Implementation Of Intelligence Research Articles

Analysis of the Implementation of Artificial Intelligence as a Tool for Digital Audiovisual Post-Production

Con el desarrollo de las tecnologías, muchos trabajadores se han visto enfrentados a la automatización de sus cargos. Al igual que la era industrial, la era virtual se está convirtiendo en un conflicto en diferentes campos laborales. En el mundo tanto del cine y de la creación de contenido, la inteligencia artificial ha tomado protagonismo, simplificando varias tareas en la producción y postproducción de contenido. En el arte de la postproducción o edición, existen muchas herramientas que facilitan diferentes trabajos que antes tomaban horas, días, e incluso meses en realizarse, tales como la colorización, los recortes, la animación, entre otras cosas. En este trabajo se indaga en el uso de la inteligencia artificial por medio de distintos softwares que se pueden emplear en la postproducción audiovisual, aprovechando la era digital como una aliada en la realización del séptimo arte.

Three dimensions of artificial intelligence in the system design of high-tech products

Subject. The article investigates the role and place of artificial intelligence in the system design of high-tech products. Objectives. The focus is on determination of artificial intelligence implementation stages in the system design of high-tech products. Methods. We apply general scientific research methods. Results. The study established that artificial intelligence in the system design of high-tech products should be considered as a single software-algorithmic, system-technological, and conceptual-terminological complex. It identified methodological and ideological problems of introducing artificial intelligence into system design. We defined the structure of hybrid intelligence and substantiated the need for its use. We proposed to use the modern methodology of program-target planning to organize a phased, planned, and coordinated introduction of artificial intelligence technologies in the system design of high-tech products. Conclusions. The findings can be used in the development of State scientific and technological programs and plans to create high-tech products for various purposes in the interests of ensuring the sustainability of innovative and technological development and national security of the Russian Federation, given the current geopolitical and economic situation in the world.

Artificial Intelligence in Medicine: from Diagnosis to Treatment

In recent years, medicine has faced the serious challenge of the covid pandemic, due to which representatives of the health care sector had to mobilize forces and resources to jointly overcome these problems. The rapid development of artificial intelligence, its learning capabilities, and in recent years the creation of a neural network opens up wide possibilities for the use of AI in medicine. Aims: To analyze the modern literature on the use of AI for diagnosis and treatment and to analyze what problems may arise with the uncontrolled introduction of artificial intelligence Methodology: When conducting a literature review, an analysis and generalization of data on the research topic from 2019 to 2024 was carried out. The literature search was carried out by keywords using the PubMed search engine. Results: The literature review demonstrated the use of artificial intelligence in medicine, which has grown significantly in recent years and continues its rapid development, which is associated with the improvement of innovative technologies. The use of artificial intelligence in diagnostics is associated with the use of a neural network, which makes it possible to identify digitized images for rapid diagnosis. The use of artificial intelligence in surgery is reflected in the application of da Vinci. Artificial intelligence has been widely used in anesthesiology. Scientific Novelty: The literature search established that the implementation of artificial intelligence in medicine creates certain challenges related to the protection of personal data, and the possibility of error is not excluded when using AI. Conclusion: The use of AI is promising for diagnosis and treatment and helps doctors quickly make a diagnosis and prescribe treatment, but certain challenges created by artificial intelligence must be solved by implementing more reliable personal data protection systems, as well as control over the information reproduced by artificial intelligence.

