Integration Of Tools Research Articles

University Teachers’ Views on the Adoption and Integration of Generative AI Tools for Student Assessment in Higher Education

This study examines the factors that may impact the adoption of generative artificial intelligence (Gen AI) tools for students’ assessment in tertiary education from the perspective of early-adopter instructors in the Middle East. It utilized a self-administered online survey and the Unified Theory of Acceptance and Use of Technology (UTAUT) model to collect data from 358 faculty members from different countries in the Middle East. The Smart PLS software 4 was used to analyze the data. The findings of this study revealed that educators developed new strategies to integrate Gen AI into assessment and used a systematic approach to develop assignments. Moreover, the study demonstrated the importance of developing institutional policies for the integration of Gen AI in education, as a driver factor influencing the use of Gen AI in assessments. Additionally, the research identified significant factors, namely performance expectancy, effort expectancy, social influences, and hedonic motivation, shaping educators’ behavioral intentions and actual use of Gen AI tools to assess students’ performance. The findings reveal both the potential advantages of Gen AI, namely enhanced student engagement and reduced instructor workloads, and challenges, including concerns over academic integrity and the possible negative impact on students’ writing and thinking skills. This study emphasizes the significance of targeted professional development and ethical criteria for the proper integration of Gen AI in educational assessment.

Exploring the Effectiveness of Advanced Chatbots in Educational Settings: A Mixed-Methods Study in Statistics

The Generative Pre-trained Transformer (GPT) is a highly advanced natural language processing model. This model can generate conversation-style responses to user input. The rapid rise of GPT has transformed academic domains, with studies exploring the potential of chatbots in education. This research investigates the effectiveness of ChatGPT 3.5, ChatGPT 4.0 by OpenAI, and Chatbot Bing by Microsoft in solving statistical exam-type problems in the educational setting. In addition to quantifying the errors made by these chatbots, this study seeks to understand the causes of these errors to provide recommendations. A mixed-methods approach was employed to achieve this goal, including quantitative and qualitative analyses (Grounded Theory with semi-structured interviews). The quantitative stage involves statistical problem-solving exercises for undergraduate engineering students, revealing error rates based on the reason for the error, statistical fields, sub-statistics fields, and exercise types. The quantitative analysis provided crucial information necessary to proceed with the qualitative study. The qualitative stage employs semi-structured interviews with selected chatbots; this includes confrontation between them that generates agreement, disagreement, and differing viewpoints. On some occasions, chatbots tend to maintain rigid positions, lacking the ability to adapt or acknowledge errors. This inflexibility may affect their effectiveness. The findings contribute to understanding the integration of AI tools in education, offering insights for future implementations and emphasizing the need for critical evaluation and responsible use.

How artificial intelligence shapes the future of biomaterials?

Biomaterials are playing a transformative role in modern medicine, revolutionizing both diagnosis and treatment options. The advent of advanced artificial intelligence (AI) tools is poised to significantly impact the field of biomaterials. AI is expected to play a critical role not only in the design and development of biomaterials but also in establishing new standards and refining definitions within this domain. AI can provide invaluable assistance by offering sophisticated tools to organize and analyze vast amounts of data. By leveraging AI’s capabilities, researchers can efficiently sift through extensive datasets, interpret results, and verify the efficacy of new biomaterials across a wide range of applications. This data-driven approach facilitates a deeper understanding of biomaterial interactions and performance, ensuring that new innovations meet rigorous standards. In this era, refining definitions and establishing a universal understanding are crucial for evaluating biomaterials more efficiently. AI can help achieve this by identifying patterns and correlations that might be overlooked by traditional methods, leading to more precise and reliable criteria for biomaterial assessment. Looking ahead, the integration of comprehensive data and advanced AI tools is expected to significantly enhance the effectiveness and quality of biomaterials. This synergy will ensure that biomaterials can meet the diverse and evolving needs of modern medicine, driving forward innovations that improve patient outcomes and healthcare practices.

