Search Results (789)

A learning management system (LMS) plays a crucial role in supporting students’ educational activities by centralized platforms for course delivery, communication, and student support. Recently, many universities have integrated chatbots into their LMS to assist students with various inquiries and tasks. However, existing chatbots often necessitate human interventions to manually respond to complex queries, resulting in limited scalability and efficiency. In this paper, we present a memory-augmented large language model (LLM) framework that enhances the reasoning and contextual continuity of LMS-based chatbots. The proposed framework first embeds user queries and retrieves semantically relevant entries from various LMS resources, including instructional documents and academic frequently asked questions. Retrieved entries are then filtered through a two-stage confidence filtering process that combines similarity thresholds and LLM-based semantic validation. Validated information, along with user queries, is processed by LLM for response generation. To maintain coherence in multi-turn interactions, the chatbot incorporates short-term, long-term, and temporal event memories, which track conversational flow and personalize responses based on user-specific information, such as recent activity history and individual preferences. To evaluate response quality, we employed a multi-layered evaluation strategy combining BERTScore-based quantitative measurement, an LLM-as-a-Judge approach for automated semantic assessment, and a user study under multi-turn scenarios. The evaluation results consistently confirm that the proposed framework improves the consistency, clarity, and usefulness of the responses. These findings highlight the potential of memory-augmented LLMs for scalable and intelligent learning support within university environments. Full article

One of the key challenges in the provision of sustainable energy is understanding how younger generations perceive and respond to the relatively higher cost of green energy. This paper examines the attitudes of Generation Z towards paying premium for using products and services made with green power technologies. We surveyed 173 first- and second-year full-time bachelor students from Krakow University of Economics in Poland, combining contingent valuation in daily life scenarios (coffee purchase, apartment rental, travel carbon offset, environmental donation) with measures of connectedness to nature and self-reported tipping behavior. The results show that between 69% and 82% of respondents are willing to pay a premium for green energy. The size of the premium depends on the product that is bought. We find that while respondents are willing to pay a 10.5% premium for coffee prepared in a restaurant that uses only green energy, they are willing to pay just a 3.1% premium for green electricity at home. We also find that respondents are willing to pay three times more for planting a tree than to offset the carbon footprint of a train trip. A stronger emotional and cognitive bond with nature (on a CNS scale) translates into a greater willingness to financially support environmental initiatives. Full article

Introduction: Understanding adolescent maturational development and its impact on physical and psychological well-being is essential for supporting the academic and professional growth of undergraduate students in Health Sciences programs (Psychology, Medicine, and Nursing). This study aimed to assess and compare self-concept, self-perception, physical activity, and lifestyle among first-year Health Sciences students. Methods: A descriptive cross-sectional study was conducted with first-year students at the University of La Laguna, Tenerife, Spain. Data were collected using the Rosenberg Self-Esteem Scale (RSES), General Health Questionnaire (GHQ-12), Physical Activity Questionnaire for Adolescents (PAQ-A), and Health Behaviour in School-aged Children (HBSC). Variables included sex, age, study program, and body mass index (BMI). Statistical analyses included descriptive statistics, reliability assessment (Cronbach’s alpha), distribution tests, and chi-squared tests. Results: Among 190 participants, the RSES showed generally positive self-esteem, although 75% of students reported low self-confidence. Male Psychology students all scored in the fair range on self-perception. Physical activity was low, particularly among female students, with 20% classified as sedentary. HBSC results indicated the need for lifestyle improvements. SOC-13 scores showed that 80.5% of students had fair levels of sense of coherence. Conclusions: Health Sciences students exhibited low self-concept, emotional distress, sedentary habits, and inadequate lifestyle behaviors. Male Nursing students and female Psychology students had the poorest self-concept scores. The findings emphasize the need for interventions promoting healthy habits and emotional well-being among students entering health-related academic programs. Full article