A Call for Artificial Intelligence Implementation Science Centers to Evaluate Clinical Effectiveness

A Call for Artificial Intelligence Implementation Science Centers to Evaluate Clinical Effectiveness

A Call for Artificial Intelligence Implementation Science Centers to Evaluate Clinical Effectiveness

A Call for Artificial Intelligence Implementation Science Centers to Evaluate Clinical Effectiveness

The use of artificial intelligence in radiology: new possibilities for diagnostic imaging. A literature review

Introduction and purpose: Rapid advances in technology enable innovative solutions to be implemented in modern medicine, relieving healthcare workers by speeding up diagnosis and improving the quality of treatment. The subject of this review is Artificial Intelligence (AI), an innovative form of help in the daily practice of doctors. It offers the opportunity to relieve healthcare workers by accelerating the diagnosis process and improving the quality and effectiveness of treatment. The aim of this paper is to present the current state of knowledge, assess the effectiveness of algorithms in the recognition and interpretation of abnormalities in medical images compared to specialists in radiology and discuss the challenges associated with the implementation of AI in various medical specialties. Brief description of the state of knowledge: Artificial intelligence, especially through machine learning and deep learning techniques, has found wide application in radiology. Many facilities around the world use advanced AI systems in the day-to-day work of radiologists, including the Mayo Clinic, Massachusetts General Hospital, and the University of Tokyo Hospital. Summary: Studies show that while AI algorithms can perform worse than radiologists in some areas, they are at the forefront of others, especially in detecting subtle abnormalities. The effective implementation of artificial intelligence requires addressing regulatory, ethical, and training issues. Despite these challenges, artificial intelligence has the potential to play a key role in the future of radiology and revolutionize medical practice, opening new perspectives and improving the quality of health care.

P-227 Blastocyst rate prediction by artificial intelligence: evaluation of static oocyte images in patients and donors

Abstract Study question Can a deep learning tool accurately predict blastocyst development from single static oocyte images across diverse patient populations? Summary answer The automatic oocyte score was directly correlated with the blastocyst formation rate in conventional treatments using patient oocytes and in the egg donation program. What is known already The implementation of artificial intelligence (AI)-based algorithms has revolutionized gamete and embryo evaluation in assisted reproduction, providing a standardized basis for clinical decisions. Despite these advances, the implementation of AI algorithms in oocyte evaluation has faced unique challenges. This study explores the predictive ability of an AI algorithm on the actual blastocyst arrival rate from static oocyte images, focusing on finding differences or similarities in these predictions between patients and donors. Study design, size, duration This retrospective cohort study analyzed 1092 static images of oocytes before ICSI. The sample included 791 donor and 301 patient oocyte images. A deep learning algorithm was employed to assess oocyte scores, categorizing them into quartiles. The study explored the correlation between oocyte scores and the actual rate of blastocyst arrival for all oocytes. The rates were also compared between patients and donors, assessing an effective cut-off point to improve the accuracy of the predictions. Participants/materials, setting, methods The oocyte images analyzed were captured with cameras connected to microinjection microscopes immediately before the ICSI procedure. Oocyte images and oocyte age were used as input data for the blind analysis performed by the deep learning-based algorithm. The algorithm predicted blastocyst arrival probability for each oocyte, and these predictions were compared with actual outcomes. This evaluation assessed the algorithm’s performance across patient and donor populations, providing valuable insights into its predictive accuracy for blastocyst arrival. Main results and the role of chance Significant differences in blastocyst formation rates were observed between quartiles of scores assigned by the artificial intelligence (AI) algorithm. Oocytes classified in Quartile 1 showed a significantly lower blastocyst arrival rate (54.63±49.99, n = 216) compared to Quartiles 3 (71.88±45.06, n = 224) and 4 (66.82±47.19, n = 220)*. In general, each increased unit in the algorithm was significantly associated with an increased probability of blastocyst formation (OR = 1.10 [95% CI: 1.02-1.16])*. The algorithm successfully predicted the blastocyst arrival rate, with an area under the curve (AUC) of 0.60 (95% CI: 0.56-0.65). When assessing differences between patients and donors, both groups exhibited similar rates in the first quartile (59.68% and 52.60%, respectively) and the three last quartiles combined (67.55% and 66.02%, respectively). However, an effective cutoff point was identified at 2.8 (between first and second quartile), where both populations experienced a notable increase in blastocyst formation rates. (*p<.05) Limitations, reasons for caution This project is limited by its retrospective, single-center nature and small sample size. Consequently, it would be advisable to conduct a large-scale multicenter study to confirm the conclusions of this study. Wider implications of the findings This study highlights the efficiency of the artificial intelligence algorithm in predicting blastocyst arrival from oocyte images. Establishing a cutoff point in oocyte scoring could enhance the management of the egg donation program and provide individualized guidance to patients based on the quality of their gametes. Trial registration number not applicable

