Models of Expertise in Knowledge Acquisition

The research aimed to identify the reality of using artificial intelligence in educational research from the point of view of graduate students at the College of Education at Taif University. The study sample consisted of 32.8% of the study population consisting of (128 male and female students) enrolled in the master’s degree- at the College of Education- Taif University In the specializations of (educational leadership, pedagogy, educational policies, special education, curricula, teaching methods and educational techniques) in the second semester of the academic year 1444-1445 AH, and using the questionnaire tool, the research concluded the following: 1- That students use artificial intelligence to a high degree in all stages of research. 2- One of the most important problems in using artificial intelligence in research is related to reliability and security, and the lack of a clear policy for applying artificial intelligence in educational research. 3- One of the most important controls that students believed should be available (formulation The Artificial Intelligence Ethics Document, the establishment of the University Center for Artificial Intelligence Ethics, and the enhancement of cyber security skills for the educational researcher, while there are no statistically significant differences between the sample members in their responses to the questionnaire axes due to specialization and level While the most widely used artificial intelligence program in educational research was Chat GPT, it affected the reality axis without the rest of the axes. Among the most important recommendations reached by this study: establishing a university center for the ethics of artificial intelligence, drafting a document on the ethics of artificial intelligence in educational research, and adding it as a course for students. Undergraduate and postgraduate studies

Hazardous waste capacity assurance planning: Volumes for one‐time remediation

<p indent="0mm">The development of sexual reproductive organism embryos begins with the fertilization process, where sperm and egg unite, followed by a series of intricate and orchestrated changes, leading to continuous embryo development and tissue differentiation, culminating in the formation of a complete biological individual. Conducting in-depth and systematic exploration of this intricate and complex process represents a monumental and challenging undertaking. Artificial intelligence, is aimed at emulating the human brain and assisting in addressing more complex problems. With the continuous updating of computing devices and the successive ascent of computing capabilities, artificial intelligence has experienced rapid development, achieving groundbreaking advancements in various fields. In the field of life sciences, artificial intelligence has a wide range of applications, especially in the study of gamete and embryo development, where it was applied in the relatively early days. From gamete selection to embryo quality assessment, and from investigating molecular mechanisms underlying embryo development to elucidating the interconnectedness among various modules during embryo development, including time, phenotype, and molecular features, artificial intelligence has been effectively utilized, demonstrating significant advantages. Hence, exploring how to better leverage artificial intelligence in the research process of gamete and embryo development is a question worthy of deeper investigation at this stage. This paper provides a comprehensive review of the current applications of artificial intelligence in gamete and embryo development research, along with an analysis of the challenges and directions for its future development.

Countries, companies, and universities are increasingly competing over top-tier artificial intelligence (AI) researchers. Where are these researchers likely to immigrate and what affects their immigration decisions? We conducted a survey $(n = 524)$ of the immigration preferences and motivations of researchers that had papers accepted at one of two prestigious AI conferences: the Conference on Neural Information Processing Systems (NeurIPS) and the International Conference on Machine Learning (ICML). We find that the U.S. is the most popular destination for AI researchers, followed by the U.K., Canada, Switzerland, and France. A country's professional opportunities stood out as the most common factor that influences immigration decisions of AI researchers, followed by lifestyle and culture, the political climate, and personal relations. The destination country's immigration policies were important to just under half of the researchers surveyed, while around a quarter noted current immigration difficulties to be a deciding factor. Visa and immigration difficulties were perceived to be a particular impediment to conducting AI research in the U.S., the U.K., and Canada. Implications of the findings for the future of AI talent policies and governance are discussed.