The rapid integration of generative AI, particularly ChatGPT, into academic settings has prompted urgent questions regarding its impact on students’ psychological and academic outcomes. Although generative AI holds considerable potential to transform educational practices, its effects on individual traits such as self-control and academic wellbeing remain insufficiently explored. This study addresses this gap through a sequential two-phase design. In the first phase, the ChatGPT Usage Scale was adapted and validated for a Turkish university student population (N = 413). Using confirmatory factor analysis and item response theory, the scale was confirmed as a psychometrically valid and reliable one-factor instrument. In the second phase, a separate sample (N = 449) was used to examine the relationships between ChatGPT usage, self-control, and academic wellbeing through a mediation model. The findings revealed that higher ChatGPT usage was significantly associated with lower levels of both self-control and academic wellbeing. Additionally, mediation analysis demonstrated that self-control partially mediates the negative relationship between ChatGPT usage and academic wellbeing. The study concludes that while generative AI tools are valuable, their integration into education presents a double-edged sword, highlighting the critical need to foster students’ self-regulatory skills to ensure they can harness these tools responsibly without compromising their academic and psychological health. Full article

Online education has become an important channel for extensive, inclusive and flexible learning experiences. However, significant gaps persist in providing truly accessible, personalized and adaptable e-learning environments, especially for students with disabilities, varied language backgrounds, or limited bandwidth. This paper presents AccessiLearnAI, an AI-driven platform, which converges accessibility-first design, multi-format content delivery, advanced personalization, and Progressive Web App (PWA) offline capabilities. Our solution is compliant with semantic HTML5 and ARIA standards, and incorporates features such as automatic alt-text generation for images using Large Language Models (LLMs), real-time functionality for summarization, translation, and text-to-speech capabilities. The platform, built on top of a modular MVC and microservices-based architecture, also integrates robust security, GDPR-aligned data protection, and a human-in-the-loop to ensure the accuracy and reliability of AI-generated outputs. Early evaluations indicate that AccessiLearnAI improves engagement and learning outcomes across multiple ranges of users, suggesting that responsible AI and universal design can successfully coexist to bring equity through digital education. Full article

Core mathematics courses are fundamental to the academic success of engineering students in higher education. These courses equip students with skills and knowledge applicable to their specialized fields. However, first-year engineering students often face significant challenges in mathematics due to a range of factors, including insufficient preparation, mathematics anxiety, and difficulty connecting theoretical concepts to real-life applications. The transition from secondary to tertiary mathematics remains a key area of educational research, with ongoing discussions about effective pedagogical approaches for teaching engineering mathematics. This study utilized a belief survey to gain general insights into the attitudes of first-year mathematics students towards the subject. In addition, it employed the activity theory framework to conduct a deeper exploration of the experiences of first-year engineering students, aiming to identify contradictions, or “tensions,” encountered within a flipped-classroom learning environment. Quantitative data were collected using surveys that assessed students’ self-reported confidence, competence, and knowledge development. Results from Friedman’s and Wilcoxon’s Signed-Rank Tests, conducted with a sample of 20 participants in 10 flipped-classroom sessions, statistically showed significant improvements in all three areas. All of Friedman’s test statistics were above 50, with p-values below 0.05, indicating meaningful progress. Similarly, Wilcoxon’s Signed-Rank Test results supported these findings, with p values under 0.05, leading to the rejection of the null hypothesis. The qualitative data, derived from student questionnaire comments and one-to-one interviews, elucidated critical aspects of flipped-classroom delivery. The analysis revealed emerging contradictions (“tensions”) that trigger “expansive learning”. These tensions encompassed the following: student expectation–curriculum structure; traditional versus novel delivery systems; self-regulation and accountability; group learning pace versus interactive learning; and the interplay between motivation and anxiety. These tensions are vital for academic staff and stakeholders to consider when designing and delivering a first-year mathematics course. Understanding these dynamics can lead to more effective, responsive teaching practices and support student success during this crucial transition phase. Full article