P-108 Automated sperm selection software (SiD) provides similar results as human selection for ICSI

Abstract Study question Automated sperm selection or human eye-based sperm selection, does it make a difference on IVF lab outcomes and clinical outcomes? Summary answer Single sperm selection by the AI SiD was as proficient as highly experienced embryologists and embryo development may have improved for inexperienced embryologists What is known already The implementation of artificial intelligence in reproductive medicine has proven effective for a variety of applications. One application for this technology is single sperm selection during ICSI. The computer assisted program SiD, was designed to grade sperm, in real-time, based on motility parameters transforming qualitative sperm assessments into a quantitative score. SiD’s assistance can be used to select the most optimal sperm for ICSI. The application of sperm selection by AI is expected to bypass user bias, fatigue, limited training, while also optimising time. However, there are limited studies correlating the usage of AI for sperm selection with ICSI outcomes. Study design, size, duration 1 year period 608 sibling MII oocytes were randomly divided in two groups; 1-ICSI group (n = 301): ICSI performed with sperm selected by the embryologist and 2- SID-ICSI group (n = 307): ICSI performed with sperm selected using SiD software. Priority was given to sperm classified as “Best” by SID software for ICSI.. Morphology selection was performed before ICSI in both groups. Embryos were cultured during 6 days in the same conditions. Participants/materials, setting, methods Sperm preparation was performed using a microfluidic chamber (Zymot™). Sperm selection was performed as previously described, and ICSI was performed following routine procedures. Biological and clinical outcomes were analysed in both groups and compared including fertilization rate, cleavage rate, embryo and blastocyst development rate, top quality embryo rate, blastocyst development rate and pregnancy rates. Biological outcomes in both groups were also compared based on embryologist seniority. Embryo morphokinetics parameters were analysed using time-lapse system. Main results and the role of chance There was a non significant trend towards better outcomes in the SID-ICSI group for all biological outcomes including fertilization rate (83,1% vs 82,4%), cleavage rate (97,6% vs 97,2%), day 3 embryo development rate (72,9% vs 70,6%), blastocyst development rate at day 5 (49,0% vs 44,8 %), good quality blastocyst development rate at day 5 (45,1% vs 41,5%), and top quality blastocyst development rate at day 5 (25,9% vs 22,2%). Similarly, a non-significant increase was observed in all these outcomes when sperm selection is performed by a junior embryologist. Embryo development was monitored using a timelapse system. Our data show that 2nd polar body extrusion (tPB2) (4,0h vs 4,8 h [p = 0,005]) and early cleavage (t2) (27,3h vs 28,7h [p = 0,05]) happen significantly earlier when SiD is used for ICSI. No significant difference was observed for all other fertilization events (cytoplasmic wave,tPN1,tPN2,presence of cytoplasmic halo,tPNf,and cytoplasmic halo disappearance), and other cleavage timings (t3->t10,tM,tSC,tEC,tSB), and embryonic cell cycles (ECC1,ECC2,ECC3,s2,s3). Euploid rate obtained with in the 2 groups was comparable (53,3% in SID-ICSI vs 44,4% in ICSI). Although there was a trend toward higher cumulative early and clinical pregnancy rate after ICSI-SID, the difference did not reach significance (43,1%vs34,0% and 27,5%vs21,2%, respectively). Limitations, reasons for caution The ZymotTM microfluidic chamber was used for sperm preparation. This device optimises sperm selection, giving rise to an already optimised population of spermatozoa. Future investigations evaluating the effect of SiD automated sperm selection software on biological outcomes in samples prepared with a less effective technique would potentially yield conversing results. Wider implications of the findings This pilot study demonstrated that the usage of the automated sperm selection software SiD gives rise to similar biological outcomes and embryo morphokinetics, indicating its effectiveness in sperm selection. We expect this program would be useful in the presence of less experienced laboratory members helping by standardizing sperm selection. Trial registration number NA

