Research on the Path of Artificial Intelligence Enabling Free Diving Training

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

Digital revolution and the resultant emergence of Industry 4.0 has driven the incorporation of Artificial intelligence (AI) in Industrial education to enhance skills development in the industry. However, there is a lack of adequate empirical evidence on the integration of Artificial intelligence in industrial education. To fill this gap, this study reviewed previous studies on the adoption of AI in Industrial education and examined the frequency of occurrence of variables obtained in the studies reviewed. Relevant literature was screened and reviewed to find empirical evidence to support findings. A systematic review of 14 studies provided insights into the current applications, benefit and challenges of AI integration in industrial education. The study found that the most cited applications of AI is Adaptive and personalised learning systems, which customise workers/learners’ information based on their interaction with learning content. Other applications are augmented simulators for real-time feedback, virtual mentors, and intelligent tutoring systems which replicate real-life interaction with professionals among others. Majority of the studies found increased engagement and improved learning outcomes and skills development as benefit of AI integration in Industrial education. Other benefits are promotion of early identification of learning challenges and timely intervention and feedback, improvement in administrative efficiency and support, personalisation of learning. Notable challenges were skills and capacity gaps, lack of infrastructure and AI resources, curriculum issues and difficulty in integrating AI into current curriculum, ethical and privacy concerns among others. Based on the findings of the study, it was recommended that the skill gap should be filled with training in AI applications and use, investment in AI infrastructural development should be explored, industry collaboration and partnership in the area of needs should be considered, AI marketing and literacy should be adopted in industries, all AI intervention should be a continuing and lifelong process to ensure sustainability.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

Background. In the context of the continuous shifts in the modern labour market and the escalating competitiveness among students of maritime specialties, there is an increasing demand for the training of highly qualified specialists in the maritime industry, who are able to interact effectively with international partners at a professional level. In this respect, the use of generative artificial intelligence (AI) to improve foreign language teaching methods for students of maritime subjects is especially relevant. This is fully justified under the conditions of globalisation and the digital transformation of the educational sphere. Purpose. The purpose is to determine the effectiveness of generative AI application in the process of building foreign language professional communicative competence of maritime students by modelling realistic scenarios of job interviews in shipping companies. Materials and methods. The present study adopted a comprehensive approach, integrating theoretical and empirical methods of analysis. The theoretical basis was established through a comprehensive review of normative and educational documents, with the aim of identifying the fundamental requirements for the educational process. This was complemented by a thorough examination of scientific and educational literature, as well as methodological literature, on the application of AI in education and linguodidactics, thus enabling the identification of current trends and the potential of technologies for teaching maritime students. The empirical element of the study involved modelling, describing and analysing professional interview scenarios using generative AI models, the observation of students' learning activities, and the conduction of interviews to obtain feedback on the perception and effectiveness of the new techniques. Results. The research yielded an innovative methodology for training students of maritime specialities at Sevastopol State University for professional interviews in international shipping companies. The developed three-stage training model, combining the creation of a database of questions, simulation of interviews using generative (AI) and practising skills through business games, demonstrated high efficiency in the formation of both linguistic and professional competences of students, in addition to soft skills deemed integral for a successful career in the maritime industry. The integration of generative AI within the training framework of maritime students has been found to expand the scope of opportunities for effective preparation for professional interviewing. The implementation of this methodology has been identified as a significant contributor to the modernisation of the educational process, rendering it more personalised, effective and aligned with the precise requirements of the contemporary labour market.

The integration of Artificial Intelligence (AI) into cloud computing represents a pivotal shift in the technological landscape, offering unprecedented opportunities for innovation across industries. Deploying AI applications at scale on the cloud poses unique challenges, necessitating the development of scalable cloud architectures tailored to meet the computational demands and data processing needs inherent to AI. This article explores the foundational aspects of cloud computing architectures, emphasizing the principles of scalability essential for AI applications. It delves into the core challenges faced when deploying AI on the cloud, such as computational requirements, data management, network constraints, and security concerns. Further, it presents various architectural models that facilitate scalability, including containerization, serverless computing, and cloud-native AI services, drawing from realworld case studies to illustrate effective strategies and best practices. Additionally, the article examines performance optimization techniques, security considerations, and the future directions of cloud-based AI deployments, highlighting the role of emerging technologies such as quantum computing and edge AI. By providing a comprehensive overview of scalable cloud architectures for AI applications, this article aims to guide researchers, practitioners, and organizations in leveraging cloud computing to its full potential, thereby enabling more efficient, secure, and scalable AI solutions.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

