinking. Data were collected from 156 educators across diverse disciplines in five teacher-training colleges in Israel using an online questionnaire assessing levels of digital tool use, from non-use to active student involvement. Item Response Theory (IRT) was applied to model teachers’ proficiency and examine differences across tools and background characteristics. Results indicate substantial variability in teachers’ ability to involve students, with particularly low involvement in tools related to problem-solving, differentiation, and personalized learning. Gender and institutional role were significant predictors, with female educators and those holding additional roles demonstrating higher proficiency. These findings highlight the importance of teachers’ techno-pedagogical competence in enabling student participation in digital decision-making and suggest that involving students in tool selection can support the development of critical thinking and learner agency in digitally mediated learning environments. Full article

The rapid advancement of artificial intelligence (AI) and the continuous reform of English education in China have greatly reshaped the professional requirements for college English teachers. In contrast to the predominance of general teacher perspectives in adaptive expertise research, college English teaching offers a discipline-specific context in which adaptive expertise becomes particularly salient in response to AI-related pedagogical change. This study adopts a grounded theory approach to explore the qualities that college English teachers should possess in the era of artificial intelligence. A five-dimensional dynamic model of adaptive expertise was developed, consisting of knowledge expertise, competence expertise, vision expertise, emotional expertise, and development expertise. It shows that knowledge expertise embodies the integration of multiple knowledge domains required in the AI era; competence expertise emphasizes the ability to design and implement AI-enhanced instruction; vision expertise reflects global and future awareness in AI-technology integration; emotional expertise sustains resilience and motivation amid technological change; and development expertise promotes lifelong learning and innovation. These dimensions transfer, enhance, and inspire one another, forming a closed and self-reinforcing loop. It enriches the understanding of teacher professionalism in the AI era and offers a framework for cultivating AI-resilient expertise in higher education. Full article

The processes of teaching and learning are primarily humanistic. However, contemporary artificial intelligence (AI) technology has significantly changed these processes. The current qualitative study aimed to explore this phenomenon by investigating the role that chatbots can play as language tutors in improving ESL students’ writing. Specifically, the study investigated students’ perceptions and experiences to assess the influence of ChatGPT-generated written communication on ESL writing improvement. Data were collected through semi-structured interviews with undergraduates from the College of Languages and Translation at a public university in Riyadh, Saudi Arabia. The emerging themes revealed that students held positive perceptions of the chatbot as a tutor, highlighting that collaborative learning with the chatbot facilitated the acquisition of writing skills and increased engagement in the writing process. Findings also showed noticeable improvement in language development, at lexical, syntactic, semantic, and pragmatic levels, as well as in the use of cognitive and metacognitive writing strategies. The study recommends reevaluating traditional writing instruction methodologies and highlights the benefits of integrating AI chatbots into second-language writing pedagogy. Furthermore, the study emphasises students’ need for accessible English-language tutoring, such as chatbots, which provide immediate, real-time writing instruction. The study also addresses the implications of incorporating AI-powered chatbots into writing curricula at Saudi universities. Full article

Implantable artificial kidneys represent a promising alternative for patients with end-stage renal disease (ESRD), aiming to overcome the limitations of conventional dialysis through the integration of microfluidic and electrokinetic technologies. In this study, we present a sawtooth electrode microfluidic chamber that achieves blood cell separation via negative dielectrophoresis at a record-low operating voltage of 1.4 V, representing a fivefold reduction compared with rectangular electrode designs and supporting potential integration into implantable artificial kidney systems. A microfluidic chip incorporating an asymmetric sawtooth electrode geometry was developed to enhance local electric field gradients while reducing power consumption. Device performance was investigated using COMSOL Multiphysics simulations. Response Surface Methodology (RSM) based on a Box–Behnken design was employed to optimize the number of teeth per unit length (N), sawtooth height (H), and applied voltage (V), while excitation frequency was fixed at 1 MHz and flow velocity was maintained constant at 0.1 µL·min⁻¹. Statistical analysis was conducted using analysis of variance (ANOVA) in Minitab (Version 27; Minitab, LLC, State College, PA, USA, 2024). The optimization model showed strong predictive capability (R² = 95.8%) and identified applied voltage (59.45% contribution) and sawtooth height (33%) as the dominant factors affecting separation efficiency, with a significant H × V interaction (p = 0.023). Comprehensive voltage-response mapping over the range of 0.8–4.0 V revealed four operational regimes, including a previously unreported high-voltage failure zone above 2.8 V, where electrothermal flow and electroporation degrade performance. Under physiological conductivity conditions, the optimized design maintained a separation efficiency of 78.3% at 1.4 V with a tip temperature rise of only 1.2 °C, while full recovery of performance was achieved at 2.2 V. Cell-specific separation efficiencies reached 97.3% for white blood cells, 95.8% for red blood cells, and 84.7% for platelets, reducing the downstream cellular load by 92.6%. These findings demonstrate that the proposed low-voltage, high-efficiency separation platform has strong potential as a cellular pre-filtration module in implantable artificial kidney systems and other lab-on-chip biomedical devices. Full article