Implementation of artificial intelligence by using Amazon web services to improve services in e-government

The implementation of artificial intelligence has the scope to revolutionize e-government services by enhancing the quality of life for citizens, enhancing operational efficiency, and enabling groundbreaking applications. There is a possibility that this can be achieved through technical improvements. A wide range of artificial intelligence technologies and services are offered by Amazon Web Services, which has the potential to alter how e-government services are provided. The purpose of this abstract is to study the revolutionary potential of artificial intelligence by utilizing Amazon Web Services and to highlight the benefits and possibilities that it provides to the field of e-government. The utilization of artificial intelligence in conjunction with Amazon Web Services has the potential to significantly enhance the quality of e-government services by reducing inefficiencies, enabling the development of creative applications, and enabling the customization of experiences. With the assistance of the artificial intelligence tools and services offered by Amazon Web Services, the public sector can take advantage of the revolutionary power of artificial intelligence while also ensuring that responsible and ethical practices are followed.

Implementation of Artificial Intelligence in Interior Design: Systematic Literature Review

Implementation of Artificial Intelligence in Interior Design: Systematic Literature Review

Neuroscience inspired neural operator for partial differential equations

We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which aims to bridge the gap between theoretical and practical implementation of Artificial Intelligence (AI) algorithms for mechanics applications. With recent developments like the introduction of neural operators, AI's potential for being used in mechanics applications has increased significantly. However, AI's immense energy and resource requirements are a hurdle in its practical field use case. The proposed VS-WNO is based on the principles of spiking neural networks, which have shown promise in reducing the energy requirements of the neural networks. This makes possible the use of such algorithms in edge computing. The proposed VS-WNO utilizes variable spiking neurons, which promote sparse communication, thus conserving energy, and its use is further supported by its ability to tackle regression tasks, often faced in the field of mechanics. Various examples dealing with partial differential equations, like Burger's equation, Allen Cahn's equation, and Darcy's equation, have been shown. Comparisons have been shown against wavelet neural operator utilizing leaky integrate and fire neurons (direct and encoded inputs) and vanilla wavelet neural operator utilizing artificial neurons. The results produced illustrate the ability of the proposed VS-WNO to converge to ground truth while promoting sparse communication.

Integration of architecture and communication: a transversal learning methodology empowered by artificial intelligence tools

With the growing implementation of Artificial Intelligence (AI) in the architectural field, significant challenges arise in education with ethical and social connotations. In the context of Architecture and Communication degrees, AI emerges as an essential tool, especially in the initial stages of architectural design in exploring ideas and conceptualizing projects. This article addresses the complexity inherent in using AI in architecture, highlighting its fundamental contribution to improving visual representation through textual algorithms, and analyzes the relevance of communication as a scientific discipline. In this context, communication in architecture is directed towards advertising and the effective transmission of messages. The article presents a methodology focused on detecting didactic errors among architecture and communication students, thanks to multidisciplinary collaboration. In conclusion, it is highlighted that the appropriate use of AI can boost the generation of creative ideas, allowing students to direct them and complement them in technical aspects. This study highlights the importance of effectively integrating AI into academic training, highlighting its benefits in improving creativity and precision in architectural communication.