A brief summary of a lengthy work of writing, such as a book, research paper, or thesis, is called an abstract. It describes the key concepts, methods, findings, and conclusions, and is usually positioned at the beginning of the document. start of academic or professional documents, abstracts help an abstract serve as a tool that allows readers can rapidly comprehend a work’s primary goal and focus without the requirement for to read the entire document. It enables readers to effectively understand the content’s goals and relevance. is to provide a preview of the information, helping readers decide whether the full document is relevant to their research or interests. It summarizes the main problem or question addressed, the methods or approach used to investigate it, and the significant outcomes or conclusions drawn. It typically contains a brief introduction, methodology, results. Abstracts are commonly used in academic journals, conferences, and other scholarly publications, as well as in research proposals and grant applications. A range of technologies collectively referred to as artificial intelligence (AI) enable computers to do complex tasks, such as data analysis, spoken or written language comprehension and translation, visual perception, and more. making suggestions. A well-written abstract can improve the visibility and accessibility of a work, as it allows researchers and professionals to quickly assess its relevance. Clear, concise, and accurate representation of the core ideas is essential for a successful abstract. Research significance: Artificial intelligence (AI) research is transformative, driving advancements in technology, society, and the economy. It fosters innovation in areas like automation, robotics, and data analysis, enabling more efficient systems and unlocking new possibilities. In healthcare, AI enhances diagnostics, personalizes treatments, and accelerates drug discovery, while in education, it provides adaptive learning tailored to individual needs. Economically, AI boosts productivity, creates new markets, and improves decision-making through data-driven insights. It also discusses global concerns including food security, public health, and climate change. can be addressed by enhancing and streamlining solutions and predictive modeling. AI research raises important ethical and philosophical questions, ensuring fairness, transparency, and responsible use. Additionally, it strengthens national and global security through advancements in defense and cybersecurity. Beyond practical applications, AI deepens our understanding of cognition and expands exploration into uncharted territories. AI research is a cornerstone of progress, reshaping society and empowering humanity to address the future. Methodology: The Demetal Methodology It is a structured method employed to study and represent intricate systems. by examining interrelationships among various factors. It combines qualitative and quantitative techniques to identify cause-effect relationships, prioritize issues, and facilitate decision-making. This methodology is particularly effective in fields like systems engineering, supply chain management, and social sciences, where interdependencies play a significant role. It begins with identifying key factors or components of a system, followed by expert input to establish connections and weightages between them. Using tools like pairwise comparison matrices, it quantifies these relationships, enabling visualization through causal diagrams or directed graphs. Alternative: Water quality, spa amenities, affordability, ease of access, and reputation. Evalution parameter: Water Quality, SPA Facilities, Cost, Accessibility, Reputation. Result: Water Quality (1): The first rank which may imply that basic standards are assumed or that it is less influential in decision-making compared to other factors. SPA Facilities (5): The last rank, emphasizing the importance of high-quality amenities and treatment options for a superior customer experience.

The use of artificial intelligence (AI) as a diagnostic and decision-support tool is increasing in dermatology. The accuracy of image-based AI tools is incumbent on images in training sets, which requires patient consent for sharing. This study aims to understand individuals' willingness to share their images for AI and variables that influence willingness. In an online survey administered via Amazon Mechanical Turk, sketches of the hand, face, and genitalia assigned to two use cases employing AI (research vs. personal medical care) were shown. Participants rated willingness to share the image on a 7-point Likert scale. Of the 1010 participants, individuals were most willing to share images of their hands (81.2%), face (70.3%), and lastly genitals (male: 56.8%, female: 46.7%). Individuals were more willing to share for personal care versus research (OR 0.77 [95% CI 0.69-0.86]). Willingness to share was higher among males, participants with higher education, tech-savvy participants, and frequent social media users. Most participants were willing to share images if offered monetary compensation, with face images requiring the highest payment (mean $18.25, SD 20.05). Only 38.7% of individuals refused image sharing regardless of any monetary compensation, with the majority of this group unwilling to share images of the genitals. This study demonstrates overall public support for sharing images to AI-based tools in dermatology, with influencing factors including image type, context, education level, technology comfort, social media use, and monetary compensation.

This letter highlights the necessity of nursing engagement in artificial intelligence (AI) research and its potential impact on healthcare. As AI transforms patient care, diagnostics, and operational efficiency, it is crucial for nurses, the largest healthcare workforce, to engage in AI development actively. Nurses’ experiential knowledge and ethical perspectives can enhance AI tools, ensuring they are patient-centered and practically applicable. This letter underscores the importance of interdisciplinary collaboration and advocates for increased nursing involvement in AI research to achieve better healthcare outcomes.

Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine

It is the contention of this paper that ethics of work ought to be anthropological, and artificial intelligence (AI) research and development, which is the focus of work today, should be anthropological, that is, human-centered. This paper discusses the philosophical and theological implications of the development of AI research on the intrinsic nature of work and the nature of the human person. AI research and the implications of its development and advancement, being a relatively new phenomenon, have not been comprehensively interrogated in the social and ethical teachings of the Catholic Church. This paper, therefore, proposes a path for this interrogation by expounding a discourse which is believed to be epistemically helpful in the developing discourse of AI in the ethical and social teachings of the Church. The advancement in the research on AI is not only redefining the meaning of work, but, even more so, it is questioning the metaphysical notion of the human person and the theological notion of work as an intrinsic part in the selfhood and dignity of the human person.