Generative Artificial Intelligence (GenAI) has been a viable technology for decades, yet widespread adoption in healthcare and academic settings has remained limited to research. One possible explanation for this is limited understanding about the beliefs around GenAI use amongst faculty and students training in biomedical disciplines that frequently lead to non-physician healthcare careers, including physical therapy (PT), occupational therapy (OT), allied health (AH), and biomedical engineering (BME). Furthermore, no known studies exist assessing differences that may exist across those disciplines. Given the significant number of professionals in those disciplines and the outsized impact they have on the healthcare system, investigating their beliefs around GenAI use is vital before widespread adoption. Accordingly, we investigated the perceptions of GenAI among students and faculty in the aforementioned fields that frequently lead to careers in healthcare. We found that knowledge of GenAI significantly influences comfort with its use completing college coursework including whether respondents believed it contributed to the process of completing that coursework and whether use of GenAI enhances learning. Interestingly, however, there were no statistically significant differences in perceptions of GenAI across disciplines, roles, or institution sizes. Qualitative findings revealed concerns about plagiarism, decline of critical thinking skills, and ethical challenges, while also recognizing GenAI’s potential to enhance learning efficiency and idea generation. Critically, the study results emphasize the need for proper training and guidelines to ensure GenAI is integrated responsibly into healthcare-related education. Full article

This study focuses on the role and mechanism of college students’ social practice in the process of technological innovation driving the integration of culture and tourism. Through diverse data sources and qualitative coding analysis strategies, it deeply explores the application level of technological innovation in cultural tourism practices, as well as the integration of culture and tourism and the educational outcomes. The study finds that technological innovation significantly promotes the activation and dissemination of cultural tourism resources, and social practice effectively realizes the tripartite integration of “technological innovation- cultural tourism-education”. A four-dimensional linkage mechanism model of “technology-resources-subjects-value” has been constructed, including the mechanisms of technology embedding, resource reconfiguration, subject linkage, and value realization. This study provides practical insights and suggestions for universities, industries, and governments in the development of cultural tourism integration while also pointing out the limitations and future prospects of the research. Full article

Continuing advances in cancer diagnosis and treatment, particularly in reproductive-aged patients, has led to numerous national medical organizations, including the American Society for Reproductive Medicine (ASRM), the American College of Obstetrics and Gynecology (ACOG), and the American Society for Clinical Oncology (ASCO), recommending prompt discussion of the potential gonadotoxic effects of chemotherapy and referral to a fertility specialist for counseling regarding possible fertility preservation. Despite overall increased utilization of assisted reproductive technology (ART) in the United States (US), racial and ethnic disparities persist on a multisystem level, ranging from decreased access and utilization of ART to inconsistent and delayed counseling to worse outcomes. While innovations in ART and cancer treatment continue to evolve in parallel, the beneficial impacts have been disparate and more limited in minority populations. This review specifically highlights racial and ethnic disparities in fertility preservation for women with cancer in the United States, highlights the underdeveloped state of this literature, and identifies possible pathways for improvement using the Disparities in Assisted Reproductive Technology (DART) hypothesis as a template. We address three main bottlenecks resulting in delay from time of cancer diagnosis to utilization of fertility preservation services. Full article