Decoding digital interactions: An extensive study of TeamViewer's Forensic Artifacts across Windows and android platforms

The pervasive influence of digital technology has ushered in a new era of connectivity, reshaping the landscape of forensic science and challenging investigators to adapt to evolving methods of digital interaction. Remote access applications (RAAs) like TeamViewer have become integral tools for facilitating remote collaboration and support across various platforms. However, the widespread adoption of such applications has also led to an increase in cybercrimes, underscoring the critical need for meticulous forensic analysis. This study presents a comprehensive examination of TeamViewer's forensic artifacts across Windows and Android platforms, employing advanced forensic techniques such as registry analysis, disk forensics, memory forensics, and Android forensics. By meticulously dissecting digital evidence and uncovering valuable insights into user interactions, configuration settings, and session dynamics, this research aims to enhance understanding of remote access activities and empower forensic investigators with the tools needed to combat cybercrimes effectively. The findings highlight the forensic significance of each investigative approach and underscore the importance of continuous innovation in the field of digital forensics.

Developing survey-based measures of mathematics teachers’ pedagogical technology knowledge: a focus on computational thinking and programming tools

ABSTRACT The aim of this research is to develop a survey to explore Norwegian teachers’ perceptions regarding integrating computational thinking (CT) and programming tools into mathematics teaching by using Rasch analysis. For this purpose, three measures are developed: mathematical knowledge for teaching (with technology), instrumental genesis and personal orientations, aligning with the components of Pedagogical Technology Knowledge framework. The empirical data to validate and refine the developed measures includes a relatively large sample of 356 Norwegian primary and secondary school mathematics teachers. Through the three measures, analysis of the survey data revealed insights into teacher perceptions and challenges in tool adoption. These results, first, highlighted the complexity of tool integration and the necessity of unpacking country-specific competencies linking CT and programming to mathematics. Second, the results underlined a need for qualitative research to delve deeper into teacher perceptions and understandings of CT and programming to address challenges and enhance classroom practices.

Talent Management in the Age of Digital Disruption: Challenges and Opportunities

The rise of digital disruption has fundamentally transformed the landscape of talent management, presenting both challenges and opportunities for organizations. This article examines how businesses are adapting their talent management strategies to cope with the rapid advancements in technology, such as artificial intelligence, automation, and the growing prominence of remote work. The study explores the challenges posed by digital disruption, including the widening skills gap, the need for continuous reskilling, and the increasing competition for top talent in a globalized workforce. On the other hand, digital tools also offer opportunities for organizations to enhance their talent acquisition, development, and retention processes. The use of data analytics and AI-powered recruitment tools, for instance, allows for more efficient and data-driven decision-making in hiring and workforce planning. Furthermore, the integration of learning management systems and virtual collaboration tools facilitates employee development and enables organizations to foster a more agile and adaptable workforce. The article presents case studies from various industries, demonstrating how companies are leveraging digital solutions to overcome talent management challenges while embracing opportunities for growth and innovation. The findings suggest that organizations that prioritize digital transformation and align their talent management strategies with technological advancements are better positioned to thrive in an increasingly competitive and digital-first world.

Exploring the Potential of Immersive Technologies to Enhance Online Learning Experiences and Engagement: A Systematic Literature Review

The purpose of this research is to find out how immersive technology can improve online learning and student engagement. The study investigates how immersive technologies, for example augmented reality (AR) and virtual reality (VR) can be incorporated into online learning environment using a case study methodology. The paper explores how immersive technologies affect online course satisfaction among learners generally through learning results, and student involvement. By conducting a comprehensive literature review, the paper synthesizes existing knowledge and identifies gaps in the literature. In the systematic review in which exclusion and inclusion criteria was used to identify the suitable articles to be analyzed based on the study questions. The outcome of the study revealed that immersive technologies significantly boost learner engagement, improving the student learning outcomes thereby increasing their overall course satisfaction with online courses. However, it also identifies challenges and gaps in current research, emphasizing the need for further exploration. Recommendations for educators and policymakers include investing in immersive technologies learning tools, providing adequate training, fostering collaboration, and addressing issues of access and affordability. This paper sough to enhance the comprehension of how immersive technologies can be strategically leveraged to revolutionize online learning environments. By exploring the integration of advanced digital tools and techniques, the study uncovered various ways in which virtual and augmented reality can significantly improve the effectiveness and engagement of online learning. Ultimately, the findings provide valuable insights that can guide educators, institutions, and educational technology developers in optimizing online learning environments through the thoughtful application of immersive technology.