BackgroundThe integration of artificial intelligence (AI) holds substantial potential to alter diagnostics and treatment in health care settings. However, public attitudes toward AI, including trust and risk perception, are key to its ethical and effective adoption. Despite growing interest, empirical research on the factors shaping public support for AI in health care (particularly in large-scale, representative contexts) remains limited.ObjectiveThis study aimed to investigate public attitudes toward AI in patient health care, focusing on how AI attributes (autonomy, costs, reliability, and transparency) shape perceptions of support, risk, and personalized care. In addition, it examines the moderating role of sociodemographic characteristics (gender, age, educational level, migration background, and subjective health status) in these evaluations. Our study offers novel insights into the relative importance of AI system characteristics for public attitudes and acceptance.MethodsWe conducted a factorial vignette experiment with a probability-based survey of 3030 participants from Germany’s general population. Respondents were presented with hypothetical scenarios involving AI applications in diagnosis and treatment in a hospital setting. Linear regression models assessed the relative influence of AI attributes on the dependent variables (support, risk perception, and personalized care), with additional subgroup analyses to explore heterogeneity by sociodemographic characteristics.ResultsMean values between 4.2 and 4.4 on a 1-7 scale indicate a generally neutral to slightly negative stance toward AI integration in terms of general support, risk perception, and personalized care expectations, with responses spanning the full scale from strong support to strong opposition. Among the 4 dimensions, reliability emerges as the most influential factor (percentage of explained variance [EV] of up to 10.5%). Respondents expect AI to not only prevent errors but also exceed current reliability standards while strongly disapproving of nontraceable systems (transparency is another important factor, percentage of EV of up to 4%). Costs and autonomy play a comparatively minor role (percentage of EVs of up to 1.5% and 1.3%), with preferences favoring collaborative AI systems over autonomous ones, and higher costs generally leading to rejection. Heterogeneity analysis reveals limited sociodemographic differences, with education and migration background influencing attitudes toward transparency and autonomy, and gender differences primarily affecting cost-related perceptions. Overall, attitudes do not substantially differ between AI applications in diagnosis versus treatment.ConclusionsOur study fills a critical research gap by identifying the key factors that shape public trust and acceptance of AI in health care, particularly reliability, transparency, and patient-centered approaches. Our findings provide evidence-based recommendations for policy makers, health care providers, and AI developers to enhance trust and accountability, key concerns often overlooked in system development and real-world applications. The study highlights the need for targeted policy and educational initiatives to support the responsible integration of AI in patient care.

A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff.

Introduction: Maternal and child health provide critical indicators of the health status of a country. Despite gains made over the decades, challenges continue to plague health care systems, especially in some developing countries. The integration of Artificial Intelligence (AI) into midwifery practice can potentially offer innovative opportunities which can revolutionize the way care is delivered. This review aims to identify evidence of the use of AI to enhance midwifery practice thereby reducing maternal morbidity and mortality. Objective: To determine the applicability of AI in midwifery practice to reduce maternal morbidity and mortality. Methods: Ninety-one articles were obtained using key words artificial intelligence, artificial intelligence in midwifery practice, maternal morbidity and mortality, midwifery practice. Sources of data include Google Scholar and EBSCO host. A total of eight (23) articles met inclusion criteria using key terms. Results: After inclusion/exclusion criteria was applied, 23 articles were selected. Seven (30. 4% addressed AI in midwifery, 7 (30.4 %) health care, 4 (17.3 %) use of AI in medicine, 2 (9%) AI, 1 (4.3) deep learning, I (4.3%) International Federation of Midwives and 1 (4.3%) World Health Organization Fact Sheet. Conclusion: The literature is sparse with evidence of the application of AI in midwifery practice to aid in the reduction of maternal morbidity and mortality as well as its application in low-resourced countries of the Caribbean region. Based on the use of artificial intelligence by other professions such as medicine to identify and prevent maternal morbidity, its use in by midwives is likely to enhance midwifery practice.

