Search Results (1,322)

The integration of artificial intelligence (AI) in education offers novel opportunities to enhance critical thinking while also posing challenges to independent cognitive development. In particular, Human-Centered Artificial Intelligence (HCAI) in education aims to enhance human experience by providing a supportive and collaborative learning environment. Rather than replacing the educator, HCAI serves as a tool that empowers both students and teachers, fostering critical thinking and autonomy in learning. This study investigates the potential for AI to become a collaborative partner that assists learning and enriches academic engagement. The research was conducted during the 2024–2025 winter semester within the Pedagogical and Teaching Sufficiency Program offered by the Audio and Visual Arts Department, Ionian University, Corfu, Greece. The research employs a hybrid ethnographic methodology that blends digital interactions—where students use AI tools to create artistic representations—with physical classroom engagement. Data was collected through student projects, reflective journals, and questionnaires, revealing that structured dialog with AI not only facilitates deeper critical inquiry and analytical reasoning but also induces a state of flow, characterized by intense focus and heightened creativity. The findings highlight a dialectic between individual agency and collaborative co-agency, demonstrating that while automated AI responses may diminish active cognitive engagement, meaningful interactions can transform AI into an intellectual partner that enriches the learning experience. These insights suggest promising directions for future pedagogical strategies that balance digital innovation with traditional teaching methods, ultimately enhancing the overall quality of education. Furthermore, the study underscores the importance of integrating reflective practices and adaptive frameworks to support evolving student needs, ensuring a sustainable model. Full article

This study investigates the effectiveness of contact and online biology teaching by assessing student performance and gathering perceptions from students, teachers, and parents. Conducted in autumn 2021 with 3035 students, 124 biology teachers, and 719 parents, this study combined post-instruction assessments of student performance in knowledge reproduction and conceptual understanding with questionnaires examining perceptions of contact and online biology teaching effectiveness across students, teachers, and parents. To investigate how various teaching-related factors influence perceived understanding of biological content, we applied a CHAID-based decision tree model to questionnaire responses from students, teachers, and parents. Results indicated that students value engaging, flexible instruction, sufficient time to complete tasks and support for independent thinking. Teachers emphasized their satisfaction with teaching and efforts to support student understanding. In contact lessons, students preferred problem-solving, teacher guidance, and a stimulating environment. In online learning, they preferred low-stress, interesting lessons with room for independent work. Parents emphasized satisfaction with their child’s learning and the importance of a focused, stimulating environment. This comparative analysis highlights the need for student-centered, research-based biology teaching in both formats, supported by teachers and delivered in a motivating environment. The results offer practical insights for improving biology instruction in different teaching modalities. Full article

Social anxiety is a prevalent mental health concern among college students, often intensified by academic and interpersonal pressures on campus. This study investigated the relationship between physical activity, loneliness, and social anxiety among college students, aiming to examine the mediating role of loneliness in the process of physical activity affecting social anxiety, as well as the moderating role of gender in this mediating effect. A cross-sectional research design was adopted, and data on physical activity levels, loneliness, and social anxiety were collected through questionnaires completed by 638 students at a university in China. This study conducted a single-factor Harman test, descriptive statistical analysis, reliability analysis, correlation analysis, and independent-samples t-tests, and it modeled the moderated mediation effect. The results showed that physical activity was significantly and negatively correlated with both loneliness and social anxiety. Loneliness played a mediating role in the influence of physical activity on social anxiety, and this mediating effect was moderated by gender, being more pronounced in the female group. This study concluded that physical activity can help alleviate social anxiety, but the mechanism involving the reduction of loneliness is more apparent in women, indicating the need to consider gender differences when developing interventions, as there may be other, more significant reasons for men. Full article

