Search Results (305)

This exploratory study investigates the relationship between student metacognition, use of artificial intelligence, and empirical performance within a multidisciplinary course on AI. Using data from a sample of college-age, full-time undergraduate students (averaging 18 participants per assessment) enrolled in an in-person junior seminar at a Midwestern U.S. university, we correlate student self-assessments with standard readability metrics (e.g., Flesch–Kincaid), L2SCA metrics, and objective assessment outcomes, analyzing how learners evaluate their own comprehension and how they deploy AI tools in response to the complexity of 21 reading assignments over 8 weeks. We find that students’ perceptions of linguistic difficulty correlate with classical readability scores, but their perceptions do not predict their success nor does their engagement with assistive AI. The results suggest that students utilize generative AI tools as a habitual baseline rather than a strategic response to difficult material. We argue that while students can identify surface-level linguistic friction, they fail to recognize deep conceptual hurdles, leading to a false sense of mastery that neither their intuition nor their AI assistants appear to mitigate. We propose that a quantifiable metric, the Conceptual Difficulty Gap (CDG), may be useful for identifying a class of texts that syntactically appear to be simple, but consistently trigger performance failures. Crucially, we uncover a possible metacognitive blind spot: student self-ratings of difficulty are negatively correlated with this gap, implying that student assessments of difficulty are not based on actual conceptual difficulty. Furthermore, self-reported AI reliance shows no correlation with the gap, indicating that students may not be strategically deploying generative AI tools to mitigate conceptual difficulty. Full article

Computer-aided de novo drug design has been widely explored for early-stage drug discovery, yet the multi-property optimization of novel molecules remains challenging. We aimed to develop a de novo drug design model to efficiently optimize multiple properties simultaneously. We developed a teacher–student-interaction deep learning model fine-tuned by reinforcement learning (TSItransRL) using bioactivity datasets (DRD2 and JNK3/GSK3β targets). A conditional transformer was pretrained as the teacher model to incorporate multi-property information. A vanilla transformer served as the student model and was subsequently optimized through interactive knowledge distillation and reinforcement learning. An evaluation was conducted using MOSES and conditional metrics on two tasks, specifically generating molecules with DRD2-targeting activity and generating molecules with dual JNK3/GSK3β-targeting activity, with the analyses including docking, the similarity ensemble approach (SEA), and scaffold novelty. TSItransRL achieved success rates of 98.36% and 98.90% for the DRD2 and JNK3/GSK3β tasks, respectively, with an internal diversity of 0.795, outperforming most baselines. The docking, SEA, scaffold, and ADMET analyses were used as exploratory in silico assessments to support the preliminary prioritization of selected generated molecules. TSItransRL provides an in silico framework for benchmark-level multi-property molecular generation and prioritization, combining interactive knowledge distillation with reinforcement learning to explore molecules that satisfy predefined predicted-activity, drug-likeness, and synthetic-accessibility criteria. The generated molecules should be regarded as computational candidates for a further medicinal-chemistry assessment, independent validation, and experimental testing rather than experimentally validated leads. Full article

To address the residual lateral coil misalignment after an unmanned aerial vehicle (UAV) lands on a fixed wireless-charging platform, this study proposes a graph-attention-based knowledge distillation method for embedded self-alignment localization. Four detection-coil voltages form an induced-voltage fingerprint database organized as a multi-scale spatial graph. A graph attention network (GAT) teacher model is trained offline to learn neighborhood correlations in the voltage–position mapping, and its spatial knowledge is distilled into a lightweight Tiny-MLP student model for microcontroller unit (MCU)-based online inference. Experimental results show that the GAT teacher achieves a mean absolute error (MAE) of 0.589 cm, while the distilled Tiny-MLP reduces the MAE of the directly trained Tiny-MLP from 1.548 cm to 1.148 cm (a 25.8% reduction under a fixed seed). In 2000 closed-loop alignment trials with random initial positions, the system achieves an 85.5% success rate under a 0.5 cm threshold, indicating that the method supports low-complexity closed-loop self-alignment for UAV wireless charging. Full article