Integration of parametric design tools with artificial intelligence in the construction industry - a review

Background: Technology continues to reshape the design and execution of construction projects in today's global scenario. Objective: The objectives of the research are focused on providing insight into the most prominent Parametric Design (PD) tools in the construction sector, accurately identifying the Artificial Intelligence (AI) techniques and algorithms used in the PD of buildings and structures, and prominently highlighting the benefits derived from the collaboration between these tools and AI in the context of construction. Methods: The present review is carried out using the Prisma methodology, with a specific search string, and applied to the determined scientific databases, between the years 2015 and 2023 and using exclusion and inclusion criteria. Results: The research concludes that the integration of parametric tools and AI, with special attention to Rhinoceros 3D + Grasshopper, has a significant impact on building through adaptive and complex models. Collaboration with artificial neural networks (ANN) and convolutional neural networks (CNN) enables detailed simulations and automations. Conclusions: The benefits are numerous: optimized designs, automation, improved quality and efficient decision-making, all of which drive innovation in construction projects.

Early Identification and Management of Chronic Kidney Disease: A Narrative Review of the Crucial Role of Primary Care Practitioners.

Early-stage (stage1-3) chronic kidney disease (CKD) has an asymptomatic presentation such that most people with CKD are unaware of their disease status and remain undiagnosed. CKD is associated with multiple long-term conditions (MLTC), or multimorbidity, the most common of these being cardiovascular disease, hypertension, and type2 diabetes. Primary care practitioners (PCPs) are crucial in the early identification and management of patients with CKD. For individuals at high risk of CKD, measurements of estimated glomerular filtration rate, urine albumin-creatinine ratio, and blood pressure should be obtained regularly and recorded in a timely manner. The importance of lifestyle changes in the prevention and management of CKD should also be highlighted. A recent addition to the treatment of CKD in people with and without type2 diabetes has been the recommendation by clinical practice guidelines of a sodium-glucose co-transporter2 (SGLT2) inhibitor alongside a renin-angiotensin-aldosterone system inhibitor as foundational therapy. SGLT2 inhibitors prevent CKD progression and reduce fatal and non-fatal kidney and cardiovascular events, hospitalization for heart failure, and all-cause mortality, and they have a favorable safety and tolerability profile. However, uptake has been slow, particularly in people with CKD without type2 diabetes. A multifaceted approach is required to ensure that people with CKD receive optimal kidney protection. Measures to raise awareness of the importance of early identification and intervention include local/national campaigns via social media and practice-based education; clinical education programs; integration of clinical decision support tools into electronic health records; detection programs built around electronic health records; and good interdisciplinary communication. PCPs at the forefront of multidisciplinary care are best placed to implement the evidence-based clinical practice CKD guidelines for lifestyle modification and guideline-directed medical therapy.

Smart Green Concrete: Innovation of Concrete Materials from Fly ash Waste and Lapindo Mud Integrated Smart E. crassipes Coir Geotextile to Realize Indonesia's Sustainable Infrastructure

Pariaman City, situated near the Semangko fault and the Indo-Australian plate subduction zone, is highly prone to earthquakes and tsunamis. To address this, the Earthquake Buddy Application (EDY App) was developed as an innovative solution for disaster mitigation. EDY App utilizes Geographic Information Systems (GIS) to provide real-time earthquake vulnerability maps, early warnings, and evacuation route suggestions to Temporary Evacuation Sites (TES). It also offers education on earthquake preparedness and facilitates donations for relief efforts. The application’s main contribution lies in its integration of comprehensive disaster management tools into one platform. By providing critical information during emergencies, the app empowers users to make informed decisions, which can reduce casualties in high-risk areas. The app also encourages community preparedness by offering accessible education on earthquake mitigation. EDY App is designed to create a disaster-resilient society in Pariaman, ensuring that residents are better equipped to respond to natural disasters through real-time alerts and evacuation support, ultimately enhancing local preparedness and reducing the potential impact of future earthquakes.