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a worldwide challenge. There are an estimated 17–24 million patients worldwide, with an estimated 60 percent or more who have not been diagnosed. Without a known cure, no specific curative medication, disability lasting years to being life-long, and disagreement among healthcare providers as to how to most appropriately treat these patients, ME/CFS patients are in need of assistance. Appropriate healthcare provider education would increase the percentage of patients diagnosed and treated; however, in-school healthcare provider education is limited. To address the latter issue, the New Jersey Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Association (NJME/CFSA) has developed an independent, incentive-driven, learning program for students of the health professions. NJME/CFSA offers a yearly scholarship program in which applicants write a scholarly paper on an ME/CFS-related topic. The efficacy of the program is demonstrated by the 2024–2025 first place scholarship winner’s essay, which addresses the biological basis of ME/CFS and how the healthcare provider can improve the quality of life of ME/CFS patients. For the reader, the essay provides an update on what is known regarding the biological underpinnings of ME/CFS, as well as a medical student’s perspective as to how the clinician can provide care and support for ME/CFS patients. The original essay has been slightly modified to demonstrate that ME/CFS is a worldwide problem and for publication. Full article

This study examines how Ontario’s Health and Physical Education curriculum addresses the needs of young adults with disabilities. A total of 54 individuals aged 18–35 years old with developmental, learning, or physical disabilities who had completed secondary school in Ontario participated in a cross-sectional mixed-methods survey. Participants were recruited through disability-focused community networks and a university psychology participant pool. They completed the Sex Education subscale of the Sexual Knowledge, Experience, Feelings and Needs Scale, a 35-item sexual knowledge questionnaire, and open-ended questions. Quantitative data were analyzed using descriptive statistics and independent samples t-tests; qualitative responses were examined using thematic analysis. Participants reported limited factual knowledge, minimal classroom representation, and heavy reliance on independent learning. Barriers included inaccessible materials, teacher discomfort, and the absence of disability narratives in sexuality units. Findings point to three priorities: revising curriculum content, expanding educator training, and creating disability-affirming resources. These measures will help ensure comprehensive and rights-based sexuality education that supports the autonomy and well-being of students with disabilities. Full article

Background and Aim: Artificial intelligence (AI) is among the most promising innovations for transforming nursing education, making it more interactive, personalized, and competency-based. However, its integration also raises significant ethical and practical concerns. This scoping review aims to analyze and summarize key studies on the application of AI in university-level nursing education, focusing on its benefits, challenges, and future prospects. Methods: A scoping review was conducted using the Population, Concept, and Context (PCC) framework, targeting nursing students and educators in academic settings. A comprehensive search was carried out across the PubMed, Scopus, and Web of Science databases. Only peer-reviewed original studies published in English were included. Two researchers independently screened the studies, resolving any disagreements through team discussion. Data were synthesized narratively. Results: Of the 569 articles initially identified, 11 original studies met the inclusion criteria. The findings indicate that AI-based tools—such as virtual simulators and ChatGPT—can enhance students’ learning experiences, communication skills, and clinical preparedness. Nonetheless, several challenges were identified, including increased simulation-related anxiety, potential misuse, and ethical concerns related to data quality, privacy, and academic integrity. Conclusions: AI offers significant opportunities to enhance nursing education; however, its implementation must be approached with critical awareness and responsibility. It is essential that students develop both digital competencies and ethical sensitivity to fully leverage AI’s potential while ensuring high-quality education and responsible nursing practice. Full article

The rapidly changing dynamics of the digital age reshape the addiction relationship that high school students establish with technology. While smartphones remove boundaries in terms of communication and access to information, their usage triggers a source of anxiety and nomophobia. The increase in students’ anxiety levels because of their over-reliance on mobile phone use leads to significant behavioral changes in their mental health, academic performance, social interactions and financial dependency. This study examined the nomophobia levels of high school students according to selected socio-demographic indicators. Using the relational screening model, the multistage sampling technique was used to select a sample of 884 participants: 388 from Science High School and 496 from Anatolian High School (459 female, 425 male, Mage = 16.45 ± 1.14 year). Independent sample test and One-way ANOVA were applied. Depending on the homogeneity assumption of the data, Welch values were considered, and Tukey tests were applied as a second-level test from post hoc analyses. Comprehensive analyses of nomophobia levels revealed that young individuals’ attitudes towards digital technology differ significantly according to their demographic and behavioral characteristics. Variables such as gender, physical activity participation, grade level and duration of smartphone use are among the main factors affecting nomophobia levels. Female individuals and students who do not participate in physical activity exhibit higher nomophobia scores. Full article

