Search Results (288)

Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults. This review explains the connections between the genesis and progression of GBM and particular cellular tumorigenic mechanisms, such as angiogenesis, invasion, migration, growth factor overexpression, genetic instability, and apoptotic disorders, as well as possible therapeutic targets that help predict the course of the disease. Glioblastoma multiforme (GBM) diagnosis relies heavily on histopathological features, molecular markers, extracellular vesicles, neuroimaging, and biofluid-based glial tumor identification. In order to improve miRNA stability and stop the proliferation of cancer cells, nanoparticles, magnetic nanoparticles, contrast agents, gold nanoparticles, and nanoprobes are being created for use in cancer treatments, neuroimaging, and biopsy. Targeted nanoparticles can boost the strength of an MRI signal by about 28–50% when compared to healthy tissue or controls in a preclinical model like mouse lymph node metastasis. Combining the investigation of CNAs and noncoding RNAs with deep learning-driven global profiling of genes, proteins, RNAs, miRNAs, and metabolites presents exciting opportunities for creating new diagnostic markers for malignancies of the central nervous system. Artificial intelligence (AI) advances precision medicine and cancer treatment by enabling the real-time analysis of complex biological and clinical data through wearable sensors and nanosensors; optimizing drug dosages, nanomaterial design, and treatment plans; and accelerating the development of nanomedicine through high-throughput testing and predictive modeling. Full article

Background: This narrative review examines the role of artificial intelligence (AI) in bowel sound analysis for the diagnosis and management of inflammatory bowel disease (IBD). Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, presents a significant clinical burden due to its unpredictable course, variable symptomatology, and reliance on invasive procedures for diagnosis and disease monitoring. Despite advances in imaging and biomarkers, tools such as colonoscopy and fecal calprotectin remain costly, uncomfortable, and impractical for frequent or real-time assessment. Meanwhile, bowel sounds—an overlooked physiologic signal—reflect underlying gastrointestinal motility and inflammation but have historically lacked objective quantification. With recent advances in artificial intelligence (AI) and acoustic signal processing, there is growing interest in leveraging bowel sound analysis as a novel, non-invasive biomarker for detecting IBD, monitoring disease activity, and predicting disease flares. This approach holds the promise of continuous, low-cost, and patient-friendly monitoring, which could transform IBD management. Objectives: This narrative review assesses the clinical utility, methodological rigor, and potential future integration of artificial intelligence (AI)-driven bowel sound analysis in inflammatory bowel disease (IBD), with a focus on its potential as a non-invasive biomarker for disease activity, flare prediction, and differential diagnosis. Methods: This manuscript reviews the potential of AI-powered bowel sound analysis as a non-invasive tool for diagnosing, monitoring, and managing inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis. Traditional diagnostic methods, such as colonoscopy and biomarkers, are often invasive, costly, and impractical for real-time monitoring. The manuscript explores bowel sounds, which reflect gastrointestinal motility and inflammation, as an alternative biomarker by utilizing AI techniques like convolutional neural networks (CNNs), transformers, and gradient boosting. We analyze data on acoustic signal acquisition (e.g., smart T-shirts, smartphones), signal processing methodologies (e.g., MFCCs, spectrograms, empirical mode decomposition), and validation metrics (e.g., accuracy, F1 scores, AUC). Studies were assessed for clinical relevance, methodological rigor, and translational potential. Results: Across studies enrolling 16–100 participants, AI models achieved diagnostic accuracies of 88–96%, with AUCs ≥ 0.83 and F1 scores ranging from 0.71 to 0.85 for differentiating IBD from healthy controls and IBS. Transformer-based approaches (e.g., HuBERT, Wav2Vec 2.0) consistently outperformed CNNs and tabular models, yielding F1 scores of 80–85%, while gradient boosting on wearable multi-microphone recordings demonstrated robustness to background noise. Distinct acoustic signatures were identified, including prolonged sound-to-sound intervals in Crohn’s disease (mean 1232 ms vs. 511 ms in IBS) and high-pitched tinkling in stricturing phenotypes. Despite promising performance, current models remain below established biomarkers such as fecal calprotectin (~90% sensitivity for active disease), and generalizability is limited by small, heterogeneous cohorts and the absence of prospective validation. Conclusions: AI-powered bowel sound analysis represents a promising, non-invasive tool for IBD monitoring. However, widespread clinical integration requires standardized data acquisition protocols, large multi-center datasets with clinical correlates, explainable AI frameworks, and ethical data governance. Future directions include wearable-enabled remote monitoring platforms and multi-modal decision support systems integrating bowel sounds with biomarker and symptom data. This manuscript emphasizes the need for large-scale, multi-center studies, the development of explainable AI frameworks, and the integration of these tools within clinical workflows. Future directions include remote monitoring using wearables and multi-modal systems that combine bowel sounds with biomarkers and patient symptoms, aiming to transform IBD care into a more personalized and proactive model. Full article