Examining The Integration Of ICT Tools In English Language Teaching: Comparative Analysis Of High School Teachers' Practices In India And Vietnam

This study explores the integration of Information and Communication Technology (ICT) tools in English language teaching practices of high school teachers in India and Vietnam. A survey with chart-based responses was distributed to 60 teachers(30 from each country) from nearby districts. The research investigates howteachers utilize ICT tools, their assessment practices, and perceived effects on student engagement and learning. The findings reveal that both countries employ ICT tools, with a focus on social media platforms, language learning applications, and online dictionaries. Vietnamese teachers demonstrated a slightly higher adoption rate, particularly for virtual reality (VR) and augmented reality (AR) technologies. While paper-based assessments remain prevalent, online tools like Google Forms and online assessment platforms are gaining traction. Teachers reported positive perceptions of ICT tools, believing they enhance student engagement and learning outcomes. However, challenges were identified, including limited access to technology resources and a lack of training on effective ICT integration. This study highlights the need for further research to directly measure the impact of ICT tools on student achievement. Additionally, it emphasizes the importance of addressing teacher training needs to unlock the full potential of ICT tools and create more engaging and effective English language learning experiences for students.

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes.

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.IMPORTANCEThe significance of our work lies in the development of Modular Viromics Pipeline (MVP), an integrated and user-friendly pipeline tailored exclusively for viromics analyses. MVP stands out due to its modular design, which ensures easy installation, execution, and integration of new tools and databases. By combining state-of-the-art tools such as geNomad and CheckV, MVP provides high-quality viral genome recovery and taxonomy and host assignment, and functional annotation, addressing the limitations of existing pipelines. MVP's ability to handle diverse sample types, including environmental, human microbiome, and plant-associated samples, makes it a versatile tool for the broader microbiome research community. By standardizing the analysis process and providing easily interpretable results, MVP enables researchers to perform comprehensive studies of viral communities, significantly advancing our understanding of viral ecology and its impact on various ecosystems.

AI in higher education: unveiling academicians’ perspectives on teaching, research, and ethics in the age of ChatGPT

ABSTRACT This study examines the potentials and challenges of integrating conversational AI (Artificial Intelligence) tools in higher education. Specifically, the study delves into how ChatGPT is reshaping teaching, learning, research, and student evaluation while addressing ethical concerns such as academic integrity. We employ a qualitative research design based on semi-structured interviews with 12 highly accomplished academicians from North America, Asia, and Europe’s higher education institutions. Our analysis reveals ChatGPT’s potential to uplift educational productivity, learning outcomes, creativity, and idea generation but raises apprehensions about academic honesty, authenticity, and over-reliance. While ChatGPT can enhance educational practices such as brainstorming, analysis, and skills acquisition, its unchecked use may impede learning, critical thinking, and research quality. The study advocates for the responsible AI use in academia, underscoring educators and policymakers to establish policies guiding conversational AI ethical applications. Thus, training faculty and executing institutional policies are essential for effective and ethical integration of ChatGPT and other AI tools in higher education. Finally, this research contributes to ongoing research on AI in education by providing valuable insights that can harness potential of these transformative tools while upholding academic integrity and standards.

Secret Key Extraction using Keyloggers

The application of keylogger technology for secret key extraction within a message-sending system is presented with practical example of its implementation in real-world scenarios. Keyloggers, designed to capture keystrokes, are repurposed to intercept cryptographic key input during the process of message encryption and decryption. By deploying keyloggers in a controlled environment, the sensitive cryptographic keys can be extracted from user interactions with the messaging application. The integration of keylogger tools with the messaging system includes technical details of their deployment and the methodologies used to capture and analyze keystrokes associated with cryptographic operations.