Students, particularly those in the medical field, are exposed to various stressors that can affect their health-related behaviors, including smoking habits, with implications for oral health and quality of life. Background and Objectives: to analyze the relationship between smoking, oral health, perceived stress level, and self-assessed quality of life in a sample of dental students. Materials and Methods: The cross-sectional study included 338 students, who completed validated questionnaires and were clinically examined. Lifestyle was assessed using a smoking behavior questionnaire, stress levels were measured with the Student Stress Inventory (SSI), and quality of life was evaluated using the EQ-5D-5L instrument. The DMFT index was calculated to determine oral health status. Results: Among the 338 participating students, 53.8% were smokers. The lifestyle analysis revealed slightly higher average scores among non-smokers across all domains—social (3.26 vs. 3.09), attitudinal (2.75 vs. 2.97), and behavioral (3.82 vs. 3.49), but without statistically significant differences (p > 0.25). The mean DMFT score was 12.48, with no significant differences between smokers and non-smokers (p = 0.554). The SSI total score averaged 83.15, indicating a moderate level of perceived stress, again with no statistically significant differences between the groups (p > 0.05). However, slightly higher average stress scores among smokers may suggest the use of smoking as a coping mechanism. In contrast, quality of life as measured by EQ-5D-5L showed significantly worse outcomes for smokers across all five dimensions, including mobility (78.6% vs. 95.5%, p = 0.000) and self-care (93.4% vs. 100%, p = 0.001). Multivariable logistic regression identified smoking (OR = 1.935; p = 0.047) and moderate stress levels (OR = 0.258; p < 0.001) as independent predictors of oral health status. Conclusions: The results obtained suggest that smoking may function as a stress management strategy among students, supporting the relevance of integrating specific psychobehavioral interventions that address stress reduction and oral health promotion among student populations. Full article

The evaluation of large language models (LLMs) increasingly relies on other LLMs acting as automated judges. While this approach offers scalability and efficiency, it raises serious concerns regarding evaluator reliability, positional bias, and ranking stability. This paper presents a scalable framework for diagnosing positional bias and instability in LLM-based evaluation by using controlled pairwise comparisons judged by multiple independent language models. The system supports mirrored comparisons with reversed response order, prompt injection, and surface-level perturbations (e.g., paraphrasing, lexical noise), enabling fine-grained analysis of evaluator consistency and verdict robustness. Over 3600 pairwise comparisons were conducted across five instruction-tuned open-weight models using ten open-ended prompts. The top-performing model (gemma:7b-instruct) achieved a 66.5% win rate. Evaluator agreement was uniformly high, with 100% consistency across judges, yet 48.4% of verdicts reversed under mirrored response order, indicating strong positional bias. Kendall’s Tau analysis further showed that local model rankings varied substantially across prompts, suggesting that semantic context influences evaluator judgment. All evaluation traces were stored in a graph database (Neo4j), enabling structured querying and longitudinal analysis. The proposed framework provides not only a diagnostic lens for benchmarking models but also a blueprint for fairer and more interpretable LLM-based evaluation. These findings underscore the need for structure-aware, perturbation-resilient evaluation pipelines when benchmarking LLMs. The proposed framework offers a reproducible path for diagnosing evaluator bias and ranking instability in open-ended language tasks. Future work will apply this methodology to educational assessment tasks, using rubric-based scoring and graph-based traceability to evaluate student responses in technical domains. Full article