The increasing prevalence of artificial intelligence (AI) in higher education has established the need to examine the implications of specific AI-based technologies. We analyzed students’ perceptions of Packback, an AI-driven discussion board platform, in a large-enrollment undergraduate course at the University of Tennessee, United States. Valid and reliable quantitative survey instruments were used to measure students’ (n = 96) user experience (UX) of Packback and their perceptions of instructors who require the use of AI platforms in their courses. Data were analyzed to determine how students’ personal characteristics, prior use of Packback, and the UX of Packback influence their perceptions of the credibility (competence, goodwill, trustworthiness) of instructors who require the use of AI platforms. Findings indicated that students had an overall favorable experience of the Packback platform, despite moderate variability. For the credibility of instructors who require the use of AI technologies, students reported a moderate-to-high belief of competence, a moderate belief of goodwill, and a moderate-to-high belief of trustworthiness. A significant model was produced to explain the variance in students’ perception of teacher credibility. Female students and students who had more favorable UX were significantly associated with having higher beliefs in instructor credibility. Although the use of AI platforms can improve efficiency in teaching and learning, our data suggest it can also influence students’ perceptions of instructor credibility. Full article

Artificial intelligence is rapidly reshaping skill expectations across media, marketing, and journalism, however, university curricula are not evolving at a comparable speed. To quantify the resulting curriculum–skill gap in communication-related programs, two synchronous corpora were assembled for the period July 2024–June 2025: 66 course descriptions from six leading UK universities and 107 graduate-to-mid-level job advertisements in communications, digital media, advertising, and public relations. Alignment around AI, datafication, and platform governance was assessed through a three-stage natural-language-processing workflow: a dual-tier AI-keyword index, comparative TF–IDF salience, and latent Dirichlet allocation topic modeling with bootstrap uncertainty. Curricula devoted 6.0% of their vocabulary to AI plus data/platform terms, whereas job ads allocated only 2.3% (χ² = 314.4, p < 0.001), indicating a conceptual-critical emphasis on ethics, power, and societal impact in the academy versus an operational focus on SEO, multichannel analytics, and campaign performance in recruitment discourse. Topic modeling corroborated this divergence: universities foregrounded themes labelled “Politics, Power & Governance”, while advertisers concentrated on “Campaign Execution & Performance”. Environmental and social externalities of AI—central to the Special Issue theme—were foregrounded in curricula but remained virtually absent from job advertisements. The findings are interpreted as an extension of technology-biased-skill-change theory to communication disciplines, and it is suggested that studio-based micro-credentials in automation workflows, dashboard visualization, and sustainable AI practice be embedded without relinquishing critical reflexivity, thereby narrowing the curriculum–skill gap and fostering environmentally, socially, and economically responsible media innovation. With respect to the novelty of this research, it constitutes the first large-scale, data-driven corpus analysis that empirically assessed the AI-related curriculum–skill gap in communication disciplines, thereby extending technology-biased-skill-change theory into this field. Full article

The use of artificial intelligence (AI) in Translation Quality Assessment (TQA) has emerged as an exciting new line of research hoping to explore the potential of this revolutionary technology within the field of translation studies in general and its effect on translator training ecosystem. The aim of this study is to explore how AI’s evaluation of students’ legal translations aligns with instructors’ evaluations and to look at the potential benefits and challenges of using AI in evaluating legal translations tasks. Ten anonymous copies of instructor-graded English-to-Arabic mid-term exam translations were collected from an undergraduate legal translation course at a Saudi university and evaluated using ChatGPT-4o. The system was prompted to detect the translation errors and score the exam using the same rubric that was used by the instructors. A manual segment-by-segment comparison of ChatGPT-4o and human evaluations was conducted, categorizing errors by type and assessing alignment by comparing the scores statistically to determine if there were significant differences. The results indicated a high level of agreement between ChatGPT-4o and the instructors’ evaluation. In addition, paired sample t-test comparisons of instructor and ChatGPT-4o scores indicated no statistically significant differences (p > 0.05). Feedback provided by ChatGPT-4o was clear and detailed, offering error explanations and suggested corrections. Although such results encourage effective integration of AI tools in TQA in translator training settings, strategic implementation that balances automation with human insight is essential. With proper design, training, and oversight, AI can play a meaningful role in supporting modern translation pedagogy. Full article