For decades, the U.S. college admissions process has utilized standardized exams as critical indicators of college readiness. With the onset of the COVID pandemic, the majority of 4-year universities implemented the Test-Optional policy to improve college access and enrollment. The Test-Optional policy allows prospective high school students to apply to institutions that have implemented this policy without a SAT or ACT score. This study examined the use of the Test-Optional policy and its relationship with early college success. Forward multiple regression examined which variables of High School GPA, Students of Color, First-Generation Status, Test-Optional, Pell Eligible, and Pre-College Credits best predict undergraduate first-year GPA. The results generated a five-variable model that accounted for 31% of the variability in first-year college GPA. High School GPA was the strongest predictor, while Test-Optional was not entered into the model. Binary logistic regression examined predictors of first-year college completion. Our results revealed the model including High School GPA, which tripled the odds of first-year completion. Again, Test-Optional was not included in the model. Although Students of Color and Pell Eligibility utilized Test-Optional significantly more than their peers, Test-Optional was not a significant predictor of first-year College GPA or first-year completion. Full article

Humor plays a vital role in human well-being and communication and is increasingly recognized as a beneficial resource in healthcare contexts. While prior studies have explored humor in general university populations, limited research has focused on nursing students, who face distinct interpersonal and emotional demands during their training. This pilot study aimed to describe multidimensional sense of humor among first-year nursing students in Spain using the validated Spanish version of the Multidimensional Sense of Humor Scale (MSHS), which includes a three-dimension model: humor competence, humor as a coping mechanism, and social attitudes toward humor. A total of 78 students completed the MSHS questionnaire via an online survey. The overall mean score was 66.8 (SD = 13.1) out of 96, with the highest mean observed in the dimension of humor as a coping mechanism (mean = 22.2, SD = 4.0). Individual item analysis revealed strong agreement with positively worded statements such as “I like a good joke” (mean = 3.36, SD = 0.82) and “Humor is a lousy coping mechanism” (reverse scored; mean = 3.69, SD = 0.67). These findings suggest that humor is a relevant personal and interpersonal resource among future healthcare professionals. Incorporating humor-related competencies in nursing education may support student resilience and enhance patient-centered care. Further research is needed to examine humor’s longitudinal development and its role in clinical practice. Full article

Beliefs about one’s abilities are powerful predictors of success. Self-efficacy is a basic belief every human should have, as it reflects the confidence that one can achieve one’s goals. As this belief can change over time and depends on one’s self-reflection competence, it is defined as a skill. Academic self-efficacy extends beyond the classroom, shaping how students approach problems, set goals, and respond to challenges. There have been many attempts to create an instrument for measuring different types of self-efficacy, from general self-efficacy about life to self-efficacy to solve specific mathematical tasks. The purpose of this study was to translate, test, and adapt the Academic Self-Efficacy Scale to a sample of Latvian adolescents. The sample comprises 360 adolescents, ranging from 13-year-old sixth-grade pupils to first-year university students. The Academic Self-Efficacy Scale was validated by confirmatory factor analysis, which demonstrated excellent model fit and good item loadings. The Academic Self-Efficacy Scale demonstrated weak to moderate correlations with self-reported achievements in literature, language, and diligence. The strongest correlations were between academic self-efficacy and mathematics. Academic self-efficacy explained 23% of achievement distribution in mathematics. Achievement in mathematics together with diligence explained 32% of self-efficacy distribution. The validated scale demonstrated good reliability, convergence, and incremental validity, and the scale’s reliability and unidimensionality were approved. Full article

Writing is essential across disciplines, yet undergraduate programs must balance writing instruction with discipline-specific content. To support writing development, we piloted an embedded writing tutor (WT) in two core public health courses serving primarily first-generation, low-income students of color. In this model, a tutor familiar with course content is integrated into the classroom to supplement traditional writing center support. Our aims were to examine (1) students’ perceptions of the WT compared to the university’s writing center, (2) the WT’s experiences and effective tutoring strategies, and (3) the instructor’s perspective on implementing the program. Using qualitative methods, the WT recorded field observations, the instructor compared course progression to prior semesters without embedded support, and students completed end-of-semester evaluations. Thematic analysis indicated that students valued the tutor’s accessibility, patience, and direct feedback, though perceived usefulness varied by course, likely due to differences in assignment structure. Challenges included role confusion and inconsistent feedback. Suggested improvements included requiring draft submissions, clarifying the tutor’s role, and aligning tutor and instructor feedback. Quantitative ratings of satisfaction were higher for the WT than for the writing center. Although the sample size was moderate (N = 79), these findings suggest embedded tutoring is a promising, equity-focused strategy for discipline-specific writing instruction. In the context of budget constraints in higher education, exploring alternative tutoring and pedagogical support models remains essential, particularly for underserved populations. Full article