Implementing Project-Based Learning Using Artificial Intelligence

Project-based learning in the education system is gaining more and more momentum, as it is promising and contributes to the formation of all the necessary life skills for of students of professional preliminary higher education. It allows the development of their critical thinking and creative abilities. The application of artificial intelligence in education is an actual area of research that contributes to increasing the effectiveness of the educational process. Implementation of project-based learning using artificial intelligence (AI) is becoming increasingly urgent in education. This approach makes it possible to significantly increase the motivation of students, stimulate their independence and creativity, and prepare them to solve real problems in the modern world, where AI plays an increasingly important role. The purpose of the study is to highlight the possibilities and prospects of using artificial intelligence to implement project-based training of students of professional preliminary higher education. The research is conducted using mixed methods, including quantitative and qualitative approaches. Data are analyzed using statistical methods and interviews are conducted with participants in the educational process. During the research and presentation of the material, theoretical methods of scientific knowledge were used: analysis and synthesis, induction and deduction, systematization, and generalization. The author hypothesizes that the use of AI in project-based learning helps to increase the motivation of students, strengthens their independence, improves the skills of working with information and critical thinking, and also stimulates teamwork. It has been established that using artificial intelligence significantly increases the effectiveness of project-based learning. Its main advantages for teachers are described, including the automation of routine tasks. Positive results for students in the form of improved learning outcomes are summarized. Recommendations for the implementation of artificial intelligence services in the educational process, in particular, in project activities, are offered. The main areas for further research are systematized. The results of the study can be used for further development of the theory of project-based learning and artificial intelligence in education. The recommendations developed by the authors regarding the implementation of artificial intelligence in project-based learning. These recommendations can be used by teachers and lecturers to optimize the educational process, increase its effectiveness, and form the competencies necessary for a successful life in modern society for students. Further research can be aimed at developing new methods and tools for using AI in project-based learning, studying its impact on various aspects of students' educational activities, as well as studying the ethical aspects of using AI in education.

Implementation of an Artificial Intelligence Based Learning Management System for Adaptive Learning

In the world of modern education, the need for adaptive and personalized learning systems is becoming increasingly important to accommodate the unique learning needs of each individual. Implementing artificial intelligence (AI) in learning management systems offers great potential for optimizing adaptive learning experiences, where the system automatically adjusts learning content and approaches based on student profiles and responses. This research aims to develop and implement an artificial intelligence-based learning management system to personalize learning. The primary objective is to assess the effectiveness of the system in improving student engagement and learning outcomes and identify factors that influence its success. The method includes developing a system using machine learning algorithms for real-time analysis of student learning data. This research uses a mixed design that combines quantitative and qualitative data to analyze learning outcomes and understand user perceptions of the system. The research results show that this AI-based learning management system successfully increases student engagement and provides effective personalization of learning. Data shows improvements in grades and understanding of the material, especially in concepts students previously found difficult. Implementing an artificial intelligence-based learning management system has proven effective in supporting adaptive learning. This system improves learning outcomes and provides new insights into how AI technology can effectively integrate into educational contexts to support diverse and dynamic learning needs. The research also identified several challenges, including the need for more significant and varied training data and smoother integration with school curricula.

Application of artificial intelligence in retail

In the context of the contemporary Industry 4.0 concept, artificial intelligence (AI) has become an integral component of human lifeʼs success, the impact of which is increa­singly difficult to overestimate today. With the advent of AI, the everyday life of individuals has noticeably transformed. Neural networks are capable of working in an algorithmic way, adapt, learn, and even generate new inven­tions within a short span of time. Since the concept of AI appeared, fervent debates have revolved around its advantages and opportu­nities for modern society, ethical frameworks of application, risks, and the negative impact of AI on various professional spheres. However, the revolutionary changes brought about by AI, enabling individuals to delegate routine tasks and freeing up time for creative endeavors, remain indisputable. Despite the high significance of previously conducted research, the state and potential opportunities of industrial application of AI remain insufficiently explored. The purpose of the study is to determine the current trends in the development of artificial intelligence, to assess the actual state of use and to justify recommendations for the future use of AI systems in the retail sector. It has been deter­mined that the introduction of AI technologies is a modern global trend and one of Ukraineʼs priorities. It has been proven that the complex realities of domestic retail development unveil opportunities for digital transformation and the intensification of AI implementation. The current state of AI development has been examined: the leading countries in artificial intelligence implementation have been identified, and the retail sector has been ranked fourth among industries with high potential for the application of advanced technologies. To gain an understanding of AI development in retail, a survey was conducted, the results of which delineated business processes where AI techno­logies are actually applied and specified the goals of their application. The priority opera­tional processes of retail enterprises for the implementation of advanced technologies have been identified and ranked by the significance of AI systems, which are most relevant to modern retail. A list of AI systems has been sorted according to their purpose and functional capa­bilities in retail.