This study investigates the challenges of promoting accessibility for deaf teachers and students in higher education, focusing on the development of inclusive teaching materials. A qualitative case study was conducted in ten teacher training programmes at the Federal University of Alagoas (Brazil), including nine distance learning courses and one face-to-face LIBRAS programme. Analysis of the Virtual Learning Environment revealed a predominance of text-based content, with limited use of Libras videos, visual resources, or assistive technologies. The integration of Brazilian Sign Language into teaching practices was minimal, and digital translation tools were rarely used or contextually appropriate. Educators reported limited training, technical support, and institutional guidance for the creation of accessible materials. Time constraints and resource scarcity further hampered inclusive practices. The results highlight the urgent need for institutional policies, continuous teacher training, multidisciplinary support teams, and the strategic use of digital technologies and Artificial Intelligence (AI). Compared with previous studies, significant progress has been made. The present study highlights the establishment of an Accessibility Centre (NAC) and an Accessibility Laboratory (LAB) at the university. These facilities are designed to support the development of policies for the inclusion of people with disabilities, including deaf students, and to assist teachers in designing educational resources, which is essential for enhancing accessibility and learning outcomes. Artificial intelligence tools—such as sign language translators including Hand Talk, VLibras, SignSpeak, Glove-Based Systems, the LIBRAS Online Dictionary, and the Spreadthesign Dictionary—can serve as valuable resources in the teaching and learning process. Full article

Introduction/Objectives: The continuous progress of information technologies and their increasing use in the health sector have driven the integration of these technologies into the care of the population, including the progressive use of Artificial Intelligence (AI). Given the rapid growth of AI, legislation and scientific evidence have been accompanying developments, clarifying the place of this technology in society. This study aimed to determine university students’ perspectives on the use of AI in healthcare, correlating them with sociodemographic characteristics. Methods: Data were collected using an original personal questionnaire to first-year students from four organic units at the University of Porto, between December 2024 and March 2025. Results: A total of 235 responses were obtained from four different Faculties, and no significant differences were found between gender, area of study, or course, regarding perspectives on the inclusion of AI in healthcare. Across the board, students view this inclusion positively, even though they trust a doctor more and do not have uniform positions regarding the system’s accountability. Conclusions: Thus, the study’s results highlight the need to deepen the debate and training on AI in healthcare, to promote the conscious, critical, and ethical integration of these technologies into healthcare. Full article

Programming education traditionally requires extensive manual assessment of student assignments, which is both time-consuming and resource-intensive for instructors. Recent advances in large language models (LLMs) open opportunities for automating this process and providing timely feedback. This paper investigates the application of artificial intelligence (AI) tools for preliminary assessment of undergraduate programming assignments. A multi-phase experimental study was conducted across three computer science courses: Introduction to Programming, Programming 2, and Advanced Programming Concepts. A total of 315 Python assignments were collected from the Moodle learning management system, with 100 randomly selected submissions analyzed in detail. AI evaluation was performed using ChatGPT-4 (GPT-4-turbo), Claude 3, and Gemini 1.5 Pro models, employing structured prompts aligned with a predefined rubric that assessed functionality, code structure, documentation, and efficiency. Quantitative results demonstrate high correlation between AI-generated scores and instructor evaluations, with ChatGPT-4 achieving the highest consistency (Pearson coefficient 0.91) and the lowest average absolute deviation (0.68 points). Qualitative analysis highlights AI’s ability to provide structured, actionable feedback, though variability across models was observed. The study identifies benefits such as faster evaluation and enhanced feedback quality, alongside challenges including model limitations, potential biases, and the need for human oversight. Recommendations emphasize hybrid evaluation approaches combining AI automation with instructor supervision, ethical guidelines, and integration of AI tools into learning management systems. The findings indicate that AI-assisted grading can improve efficiency and pedagogical outcomes while maintaining academic integrity. Full article