Background: The present study investigated the relation between dental inclination, colorimetric variation, and aesthetic perception according to the modification of incisor inclination. Smile aesthetics, shaped by morphological factors and patient perception, are vital for social attractiveness and treatment success. This study aimed to assess the effect of varying head tilt on the perceived colour of upper central incisors by simulating changes in torque of the tooth, as well as evaluate factors influencing the perception of an aesthetic smile, including morphological characteristics and gingival aesthetic parameters. Methods: The study was comprised of three stages: colour analysis, evaluation of micro- and mini-aesthetic smile features, and an image-based assessment to determine evaluator perceptions and overall smile attractiveness. A sample of 50 students with complete, lesion-free anterior dentition was analysed. To simulate the effect of orthodontic torque changes during colour analysis, subjects tilted their heads downward and upward, representing palatal and buccal crown torque, respectively. Standardized macro-intraoral photographs were captured under controlled lighting conditions using a DSLR camera stabilized on a tripod in the different positions: the neutral head position (p0), 15° upward (p + 15), and 15° downward (p − 15). Digital colour analysis was conducted in the CIELAB colour space (L*, a*, b*). In the next stage, focusing on micro- and mini-aesthetic evaluation, an additional 50 smiles were generated using artificial intelligence via the SmileCloud program—one digitally enhanced smile per subject—complementing the initial set of 50 spontaneous smiles. These 100 smile images were evaluated by 50 laypersons and 50 dentists using a visual analogue scale via an online questionnaire, in order to assess perceptions, determine smile attractiveness, and quantify gingival aesthetic parameters. Results: The statistically significant regression results are as follows: those for the L* values in all three head inclinations: downward (−15 degrees), upward (+15 degrees), and total tilting (−15 to +15 degrees), as well as for the a* values for downward tilting and the b* values for total tilting. When the head is tilted downwards, the central incisors are positioned retrusively, and the L* b* values reveal a darker and more yellowish appearance, whereas, with the head tilted upwards, the central incisors protrude, and L* a* values indicate a brighter and more greenish appear. In the evaluation stage of the smile aesthetics study, no significant differences were observed in the judgments between laypersons and dentists or between males and females. Smiles with a high or average anterior line, parallel arc, upward lip curvature, visible first/second premolars, a smile index of 5.08–5.87, and symmetry score of 1.04 were rated as more attractive. Significant asymmetries were observed between upper dental hemi-quadrants in gingival contour and interdental papilla height, highlighting subtle morphological variations relevant to smile aesthetics. Conclusions: Aesthetic assessment revealed that the findings suggest a measurable impact of head position on dental colour perception and aesthetic evaluation. Evaluator variables including profession and gender exerted negligible effects on aesthetic perception, whereas smile attractiveness features and gingival aesthetic parameters demonstrate significant clinical applicability in patient management. Full article

This study explores the use of a custom generative AI (GenAI) tool, built using a prompt-engineered instance of ChatGPT, to provide formative feedback on first-year undergraduate physics lab reports. A preliminary survey of 110 students identified writing style as an area of low confidence and highlighted strong demand for more actionable, detailed feedback. Students expressed greater comfort with GenAI in formative contexts, particularly when used alongside human assessors. The tool was refined through iterative prompt engineering and supported by a curated knowledge base to ensure accuracy, clarity, and pedagogical alignment. A mixed-methods evaluation with 15 students found that the feedback was useful, actionable, and clearly written, with particular praise for the suggested improvements and rewritten exemplars. Some concerns were raised about occasional inaccuracies, but students valued the tool’s structure, consistency, speed, and potential for interactive follow-up. These findings demonstrate that, when carefully designed and moderated, GenAI can serve as a valuable, scalable support tool within the broader formative assessment cycle for long-form scientific writing. The tool’s flexibility, clarity, and responsiveness highlight its value as a supportive resource, especially as generative AI technologies continue to evolve in educational contexts. Full article