Extramural clinical rotations (ECR) are an integral part of veterinary education in Germany and give students the opportunity to practice and deepen their knowledge and skills. In this study, students from all five veterinary schools in Germany who completed their practical year between the years of 2022 and 2025 were surveyed online regarding their perceived preparedness, their evaluation of teaching during ECR, and what aspects were relevant to them when choosing ECR. Data records of 386 students were analyzed. Being assigned a direct supervisor and feedback meetings were among the five most important aspects for students when choosing their ECR, underlining the importance of a clear structure and constructive feedback for successful learning. More than half of the students reported feeling insufficiently prepared, both theoretically and practically, for surgery. Although the students already passed their propaedeutics exams years before, only 46% of respondents stated having felt (very) well prepared in terms of propaedeutics knowledge going into their ECR, 59% felt (very) well prepared in terms of performing general examinations (routine clinical examinations) and 20% in terms of specific examinations (e.g., neurological or rectal examinations). Meanwhile, 75% of students rated the quality of practical teaching during their ECR positively. However, in some areas—such as specific examinations and diagnostic imaging—there was still room for improvement. Overall, the results suggest that ECR are generally perceived positively. Concerning the feeling of mostly insufficient preparation, further studies are needed to elaborate whether teaching should focus more on practical skills. Full article

Graduate STEM education in the U.S. has experienced continued growth in enrollment, due to its strong international reputation. Yet, attrition rates among students remain high. Mentoring is frequently identified as a critical factor for supporting graduate student success; however, there is limited empirical evidence regarding the most effective mentoring practices for graduate STEM students. The Mentoring Experiences of Graduate Students Survey (MEGSS) was developed and validated with data from 280 graduate STEM students enrolled in a large, public, research-intensive university in the Eastern U.S. Exploratory factor analysis was performed to examine the survey’s reliability and construct validity. A five-factor, 38-item model was developed, consisting of the following subscales: psychosocial support, program completion, research and writing support, career expectations, and career support. The findings show statistically significant differences in students’ perceptions of mentoring experiences and anticipated outcomes based on gender, citizenship, and stage in the program. Recommendations are offered for faculty mentors and institutions to strengthen mentoring practices, particularly in psychosocial areas, research and writing, and career support. Extending the distribution of MEGSS to other graduate research programs (including non-STEM) could identify mentoring gaps and inform evidence-based strategies to strengthen graduate student development. Full article

The integration of international students into host countries and their cultures is a multifaceted challenge that significantly impacts their academic success and well-being. This study leverages Explainable Artificial Intelligence (XAI) to model and interpret variables associated with the self-rated integration of 175 international students at Charles Darwin University (CDU) in Australia, using data from a 42-question survey. Employing machine learning models, including Decision Tree (DT) and Gradient Boosting Machine (GBM), we use XAI techniques to identify variables most strongly associated with students’ self-rated integration, including career confidence, perceived future happiness, and perceived career obstacles. SHAP analyses and partial dependence plots provide global and instance-level insights, revealing both the magnitude and directional effects of these features. The findings highlight the predictive relevance of psychological and social variables in students’ self-rated integration, offering exploratory insights that inform targeted support programs. By enhancing model transparency through XAI, this research fosters trust in AI-driven educational interventions, addressing ethical considerations and promoting equitable outcomes for diverse student populations. Full article

The end-to-end design and successful integration of a low-voltage educational electric vehicle (EV) built around a Controller Area Network (CAN) backbone is presented in this study. Its reproducible system architecture was built on a unified message specification database, and a set of bring-up and diagnostic procedures enables students to assemble, validate, and extend an EV using commodity controllers. The vehicle was manufactured and commissioned with classic-CAN operating at 250–500 kbps, integrating traction, battery management system, dashboard, lighting, and safety nodes. Initial tests confirmed reliable messaging and error-free operation under typical campus driving conditions. In addition, an upgrade path to CAN with flexible data-rate and 100BASE-T1 Ethernet is provided for future curricula. The platform reduces integration complexity, shortens fault-finding, and supports multidisciplinary teaching across mechanical engineering, electrical and computer engineering, and computer science. Full article