The growing integration of technology in education has heightened awareness of global risks, such as the spread of disinformation. This awareness is vital for fostering the well-being of individuals, especially teenagers, by promoting critical thinking and responsible digital practices. By cultivating these skills, sustainable education empowers individuals to identify potential threats, protect themselves, and advocate for informed, positive change. As part of a European project, this study aims to analyze the current level of awareness among secondary school students (12 to 17) and their teachers. Differences between both are analyzed in how they deal with disinformation in terms of (1) perceptions, (2) feelings and practices, and (3) knowledge and management. A quantitative approach was adopted for this study, which surveyed 1186 minors and 166 teachers. The analysis was based on non-parametric statistics; the Mann–Whitney U statistic was applied as the appropriate measure for comparing independent samples (teachers and students) with a non-normal distribution (p < 0.05). The results were surprising in that they highlighted that minors were more expert than expected in their use of technology and their awareness of the risks of disinformation. These conclusions make it clear that technological tools have the potential to raise awareness of the dangers of disinformation and improve the sustainability of education. Full article

First-generation students can experience a cultural mismatch between their values and those that colleges and universities tend to prioritize. This mismatch can increase cognitive load, leaving fewer resources available for learning. Effective and long-lasting learning requires actively processing new information and connecting it to existing knowledge—an effort that demands significant cognitive resources. Value affirmation exercises, where students select and reflect upon values that are important to them, have shown promise in reducing cultural mismatch and improving performance on cognitive tasks. However, the impact of these exercises on the learning and recall of new information is less clear. The current study investigated whether a value affirmation exercise, completed before reading an educational passage, would improve memory recall for that passage in a sample of 400 first-generation and continuing-generation young adults, as compared to not affirming. Our results failed to provide evidence that value affirmation exercises impacted recall performance, regardless of whether participants affirmed independent values, interdependent values, or both. Given the importance and implications of this outcome for student learning, we discuss possible explanations for these null findings and suggest future directions in affirmation research. Full article

Background/Objectives: While artificial intelligence (AI) is rapidly transforming education, its specific effectiveness for students with learning disabilities (LD) requires rigorous evaluation. This systematic review aims to assess the efficacy of AI-based educational interventions for students with LD, with a specific focus on the methodological quality and risk of bias of the available evidence. Methods: A systematic search was conducted across seven major databases (Google Scholar, ScienceDirect, APA PsycInfo, ERIC, Scopus, PubMed) for experimental studies published between 2022 and 2025. This review followed PRISMA guidelines, using the PICOS framework for inclusion criteria. A formal risk of bias assessment was performed using the ROBINS-I and JBI critical appraisal tools. Results: Eleven studies (representing 10 independent experiments), encompassing 3033 participants, met the inclusion criteria. The most studied disabilities were dyslexia (six studies) and other specific learning disorders (three studies). Personalized/adaptive learning systems and game-based learning were the most common AI interventions. All 11 studies reported positive outcomes. However, the risk of bias assessment revealed significant methodological limitations: no studies were rated as having a low risk of bias, with most presenting a moderate (70%) to high/serious (30%) risk. Despite these limitations, quantitative results from the stronger studies showed large effect sizes, such as in arithmetic fluency (d = 1.63) and reading comprehension (d = −1.66). Conclusions: AI-based interventions demonstrate significant potential for supporting students with learning disabilities, with unanimously positive reported outcomes. However, this conclusion must be tempered by the considerable risk of bias and methodological weaknesses prevalent in the current literature. The limited and potentially biased evidence base warrants cautious interpretation. Future research must prioritize high-quality randomized controlled trials (RCTs) and longitudinal assessments to establish a definitive evidence base and investigate long-term effects, including the risk of cognitive offloading. Full article