Against the background of China’s ecological civilization construction and sustainable development strategies, how remote sensing courses adapt to the demands of the artificial intelligence era has become an urgent issue for undergraduate education in relevant disciplines at universities. This study proposed a trinity teaching reform path of “deep learning and remote sensing, and ecological sustainability”, aiming to cultivate interdisciplinary talents with capabilities in intelligent interpretation and practical application. The study established a three-stage curriculum objective system, integrating knowledge, ability, and literacy, designed a five-dimensional linkage teaching method combining case-driven teaching, modular training, and blended learning, and conducted teaching practices using mainstream deep learning frameworks and cloud platforms. Through hierarchical teaching practice cases and multi-dimensional evaluation data, it was shown that the reform effectively enhanced the experiment group students’ abilities in deep learning applications, complex remote sensing data processing, and ecological problem-solving. The achievement values for all five evaluation indicators exceeded 80%, with the highest improvement reaching 28% compared to the control group. The results indicate that this teaching reform not only enhances learning outcomes but also provides a valuable framework and practical pathway for remote sensing education empowered by artificial intelligence and the cultivation of professional talent in future sustainable development fields. Full article

Artificial intelligence is transforming higher education, particularly in flipped classroom settings, in which students learn independently prior to class and collaborate during in-person sessions. This study examines the role of ChatGPT as a virtual peer in a veterinary anatomy course centered on cardiovascular and respiratory systems. Over two academic years (2023–2025), 297 first-year veterinary students worked in small groups to explore anatomy through structured prompts in English and Spanish using ChatGPT versions 3.5 and 4. Activities involved analyzing AI output, evaluating anatomical accuracy, and suggesting alternative names for vascular variations. Learning outcomes were assessed using Bloom’s Taxonomy-based questions, and student perceptions were captured via online surveys. Progressive performance improvement was noted across three instructional phases, particularly in higher-level cognitive tasks (Bloom level 4). Responses to English prompts were more accurate than those to Spanish prompts. While students appreciated ChatGPT’s role in reinforcing knowledge and sparking discussion, they also flagged inaccuracies and emphasized the need for critical evaluation. Peer collaboration was found to be more influential than chatbot input. Conclusions: ChatGPT can enrich flipped anatomy instruction when paired with structured guidance. It supports content review, fosters group learning and promotes reflective thinking. However, developing digital literacy and ensuring expert oversight are essential to maximizing the educational value of AI. Full article

With the rapid advancement of generative artificial intelligence (GenAI), its integration into education has gained significant attention. However, the impact of GenAI tools in expressive arts counseling courses (EAsCCs) remains underexplored. Therefore, we examined the effects of integrating GenAI tools, such as text-to-image, into a four-week EAsCC involving 10 college students (2 males and 8 females). Using a mixed-methods approach, the participants in this study engaged in art-based practices to enhance self-awareness, explore life milestones, envision future goals, and develop their action plan. GenAI tools were used to create art-based photos in this study. Qualitative data from reflection journals and quantitative data from a designed questionnaire were analyzed. The results indicated that the course enhanced participants’ self-awareness, confidence, and understanding of expressive arts practice (EAsP). However, the participants encountered challenges with the precision of GenAI tools in generating intended images, highlighting their current limitations. These results underscore the need for the refinement of GenAI technologies to better support creative expression and therapeutic exploration. The results also provide information on the potential and challenges of GenAI in expressive arts education, emphasizing the importance of interdisciplinary collaboration to develop effective and meaningful learning and teaching experiences. Full article