Driven by the digital transformation of territorial spatial governance, traditional urban planning is irreversibly shifting towards a data-driven empirical paradigm. However, constrained by mimetic isomorphism and path dependence, many geography-based regional universities remain trapped in an educational dilemma: they overemphasize morphological representation while marginalizing quantitative decision-making, fostering a structural mismatch between graduate competencies and industry demands. To explore a systematic pathway out of this dilemma, this study chronicles a three-year pedagogical intervention utilizing a mixed-methods design with a historical control cohort (N = 275) within the urban planning program of Gansu Agricultural University—a regional institution situated in a less-developed frontier where territorial renewal demands macro-spatial synthesis over aesthetic forms. The intervention strategically redefined the graduate competency profile as “spatial data analysts”, constructing a pedagogical model comprising foundational algorithmic training, cross-disciplinary faculty collaboration, and real-world Project-Based Learning (PBL), coupled with a restructured, evidence-based evaluation system. Longitudinal tracking and quantitative analyses indicate a structural alignment with elevated educational efficacy. At the macro level of employment trajectories, the proportion of graduates securing knowledge-intensive data positions experienced a structural shift, rising from a baseline of 14.5% to 42.5%, reflecting an enhanced capacity to capitalize on expanding societal demands. At the meso level of practical competence, the award rate in high-level professional competitions increased by 35.4%. At the micro cognitive level, the new evaluation mechanism is associated with a successful redirection of students’ cognitive resources toward algorithmic logic and policy translation (p < 0.001) while highly significantly enhancing their self-efficacy in tackling complex, wicked engineering problems (p < 0.001). Rather than isolating pure causal mechanics, this study interprets these systemic gains as a contextual realignment of academic supply. It provides a context-sensitive, reproducible methodological reference for cultivating professional distinctiveness and reshaping the spatial planning education system in the digital era. Full article

This paper adopts an exploratory approach to examine how software configuration, hardware type, user background and context of use influence the usability of Virtual Reality (VR) systems in engineering product development. A VR usability assessment approach that combines two task-based questionnaires, the System Usability Scale (SUS) and the NASA-TLX questionnaire, was evaluated systematically across six experiments involving students, junior engineers and senior engineers in academic and industrial settings. Across the experiments, usability ratings varied depending on user background, task context, hardware configuration, and software implementation. In the observed cases, standalone VR configurations were associated with higher usability ratings among less experienced participants, while PC-based configurations were frequently used in scenarios requiring higher geometric precision and complex engineering interaction. These observations should be interpreted as context-specific findings rather than generalizable causal effects. In addition, professional engineers primarily evaluate VR in terms of workflow integration, precision and return on investment, whereas students focus more on novelty and the interaction experience. Based on these findings, practical design recommendations have been derived for selecting a VR system, adapting interaction concepts, and implementing VR in product development processes. The study does not aim to establish causal relationships, but rather explore usability trends across different contexts as it highlights that VR should not be deployed as a one-size-fits-all solution, but rather as a tool that is both context-specific and user-centered. It also shows how systematic, iterative usability evaluation can directly support the successful industrial integration of VR technologies. Full article

The increasing adoption of data-driven educational systems requires reliable methods to predict student readiness for future coursework and support personalised learning pathways. This study proposes a graph-enhanced ensemble framework that integrates curriculum structure and skill-gap awareness to estimate student course readiness. A global prerequisite directed acyclic graph (DAG) of university subjects was constructed to model curriculum dependencies, from which structural features including the PageRank, in-degree, out-degree, and prerequisite chain depth were derived. In parallel, a domain-informed skill cluster mapping grouped subjects into nine interpretable competency domains to enable skill-gap analysis. These curriculum-aware features were combined with academic history, behavioural engagement, and demographic indicators to produce 38 engineered features for each student–subject pair. Six models (CatBoost, XGBoost, LightGBM, FT-Transformer, MLP and TabPFN) were trained and combined using a weighted ensemble. Experiments on a real-world institutional dataset containing 20,581 students and 727,168 records achieved an AUC of 0.8908 for predicting course success. Ablation experiments demonstrate that graph-derived and skill-cluster features provide modest but statistically significant incremental value. The resulting model was integrated into a prototype personalised recommender that prioritizes curriculum-consistent learning pathways. The proposed framework provides an interpretable and curriculum-aware approach for personalised learning. While the model demonstrates strong overall performance, a moderate gender disparity in the false positive rate was observed. Results were obtained on a large longitudinal dataset from a single university, and external validation at other institutions is needed to confirm generalizability. Full article