Background/Objectives: Hypertension (HTN) continues to be a leading cause of death and disability in older adults, especially in the southeastern United States. A cross-sectional study was conducted to evaluate the relationships among measured, diagnosed, and treated (HTN) in community-dwelling adults participating in student-led health screenings in eastern Alabama. Methods: Between 2017 and 2019, students from health-related disciplines facilitated screenings at 23 community and independent living sites to conduct health assessments, including measuring blood pressure (BP), obtaining medical history, and evaluating current prescriptions. Statistical analyses including chi-square tests, t-tests, and backward stepwise linear regression were performed. Results: The current sample includes data from 357 adults aged 60 to 99 years (mean age 74.6 ± 8.7), who were 70.9% females, 60.8% identifying as Black/African American (BA), and 36.8% residing in rural areas. The majority of clients had a prior HTN diagnosis (71.1%) and/or currently measured HTN (78.7%). Forty-three percent of adults screened had measured, diagnosed, and pharmaceutically treated HTN, while 31% had measured but untreated HTN. Black clients had higher measured systolic and diastolic BP and were more likely to also have been diagnosed with HTN (p < 0.05 for all). Linear regression indicated that lower systolic BP was predicted by not living alone (p = 0.003), White race (p = 0.004), and previous HTN diagnosis (p = 0.012), while female gender (p = 0.079) and decreasing body mass index (p = 0.053) had marginal predictive value. Conclusions: These results indicate that awareness and screening of HTN in this population are noteworthy, though management of the disease through ongoing screening and referrals is essential to reduce disparities. Full article

With continuous advancements in semiconductor technology, mastering efficient designs of high-quality and advanced chips has become an important part of science and technology education. Chip performances will determine the futures of various aspects of societies. However, novice students often encounter difficulties in learning digital chip designs using Verilog programming, a common hardware design language. An efficient self-study system for supporting them that can offer various exercise problems, such that any answer is marked automatically, is in strong demand. In this paper, we design and implement a web-based Verilog programming learning assistant system (VPLAS), based on our previous works on software programming. Using a heuristic and guided learning method, VPLAS leads students to learn the basic circuit syntax step by step, until they acquire high-quality digital integrated circuit design abilities through self-study. For evaluation, we assign the proposal to 50 undergraduate students at the National Taipei University of Technology, Taiwan, who are taking the introductory chip-design course, and confirm that their learning outcomes using VPLAS together are far better than those obtained when following a traditional method. In our final statistics, students achieved an average initial accuracy rate of over 70% on their first attempts at answering questions after learning through our website’s tutorials. With the help of the system’s instant automated grading and rapid feedback, their average accuracy rate eventually exceeded 99%. This clearly demonstrates that our system effectively enables students to independently master Verilog circuit knowledge through self-directed learning. Full article

Throughout the generations, traditional academic writing skills development has taught students in socio-human programs to express their knowledge and thoughts with an evidence-based foundation, helping them make a special connection with their professional fields. However, a lack of digital learning and writing resources in this process has been identified. This study of the social work field connects digital academic writing, social protection functionality, and research innovations, identifying and exploring open-access (OA) educational and social resources for social work higher education (SWHE). Applying content analyses to online documents and websites, we identified key terms characteristic of social work, following a standard approach on formulating research questions, identifying categories, creating a code book, sampling, and measuring information. The research questions were as follows: How is digital academic writing being developed in social work education programs in Romanian universities? Where do researchers, students, teachers, and professionals gather OA digital information and data for academic innovation? What kind of OA information and data are contained in websites for academic writing? We also used OA socio-educational resource analysis to derive digital, evidence-based, and academic writing codes. The frequencies of these elements in documents and websites were examined. Professional samples of four OA documents and five academic and non-academic Romanian websites with extensions were processed. Furthermore, information from a non-academic official website concerning social protection functionality was observed, identified, and measured. We concluded that academic writing is not included as an independent course in the curricula of Romanian social work programs at universities; this topic is rarely researched. Digital and evidence-based education is also a marginalized topic in socio-human scientific resources. OA information, laws, reports, and statistics were identified. Information on scientific research, academic–non-academic partnerships, descriptions of good practices, and human resources information was lacking. In conclusion, this study contributes to increasing productivity and developing digital academic skills in social work education and research. Full article