Pros And Cons Of Artificial Intelligence (Ai) Technology By Local Guide In Dusun Adat Sade Rembitan Village In Central Lombok - NTB - Indonesia

Technology is rapidly advancing today, with the most popular technology known as Artificial Intelligence (AI). This technological innovation greatly benefits the tourism sector. However, the implementation of Artificial Intelligence (AI) technology often sparks debate, including in the tourism industry. This study aims to identify the pros and cons related to the presence and use of AI technology by local guides at tourist sites that still maintain the authenticity of culture, such as in the Dusun Adat Sade, Rambitan Village, Central Lombok, West Nusa Tenggara, Indonesia. The research was conducted using an inductive approach, with a qualitative methodology, a case study research strategy, and a cross-sectional research time frame. Data collection and analysis were performed using data reduction, data display, and conclusion drawing. Data analysis utilized content analysis, categorizing data into specific themes or categories. Triangulation of methods and sources was employed for data validity. Methodological triangulation involved three methods: observation, interviews, and documentation. Additionally, source triangulation was conducted by interviewing 8 informants. The research findings categorized data into the following: Pros and Contras regarding Artificial Intelligence (AI) technology. After analyzing and processing the data from both categories, the majority of local guides in the Sade Traditional Village, Rembitan Village, exhibit a predominantly contra or skeptical attitude towards Artificial Intelligence (AI) technology due to concerns about Artificial Intelligence (AI) taking over their jobs. However, they still acknowledge technological advancements that facilitate their service to tourists. To further assess the extent to which Artificial Intelligence (AI) technology assists local guides, especially at tourist sites, further research on the benefits of Artificial Intelligence (AI) for supporting and facilitating local guides in their work is needed

Role of artificial-intelligence-assisted automated cardiac biometrics in prenatal screening for coarctation of aorta.

Although remarkable strides have been made in fetal medicine and the prenatal diagnosis of congenital heart disease, around 60% of newborns with isolated coarctation of the aorta (CoA) are not identified prior to birth. The prenatal detection of CoA has been shown to have a notable impact on survival rates of affected infants. To this end, implementation of artificial intelligence (AI) in fetal ultrasound may represent a groundbreaking advance. We aimed to investigate whether the use of automated cardiac biometric measurements with AI during the 18-22-week anomaly scan would enhance the identification of fetuses that are at risk of developing CoA. We developed an AI model capable of identifying standard cardiac planes and conducting automated cardiac biometric measurements. Our data consisted of pregnancy ultrasound image and outcome data spanning from 2008 to 2018 and collected from four distinct regions in Denmark. Cases with a postnatal diagnosis of CoA were paired with healthy controls in a ratio of 1:100 and matched for gestational age within 2 days. Cardiac biometrics obtained from the four-chamber and three-vessel views were included in a logistic regression-based prediction model. To assess its predictive capabilities, we assessed sensitivity and specificity on receiver-operating-characteristics (ROC) curves. At the 18-22-week scan, the right ventricle (RV) area and length, left ventricle (LV) diameter and the ratios of RV/LV areas and main pulmonary artery/ascending aorta diameters showed significant differences, with Z-scores above 0.7, when comparing subjects with a postnatal diagnosis of CoA (n = 73) and healthy controls (n = 7300). Using logistic regression and backward feature selection, our prediction model had an area under the ROC curve of 0.96 and a specificity of 88.9% at a sensitivity of 90.4%. The integration of AI technology with automated cardiac biometric measurements obtained during the 18-22-week anomaly scan has the potential to enhance substantially the performance of screening for fetal CoA and subsequently the detection rate of CoA. Future research should clarify how AI technology can be used to aid in the screening and detection of congenital heart anomalies to improve neonatal outcomes. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Ethical implementation of artificial intelligence in the service industries