Predicting student performance has emerged as an essential element of contemporary learning assessment, allowing educational organizations to determine problematic students and offer early intellectual assistance. Many machine learning (ML) methodologies prioritize predicted accuracy at the expense of interpretability and practical insights. This paper provides a framework for predicting student performance that is both risk aware and explainable utilizing a chaotic educational competition optimizer (ECO) in conjunction with a support vector classifier (SVC) to overcome existing challenges. The ECO serves as a metaheuristic feature selection technique for selecting the most significant features from a multivariate educational dataset consisting of 1195 students and 29 behavioral, demographic, and academic characteristics. Experimental findings demonstrate that ECO effectively condenses the feature space to 11 essential indications and improves generalization of model while maintaining classification robustness. Utilizing the chosen features, the ECO–SVC model attains a complete classification accuracy of 87.03%, with F1-scores of 0.92, 0.69, and 0.82 for high-, medium-, and low-performance student categories, respectively, surpassing other benchmark ML methods. The proposed framework incorporates explainable artificial intelligence (XAI) to improve transparency by utilizing local explanations and permutation-driven feature significance. The XAI research verifies that institutional support, learner engagement, and previous academic success are the most important contributing factors to predictive results. Notably the ECO functions as a classifier-independent feature selection mechanism; however, the support vector classifier (SVC) is adopted in this study due to its strong generalization capability and effectiveness in exploiting the optimized feature space. The findings are analyzed using a semiotic-linguistic framework, wherein certain qualities are correlated with symbolic, indexical, and temporal educational signs, converting numerical significance into substantive pedagogical insights. Furthermore, an initial academic risk profile strategy is established by utilizing SVC decision confidence and elucidating feature contributors. The consequent risk ratings accurately categorize students into low-, medium-, and high-risk categories, facilitating the detection of at-risk learners beyond mere final score assessment. The proposed risk-aware and explainable ECO–SVC framework enhances learning outcomes assessment by integrating interpretability, high accuracy, and proactive academic reasoning, rendering it suitable for real-life educational decision-support systems. Full article

Understanding student artificial intelligence (AI) literacy in the context of higher education is crucial as technology advances and AI use increases. The purpose of this study is to better understand how university students perceive, define, and apply AI literacy within their own educational experiences and from their own disciplinary lens. Collecting electronic survey responses from 130 graduate and undergraduate students across several disciplines including First-Year Writing, Communication Studies, and Education, this study attempts to elucidate how students articulate and perceive their own degree of AI literacy—Access, Understanding, Critical Thinking, Application, and Ethics—in the educational context. Overall, students reported infrequent use, using ChatGPT most often. Education students reported a lower understanding of AI than non-education students. Undergraduates reported higher rates within ethics than graduate students. No significant differences in AI literacy were found between students who were or were not first-generation students, students who did or did not receive financial aid, or by gender. Students reporting higher rates of use also reported higher rates of AI literacy. Crucially, this study provides key qualitative and quantitative insights exploring how students perceive their own AI literacy. Understanding the current state of students’ AI literacy is important to facilitating holistic student success in academic environments and career readiness as institutions of higher education adapt and prepare curricula, programs, and interventions addressing AI literacy across disciplines. Full article

This study explored faculty perceptions of using Facial Recognition Technology (FRT) for tracking medical student attendance at a private Saudi medical college. Using a mixed-methods approach, researchers surveyed 112 faculty members and conducted focus groups with 26 participants. The findings revealed a balanced but divided perspective. While a slight majority (51.8%) showed good acceptance, a significant minority (48.2%) did not. Faculty rated the technology highly for its perceived ease of use (85.7%) and effectiveness (75%). However, significant privacy concerns were a major issue for over half of the respondents (55.3%). Qualitative data highlighted key themes, including initial staff reactions to FR technology, the need for better staff communication and training, the balance between efficiency and technical challenges, and deep-seated ethical and privacy concerns related to surveillance. The study concludes that, while faculty see the potential benefits of FRT, successful implementation depends on addressing their legitimate concerns. To succeed, institutions must develop comprehensive strategies that include transparent privacy policies, reliable technology, and robust training for staff. Prioritizing stakeholder engagement and creating culturally sensitive implementation plans are crucial for balancing the benefits of FRT with privacy and ethical considerations. Full article