In Chinese character learning, distinguishing similar characters is challenging for learners regardless of their proficiency. This is due to the complex orthography (visual word form) linking symbol, pronunciation, and meaning. Multimedia learning is a promising approach to implement learning strategies for Chinese characters. However, the availability of multimodal resources specifically designed for distinguishing similar Chinese characters is limited. With the advanced development of generative artificial intelligence (GenAI), we propose a practical framework for constructing multimodal resources, enabling flexible and semi-automated resource generation for Chinese character learning. The framework first constructs image illustrations due to their broad applicability across various learning contexts. After that, other four types of multimodal resources implementing learning strategies for similar character learning can be developed in the future, including summary slide, micro-video, self-test question, and basic information. An experiment was conducted with one group receiving the constructed multimodal resources and the other receiving the traditional text-based resources for similar character learning. We explored the participants’ learning performance, motivation, satisfaction, and attitudes. The results showed that the multimodal resources significantly improved performance on distinguishing simple characters, but were not suitable for non-homophones, i.e., visually similar characters with different pronunciations. Micro-videos introducing character formation knowledge significantly increased students’ learning motivation for character evolution and calligraphy. Overall, the resources received high satisfaction, especially for micro-videos and image illustrations. The findings regarding the effective design of multimodal resources for implementing learning strategies (e.g., using visual mnemonics, character formation knowledge, and group reviews) and implications for different Chinese character types are also discussed. Full article

Environmental awareness and sustainability are essential for city development. Therefore, the study examined the attitudes of 1023 Polish Generation Z students from WULS-SGGW in Warsaw, Poland, toward urban trees and willingness to support tree planting. The findings revealed that 75% care about the environment, 93% value nature, and 92% enjoy seeing new trees. Additionally, 74% support funding tree planting, 51% would volunteer, and 39% donate money. However, 54% believe that property owners should be free to cut trees. The Agglomerative Hierarchical Clustering (AHC) method was applied to divide students into clusters. Clusters differed first in students’ attitudes toward trees, from “Tree Lovers” to “Tree Sceptics”, and second in students’ anthropocentric vs. environmental orientation: opposing (“Trees First”) vs. supporting (“People First”) the right to freely cut private trees. An additional questionnaire allowed us to link students’ clusters with importance assigned to positive and adverse tree attributes, like “Attractiveness,” “Usefulness,” and “Danger”. The study results do not provide a clear answer regarding the issue of Polish Generation Z students and the future sustainable development of urban greenery. They want to support trees for practical qualities, beauty, and utility. However, many place an even greater value on their right to self-determination regarding their property, including tree removal. Full article

Generative AI tools such as ChatGPT are reshaping educational practice, yet first-year teacher-education students often lack the prompt-engineering skills and confidence required to use them responsibly. This pilot study examined whether a concise three-session clinic on prompt engineering could simultaneously boost AI literacy and reduce technology anxiety in prospective teachers. Forty-five freshmen in a Peruvian teacher-education program completed validated Spanish versions of a 12-item AI-literacy scale and a 12-item technology-anxiety scale one week before and after the intervention; normality-checked pre–post differences were analysed with paired-samples t-tests, Cohen’s d, and Pearson correlations. AI literacy rose by 0.70 ± 0.46 points (t (44) = −6.10, p < 0.001, d = 0.91), while technology anxiety fell by 0.58 ± 0.52 points (t (44) = −3.82, p = 0.001, d = 0.56); individual gains were inversely correlated (r = −0.46, p = 0.002). These findings suggest that integrating micro-level prompt-engineering clinics in the first semester can help future teachers engage critically and comfortably with generative AI and guide curriculum designers in updating teacher-training programs. Full article