What facilitates the socio-spatial reattachment after a disaster? A sense of place approach

Disasters disrupt the socio-spatial bonds of the people and communities that they affect. Even though there is a large body of research on the impacts that these events produce, we know less about how people reconnect to transformed environments. To understand how the re-establishment of ties is experienced, in this study, we examine how sense of place develops in post-disaster settings. We conducted 18 group interviews and 80 walking interviews (n=205), distributed across three cases of emblematic disasters in Chile caused by a volcanic eruption, an earthquake and tsunami, and a fire. We used thematic analysis to examine these interviews. Results show that sense of place, in connection with the re-establishment of bonds, is produced in the assemblage of natural, material, personal, and community aspects, grouped into three major topics: appropriation of space, opening of the self, and community activation. We discuss the relevance of natural landscapes and socio-ecological networks for well-being, as well as the need to address the psychosocial working-through dimension in reconstruction policies. We conclude that sense of place is an integrative theoretical-analytic tool that makes it possible to examine the configurations of socio-spatial reattachment in individuals affected by disasters.

FORMATION OF PROFESSIONAL COMPETENCE OF STUDENTS OF TECHNOLOGICAL EDUCATION

This article explores the processes and pedagogical strategies required to develop the professional competence of students in technological education. It emphasizes the relevance of integrating professional knowledge, skills, and values essential for preparing future specialists in technology-related fields. Using contemporary educational paradigms, the article identifies key components influencing the formation of professional competence, including the role of practical activities, interdisciplinary integration, and digital tools. It also provides recommendations for educators and institutions to effectively nurture competencies that align with the evolving demands of the workforce.

Interactive Surgical Training in Neuroendoscopy: Real-Time Anatomical Feature Localization Using Natural Language Expressions.

This study addresses challenges in surgical education, particularly in neuroendoscopy, where the demand for optimized workflow conflicts with the need for trainees' active participation in surgeries. To overcome these challenges, we propose a framework that accurately identifies anatomical structures within images guided by language descriptions, facilitating authentic and interactive learning experiences in neuroendoscopy. Utilizing the encoder-decoder architecture of a conventional transformer, our framework processes multimodal inputs (images and language descriptions) to identify and localize features in neuroendoscopic images. We curate a dataset from recorded endoscopic third ventriculostomy (ETV) procedures for training and evaluation. Utilizing evaluation metrics, including "R@n," "IoU= θ," "mIoU," and top-1 accuracy, we systematically benchmark our framework against state-of-the-art methodologies. The framework demonstrates excellent generalization, surpassing the compared methods with 93.67 % accuracy and 76.08 % mIoU on unseen data. It also exhibits better computational speed compared with other methods. Qualitative results affirms the framework's effectiveness in precise localization of referred anatomical features within neuroendoscopic images. The framework's adeptness at localizing anatomical features using language descriptions positions it as a valuable tool for integration into future interactive clinical learning systems, enhancing surgical training in neuroendoscopy. The exemplary performance reinforces the framework's potential in enhancing surgical education, leading to improved skills and outcomes for trainees in neuroendoscopy.

DAVID Ortholog: an integrative tool to enhance functional analysis through orthologs.

The Database for Annotation, Visualization, and Integrated Discovery (DAVID) is a web-based bioinformatics system for the functional interpretation of large lists of genes/proteins generated from high-throughput assays. It has been cited in 72 287 papers since its debut in 2003 as of 23 July 2024. The analysis is usually limited to the species of study. However, the knowledge of genes may be incomplete or unavailable for some species. Model organisms have been studied more extensively and analyzing gene lists in the context of these species can offer valuable insights, helping users better understand the genes and biological themes in their species of interest. We developed DAVID Ortholog for the conversion of gene lists between species. We utilized the ortholog data downloaded from Orthologous MAtrix (OMA) and Ensembl Compara as the base for the conversion. The OMA ortholog IDs and Ensembl gene IDs were converted to DAVID gene IDs and the pairing information of these IDs from these two sources was integrated into the DAVID Knowledgebase. DAVID Ortholog can convert the user's source gene list to an ortholog list of a desired species and the downstream DAVID analysis, in the context of that species, can be continued seamlessly, allowing users to further understand the biological meaning of their gene list based on the functional annotation found for the orthologs. https://davidbioinformatics.nih.gov/ortholog.jsp.