The organized integration of research competencies into nursing curricula is still a global challenge and is key for preparing professionals to respond to complex clinical contexts, technological advancements, and contemporary societal demands. At the School of Health of the Polytechnic Institute of Setúbal, a longitudinal research axis was implemented across the four years of the undergraduate nursing program, involving epistemological foundations, the research process, evidence-based practice, and applied practice. Objective: The objective of this study was to describe the design and implementation of the longitudinal axis of research, analyzing institutional indicators of academic success and the progressive development of students’ scientific competencies. Methods: A descriptive documentary study based on institutional data analysis (the number of enrolled students, pass rates, and mean grades in the four research-related curricular units) was conducted, complemented by a review of pedagogical materials produced (two published course booklets: “Research I—From the origin to the dissemination of knowledge” and “Research II—(De)Constructing the Research Process: A Critical and Practical Analysis”) and evidence of scientific dissemination (conference presentations and published articles). Results: A continuous progression in academic performance was observed across the research curricular units, accompanied by increased complexity of student work and enhanced scientific literacy. The sequential structure proved essential: the articulation of epistemology, methodology, critical appraisal, and scientific production demonstrated strong coherence and pedagogical efficiency. Conclusions: The longitudinal research axis constitutes a curricular innovation that strengthens essential scientific competencies in undergraduate nursing education. Longitudinal models that reflect both conceptual and practical progression can significantly contribute to the development of nurses who are critical thinkers, reflective practitioners, and capable of integrating evidence into clinical decision-making. Full article

Background: Infection following total knee arthroplasty (TKA) is a challenging complication. Optimal empirical antibiotic therapy and surgical management hinge on up-to-date knowledge of local pathogen distribution and resistance patterns. However, few studies have examined whether geographical factors, specifically rural versus urban residence, influence the microbiology or clinical outcomes of periprosthetic joint infection (PJI) within integrated healthcare systems. The goal of this study was to assess the temporal evolution of bacterial species and antimicrobial resistance in knee PJI over an 11-year period. As a secondary objective, we wanted to evaluate the potential impact of patient residence on microbiological trends and treatment success. Methods: We conducted a retrospective analysis of all patients diagnosed with knee PJI who underwent surgical treatment between 2013 and 2023 at our center. Infections were classified as acute postoperative, acute hematogenous, or chronic. Patient residence was categorized as rural (<5000 inhabitants) or urban. Temporal trends were modeled using Poisson regression, and comparisons between subgroups were performed using Fisher’s exact test and Student’s t-test. Results: A total of 98 patients were analyzed, with 99 microorganisms identified. Gram-positive organisms predominated (72.3%), with Staphylococcus aureus (33.3%) and Coagulase-negative Staphylococci (CoNS) (29.3%) as the most frequent isolates. Resistance to vancomycin was not detected in S. aureus isolates. However, CoNS demonstrated high resistance to fluoroquinolones (55.2%) and rifampicin (20.7%). No significant annual shifts were observed for Gram-positive (IRR = 0.94; 95% CI: 0.86–1.03; p = 0.413) or Gram-negative cases (IRR = 0.75; 95% CI: 0.53–1.05; p = 0.086). Comparing rural versus urban populations, no differences were found in microbiological profiles (Fisher’s exact test, all p > 0.05). Furthermore, clinical treatment success rates were comparable (Rural 69.4% vs. Urban 63.0%, p = 0.500), despite a significantly higher prevalence of diabetes mellitus in rural patients (34.7% vs. 10.2%, p = 0.007). Conclusions: The microbiological landscape of knee PJI has remained stable, with no emergence of multidrug-resistant S. aureus. In our setting, standardized management protocols appeared to be equally effective regardless of patient residence. However, given the single-center nature and sample size of this study, broader multicenter validation is required before these findings can be generalized. Full article