Auxiliary line construction has been identified as a crucial approach to fostering mathematical creative thinking. However, existing studies have only focused on the correlations between auxiliary line construction tasks and mathematical creative thinking, without investigating whether engaging in auxiliary line construction can improve mathematical creativity. As a well-established research paradigm, cognitive priming can elicit changes in thinking within a short period. Based on this idea, the present study adopted the cognitive priming paradigm combined with functional near-infrared spectroscopy (fNIRS) technology, and randomly assigned 42 Chinese college students to an auxiliary line group or a control group. The students’ brain activity was monitored in real time during the priming phase (the auxiliary line group completed geometric problems requiring auxiliary line construction, while the control group finished proof problems with pre-set auxiliary lines) and the post-test phase (both groups completed a mathematical creative thinking test). The behavioral results showed that the auxiliary line group achieved significantly higher scores in fluency and originality of mathematical creative thinking than the control group in the post-test phase. The fNIRS data revealed that during the priming phase, the auxiliary line group exhibited stronger activation of the right superior frontal gyrus and higher variability in dynamic functional connectivity; meanwhile, in the post-test phase, the right superior frontal gyrus and right middle frontal gyrus maintained robust neural activation, and brain functional connectivity exhibited a lower clustering coefficient and attenuated small-world network properties. This study confirms that short-term engagement in auxiliary line construction exerts a priming effect on the fluency and originality of mathematical creative thinking, which may be associated with the enhanced activation of specific brain regions and the dynamic adjustment of brain functional connectivity. These findings provide theoretical and empirical evidence for the cultivation of mathematical creative thinking. Full article

Generative Artificial Intelligence (GAI) is rapidly reshaping pedagogical practices and offering new opportunities to advance sustainability within higher education. This study investigates the extent to which postgraduate students utilize GAI to support Sustainable and Environmentally Responsible Teaching Practices (SERTPs), and examines whether this use varies across demographic, academic, and technological characteristics. A descriptive quantitative design was employed, involving 310 postgraduate students from the College of Education at King Khalid University. Data were collected using a validated and highly reliable instrument measuring five dimensions of GAI-supported sustainable teaching. Descriptive and inferential analyses, including t tests, one-way ANOVA, and LSD post hoc comparisons, were conducted. The findings reveal that postgraduate students demonstrate a moderate overall level of GAI use in advancing SERTPs, with the highest engagement occurring in the promotion of sustainable educational practices. Significant differences were only found in relation to students’ levels of technology use and students’ levels of GAI use, indicating that frequent and sophisticated engagement with AI tools is the strongest predictor of sustainable teaching practices. No significant differences emerged across gender, age, academic department, program level, or specialization. The study highlights the need for targeted training and institutional strategies that enhance students’ AI proficiency, thereby enabling GAI to serve as a catalyst for environmentally responsible and sustainable teaching practices in higher education. Full article

This entry presents a conceptual approach for how artificial intelligence (AI) can be used to support high school and college students’ reading comprehension of complex texts across disciplines, using the Revised Metacognitive Awareness of Reading Strategies Inventory (MARSI-R), as an organizing framework. Drawing on research in literacy, learning sciences, and educational technology, the entry conceptualizes AI tools as potential metacognitive supports that can assist learners in planning, monitoring, and evaluating reading. At the same time, it distinguishes between AI use that risks promoting cognitive outsourcing, particularly when tools replace rather than support readers’ active regulation of meaning-making. The entry emphasizes the importance of instructional design and teacher mediation in aligning AI-supported reading practices with established models of metacognitive strategy use. Central to this discourse is the distinction between cognitive scaffolding, using AI to support and extend students’ strategic engagement within their zone of proximal development, and cognitive outsourcing, using AI to bypass cognitive effort entirely, thereby undermining active meaning-making. A distinctive feature of this entry is its use of MARSI-R not only as an assessment instrument but also as a design heuristic for structuring AI-supported reading interactions. By mapping AI affordances onto MARSI-R’s three strategy dimensions, the entry provides a conceptual bridge between established metacognitive theory and the practical design of AI-enhanced reading environments. This framing distinguishes the present contribution from prior work that treats AI tools and metacognitive frameworks as separate domains. Using MARSI-R’s dimensions of Global, Problem-Solving, and Support reading strategies, this entry describes how AI may provide personalized prompts and feedback that encourage strategic engagement with texts in STEM, the humanities, and social sciences. Illustrative classroom examples and research findings are used to highlight AI’s potential to support students in becoming “architects of their own understanding,” while also addressing ethical considerations such as overreliance on automated summaries and data privacy concerns. This entry offers a practical and theoretically grounded roadmap for integrating AI to support thoughtful, reflective reading across disciplines. Full article