Meta-learning, social cognition and consciousness in brains and machines

In this final set of explorations/meditations (of three), we examine the requirements for developing artificial general intelligence (AGI) through the lens of human cognitive architecture, with particular emphasis on the role of narrative selfhood and social cognition. Drawing on perspectives from cognitive science, philosophy of mind, and artificial intelligence research, we critically evaluate current claims about the capabilities of large language models, particularly regarding their purported achievements of theory of mind and self-awareness. We argue that genuinely human-like artificial intelligence may require more than sophisticated pattern recognition and language modeling, potentially necessitating the development of coherent narrative self-models and rich causal understanding. Special attention is given to the relationship between consciousness, conscience, and trustworthy AI systems, suggesting that meaningful artificial intelligence may require forms of richly-embodied and socially-embedded development to achieve robust and reliable functionality. We conclude by proposing that the path to artificial general intelligence may require recapitulating aspects of human cognitive development, particularly regarding the construction of narrative identity and social-moral reasoning capabilities. This analysis has implications for both the technical development of AI systems and the ethical frameworks through which we evaluate artificial minds.

Artificial intelligence (AI) is quickly transforming business operations and society, as AI capabilities are incorporated into applications ranging from mobile personal assistants to self-driving cars. The potentially disruptive nature of AI calls for an extensive investigation into all aspects of AI-human interactions at individual, group, organizational and market levels. However, there is paucity of academic information systems (IS) research in this area that goes beyond the development and testing of specific narrow AI capabilities. AI represents an important opportunity for organizational and behavioral IS researchers, but also presents challenges associated with the underlying complexity of AI technologies and the diversity of AI applications. Understanding how existing AI research and business practice relate to traditional areas of IS research is an important step towards creating a comprehensive behavioral and organizational AI research agenda. This dissertation seeks to achieve a dual purpose in a series of three essays. Essay 1 seeks to understand the current state of business AI research and practice in business through a quantitative literature review, relate the findings to traditional IS research areas, and identify potentially fruitful research areas for AI-focused IS research. Essays 2 and 3 seek to address specific research questions related to one of such research areas, namely, human interactions with AI enabled applications. Essay 2 focus on user experience with a chatbot, a popular AI application, and Essay 3 explores how user experiences with AI assistant apps differ from their interactions with more traditional IT artifacts.

Application projects, may it be in healthcare and the public sector or elsewhere, have the potential to advance foundational (“genuine”) artificial intelligence (AI) research. Unfortunately, insights from specific application projects are rarely propagated back to AI research. This article argues for ways to facilitate such backpropagation and how the contributions in this special issue enable exactly this backpropagation. It also addresses the challenges that come along with high-risk application project, which frequently occur in the area of healthcare and the public sector due to the sensitivity of the subjects.

There is increasingly much agreement in the cognitive sciences that human cognition is embodied &amp;ndash; to some significant extent. However, there is much less agreement regarding in what sense(s) cognition is embodied. In particular, there is much agreement that sensorimotor interaction with the environment is fundamental to cognition. From a historical perspective, this emphasis on the sensorimotor body is at least partly due to the crucial role that the conceptual shift in artificial intelligence (AI) research &amp;ndash; from computational to robotic models &amp;ndash; has played in the overall development of embodied cognitive science. Most embodied AI research, however, in particular work on symbol grounding and related approaches, reduces the body to a mere sensorimotor interface for internal processes that are still just as computational as they were 30&amp;ndash;40 years ago. In Harnad's terms, this type of AI has only gone from a computational to a robotic functionalism. In theory, this could be limited to AI research as such, but in practice the view of the physical body as the computational mind's sensorimotor interface to the world still pervades much of cognitive science and philosophy of mind. The argument presented here is that there are good reasons to say that at least today's robots are in fact not embodied &amp;ndash; in any sense that would allow for anything even close to human embodied cognition and intentionality &amp;ndash; and that this has implications for social interactions between humans and robots.

StarCraft II (SC2) is a real-time strategy science-fiction video game developed by Blizzard Entertainment. Known for its complex state space and open-source environment [8], SC2 has become a popular domain for Artificial Intelligence (AI) research. This paper leverages the advances in AI research from SC2 to build human-autonomy teaming (HAT) aids, AI-driven software tools for human interactivity, for players to improve their skills. The human-machine interface (HMI) that houses these tools breaks the game into different components and visually represents each HAT aid to increase situational awareness and decrease response time.