Disparities in mental health screening for veteran women with a new diagnosis of breast cancer.

222 Background: Compared to the general population, mental health disorders, including depression, are higher for Veteran women. Breast cancer (BC) is the most commonly diagnosed cancer in this population and up to 50% of women will experience depressive symptoms after diagnosis, associated with poor prognosis and survival. An increasing number of Veteran women live in rural areas, where access to behavioral health care may be limited. This study sought to evaluate rural disparities in mental health screening and follow-up evaluation after a new BC diagnosis. Methods: Women Veterans with a new BC diagnosis were identified between 2017 and 2021. Data for mental health screenings [Patient Health Questionnaire (PHQ)-2, PHQ-9, General Anxiety Disorder-7] were collected subsequent to the first BC diagnosis. Rurality was defined per the USDA Rural-Urban Commuting Area (RUCA) codes framework to determine rurality based on population density and commuting patterns. Data were abstracted from several large VA and non-VA datasets, including the Corporate Data Warehouse (CDW) Outpatient files, non-VA Care Medical and Pharmacy System files, Program Integrity Tool, and VA Informatics and Computing Infrastructure for Oncology. Rurality data was specified using CDW designations combined with ZIP-code-level USDA RUCA information. All categorical variables were analyzed using chi-square tests. Means were tested using t-tests. Results: 14,225 Veteran women with a new BC diagnosis were identified. The mean age was 63.4 (SD 11.7). The most common documented race was White (30.6%), followed by Black (15.7%). 59.6% lived in an urban setting as opposed to rural setting (40.4%). In the entire population, only 14% had a mental health screening within 3 months of a cancer diagnosis. Veteran women living in an urban setting were more likely to be screened compared to those living in a rural setting (17.4% vs 9.2%, p < 0.001). For women screened within 3 months, most screened negative, regardless of rurality (76.1% rural, 75.7% urban). Of women who had a positive mental health screen within 3 months, only approximately one-third (31.8% rural, 34.2% urban) had additional screening regardless of rurality. However, for those with a negative initial screen, urban Veterans were more likely to have any additional screening (17% vs 10.2%, p = 0.001). PHQ-2 was the most common mental health screening tool used (73.8%) followed by a combination of PHQ-9 and GAD-7 (9.6%). Conclusions: Mental health screening within 3 months of breast cancer diagnosis is low for Veteran women. There is a significant difference in screening between rural and urban populations. It is critical to both improve mental health screening overall in this population and bridge the disparity gap between screening in rural and urban Veteran women. This may impact not only mental health but also breast cancer outcomes during the period encompassing treatment and survivorship.

Exploring barriers and facilitators of ICT in English pronunciation instruction: Perspectives from Jordanian tertiary education

This study examines the barriers and facilitators to integrating ICT in teaching and learning English pronunciation among university lecturers and students. Three female lecturers from a Jordanian government university's Department of Pharmacy, with teaching experience spanning 3 to 10 years, participated, along with six focus groups of male and female students aged 20 to 23 from the Faculty of Pharmacy. Thematic analysis uncovered barriers such as limited ICT access, inadequate training, large class sizes, time constraints, and students' negative attitudes and low ICT skills, alongside facilitators including leadership support, ICT tool accessibility, basic training for students, and consistent ICT integration in assessment and curriculum. Students advocated for lecturer support, mobile device integration, ICT training, smaller class sizes, consistent ICT tool integration, tool accessibility, and specialized pronunciation courses. This study offers fresh insights into understanding perceptions of ICT adoption barriers and facilitators among university lecturers and students, benefiting researchers and policymakers focusing on teacher professional development to enhance ICT integration in English pronunciation teaching and learning at the tertiary level.