The higher education sector is increasingly being reshaped and reimagined in the era of Generative Artificial Intelligence (GenAI). For instance, the promise of GenAI to innovate pedagogical approaches in the way teaching and learning (T&L) occur across universities has been increasingly recognised. It is in this context that the question of how literature on the GenAI and Pedagogy (GenAIP) nexus has evolved in recent years has the potential to generate insights that inform and shape T&L policies and practices. However, the systematic analysis of scholarly literature on the GenAIP nexus has remained under the radar. This study responds to this gap and draws on PRISMA for the Scoping Review (PRISMA-ScR) method to carry out a Bibliometric Scoping Review of the GenAIP nexus. It examines scholarly research outputs (n = 310) published between 2023 and 2025 that are available on the Scopus database with two research objectives: (i) to ascertain research trends, thematic emphasis, prominent authors, countries and outlets, and (ii) to map various pedagogical approaches. Beyond revealing that authors from developing economies have produced significantly fewer research outputs than those from developed economies, the analysis highlights an urgent need for appropriate GenAI policies and curriculum redesign. It also documents 40 distinct pedagogical approaches reported in the literature. In light of the growing academic integrity challenges posed by GenAI, this article discusses three key implications for the higher education sector and future research: (i) redesigning courses and assessments to foster AI literacy, (ii) developing fit-for-purpose academic integrity policies, and (iii) delivering AI-focused professional development for academic staff. Full article

The article presents an approach and software tool aimed at achieving quality, accessible, and sustainable education. The approach is based on reusable learning objects—educational video materials that can be repeatedly used and adapted for different languages and audiences. The proposed learning model uses quality learning resources (regardless of their language) and integrates them into courses and educational processes, regardless of the language proficiency of the learners. The approach relies on the integration of subtitle translation technologies into educational video resources, aiming to overcome language barriers in education. The software tool, AI Subtitle Translator, is developed using artificial intelligence (AI) and offers automated subtitle translation. It utilizes OpenAI models (GPT-4o and GPT-4.5) to provide translation services. The workflow, architecture, implementation, and operational scenario of the software tool are also presented. The discussed approach serves as a solution to enhance accessibility to global educational content. By combining reusable learning objects with AI Subtitle Translator, effective education without language constraints is ensured. Full article

With the proliferation of online learning platforms, selecting appropriate artificial intelligence (AI) courses has become increasingly complex for learners. This study proposes a novel hybrid AI course recommendation framework that integrates Term Frequency–Inverse Document Frequency (TF-IDF) and Bidirectional Encoder Representations from Transformers (BERT) for robust textual feature extraction, enhanced by a Random Forest classifier to improve recommendation precision. A curated dataset of 2238 AI-related courses from Udemy was constructed through multi-session web scraping, followed by comprehensive data preprocessing. The system computes semantic and lexical similarity using cosine similarity and fuzzy matching to handle user input variations. Experimental results demonstrate a high recommendation accuracy = 91.25%, precision = 96.63%, and F1-Score = 90.77%. Compared with baseline models, the proposed framework significantly improves performance in cold-start scenarios and does not rely on historical user interactions. A Flask-based web application was developed for real-time deployment, offering instant, user-friendly recommendations. This work contributes a scalable and metadata-driven AI recommender architecture with practical deployment and promising generalization capabilities. Full article

Background/Objectives: There is growing evidence to support ambient artificial intelligence (AI) scribes in healthcare to improve medical documentation by generating timely and comprehensive notes. Using the Plan–Do–Study–Act (PDSA) methodology, this study evaluated the utility and potential time savings of an ambient AI scribe, Scribeberry, (V2), in a palliative medicine outpatient setting, comparing it to the standard practice of dictation. Methods: This prospective quality improvement study was conducted at an academic medical center by two palliative medicine resident physicians. Residents documented patient visits using a freely available ambient AI scribe software program, Scribeberry, as well as using standard dictation software. Primary outcome measures included the editing time for the AI scribe and the dictating and editing times for a dictated manuscript, as well as subjective assessments of the accuracy, organization, and overall usefulness of the AI-generated clinical letters. Results: A heterogenous response was seen with the implementation of an AI scribe. One resident saw a statistically significant reduction (p < 0.025) in the time spent on clinical documentation, while a second resident saw essentially no improvement. The resident who experienced time savings with the ambient AI scribe also demonstrated a significant improvement in the graded organization and usefulness of the AI outputs over time, while the other resident did not demonstrate significant improvements in any of the metrics assessed over the course of this project. Conclusions: This pilot study describes the use of an ambient AI scribe software program, Scribeberry, in the community palliative medicine context. Our results showed a mixed response with respect to time savings and improvements in the organization, accuracy, and overall clinical usefulness of the AI-generated notes over time. Given the small sample size and short study duration, this study is insufficiently powered to draw conclusions with respect to AI scribe benefits in real-world contexts. Full article