ABSTRACT This study employs a systematic literature review (SLR) combined with bibliometric analysis to investigate the ethical implementation of Artificial Intelligence (AI) in the service industries. This research uncovers key challenges such as privacy, bias, transparency, and accountability, emphasizing the critical need for ethical AI practices in service sectors handling sensitive customer data. Findings reveal that AI's ethical implementation is crucial in areas like decision support, customer engagement, automation, and new service development. The analysis provides actionable insights into enablers, including ethical guidelines, human oversight, comprehensive training, and adaptive organizational culture, which are essential for unlocking AI's potential and mitigating risks. The study offers a roadmap for future research, advocating interdisciplinary collaboration, customer co-creation in ethical frameworks, and sector-specific policy adaptation, ultimately aiming to build responsible and trustworthy AI in the service industries.

AI in education: Comparative perspectives from STEM and Non-STEM instructors

The integration of artificial intelligence into education has emerged as a promising avenue for enriching teaching and learning experiences. Nevertheless, the successful implementation of artificial intelligence in educational contexts hinges upon various factors, one of which is the perspective of instructors. With this in mind, this study aimed to examine the perspectives of 536 instructors in STEM and non-STEM disciplines regarding AI integration. The respondents’ thoughts, opinions, and concerns regarding advantages, disadvantages and challenges were gathered through an online questionnaire featuring both closed and open-ended questions. Additionally, a series of semi-structured interview sessions were conducted with a cohort of instructors to collect qualitative and quantitative data. The findings revealed that both STEM and non-STEM instructors expressed positive attitudes toward the integration of AI technologies into education. However, notable differences in responses and concerns were also identified in relation to the perceived capabilities and limitations of AI technologies within educational contexts. The results further elucidated a spectrum of opinions on the benefits (e.g., scalability and tirelessness), drawbacks (e.g., deepfake technology and comfort-seeking behavior), and potential challenges (e.g., educational disillusionment and espionage) associated with AI integration. The study concluded by discussing the implications of these findings for STEM and non-STEM education and offering recommendations for the effective and ethical integration of AI technologies in classrooms.

Challenges and opportunities of artificial intelligence implementation within sports science and sports medicine teams.

The rapid progress in the development of automation and artificial intelligence (AI) technologies, such as ChatGPT, represents a step-wise change in human's interactions with technology as part of a broader complex, sociotechnical system. Based on historical parallels to the present moment, such changes are likely to bring forth structural shifts to the nature of work, where near and future technologies will occupy key roles as workers or assistants in sports science and sports medicine multidisciplinary teams (MDTs). This envisioned future may bring enormous benefits, as well as a raft of potential challenges. These challenges include the potential to remove many human roles and allocate them to semi- or fully-autonomous AI. Removing such roles and tasks from humans will make many current jobs and careers untenable, leaving a set of difficult and unrewarding tasks for the humans that remain. Paradoxically, replacing humans with technology increases system complexity and makes them more prone to failure. The automation and AI boom also brings substantial opportunities. Among them are automated sentiment analysis and Digital Twin technologies which may reveal novel insights into athlete health and wellbeing and team tactical patterns, respectively. However, without due consideration of the interactions between humans and technology in the broader system of sport, adverse impacts are likely to be felt. Human and AI teamwork may require new ways of thinking.