Assessment of MRI technologists in acceptance and willingness to integrate artificial intelligence into practice

This study explores the integration of Artificial Intelligence (AI) into the Reverse Logistics Process (RLP) to enhance decision-making, operational efficiency, and resource recovery. Through a systematic literature review, the applications of AI across critical RLP stages (planning, execution and control) are identified. Key findings highlight the transformative role of AI in optimizing network design, improving product collection and inspection, and supporting decision-making for the disposition of recovered items. AI technologies and functionalities such as machine learning, predictive analytics, and decision-support systems demonstrate significant potential for automating complex processes, reducing operational costs, and improving logistical precision. However, barriers to AI adoption in the RLP include high implementation costs, organisational resistance, lack of specialist personnel, and limited technological infrastructure. The review also identifies enablers such as advancements in the Internet of Things, blockchain, and big data analytics, which facilitate AI adoption and integration in RLP. The study concludes that AI is essential for establishing resilient and adaptive RL systems and offers substantial opportunities to address uncertainty while improving efficiency and decision-making. Future research should focus on overcoming adoption barriers, advancing predictive models, and integrating AI with other Industry 4.0 technologies to enhance RLP outcomes. This review fills a significant gap in the literature by offering a structured analytical framework that categorises AI applications based on decision types and RLP stages, providing an integrated perspective not previously addressed in the field.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

This research study investigated artificial intelligence (AI) integration into the process of teacher training practicum, in Jamaica. The study utilised a mixed-methods approach, involved the participation of 60 [Primary, Early Childhood, and Secondary] teacher trainees and 15 educators, including cooperating teachers and teacher-educators [practicum supervisors], who responded through questionnaires, interviews, and focus group discussions. The study aimed to assess how AI is applied in the practicum exercises during the 2023-2024 academic year and examine the perceptions of AI’s impact on teacher trainees’ skills, experiences, and preparedness for future classrooms. Quantitative findings indicated that 75% of teacher trainees found AI useful in lesson planning and personalised feedback, while 70% appreciated its role in managing the classroom. Qualitative data revealed that AI enhanced the practicum experience, through challenges with access to technology, fosters creativity and provides real-time feedback. Teacher trainees reported feeling more confident in managing diverse student needs due to AI’s support in differentiated learning. A key recommendation is for the Ministry of Education and Youth, along with other policymakers, to ensure equitable access to AI tools across all practicum settings, providing comprehensive training in AI applications and addressing the technology gap. The study’s insinuations are profound, two of which are that through its offering of personalized coaching, AI has the potential to transform teacher training and particularly in the context of teacher shortages, reducing the mentorship burden in Jamaica’s education system. These findings underscore AI’s capacity to enhance both teaching quality and student outcomes, positioning it as a critical tool in the future of education.

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

Disclosure: H.M. Heshmati: None. N. Zameni: None.Background: Robotic surgery is quickly replacing traditional surgery performed by humans. It has significantly influenced the field of endocrine surgery. This review presents an update on the use of robotic surgery and artificial intelligence for adrenal tumors. Methods: A systematic search of literature was conducted using the search terms robotic surgery, artificial intelligence, adrenal tumors, safety, outcome, and cost. Results: Adrenal tumors are found in 10% of people who have imaging of the adrenal gland. They are typically unilateral and benign in the majority of cases. Robotic systems such as da Vinci Surgical System provide surgeons with enhanced dexterity and 3D visualization. Autonomous surgical robots can be categorized into various levels of autonomy including supervised autonomy (e.g., performance of specific tasks with human oversight), collaborative autonomy (e.g., robots working alongside human surgeons, sharing tasks and responsibilities), and fully autonomous systems (e.g., robots performing entire surgical procedures independently with human intervention when necessary). Robotic surgery allows access through tight spaces to remove adrenal tumors with small incision and minimal impact on surrounding tissues. The review of 1,162 patients who underwent adrenalectomy (robotic surgery in 747 patients, laparoscopic adrenalectomy in 415 patients) showed comparable blood loss and complications with the two techniques but patients with robotic surgery had significantly shorter hospital stay. The future of robotic surgery can be revolutionized by the integration of autonomous robots and artificial intelligence (AI), a field rapidly advancing. These innovations promise to further enhance the precision, safety, and effectiveness of surgery involving the adrenal glands. AI-powered systems provide real-time guidance and feedback to surgeons, enhancing decision-making and surgical precision. Autonomous systems can ensure consistent execution of surgical tasks, reducing variability and enhancing patient safety. AI algorithms can analyze extensive surgical data to refine techniques and predict potential complications, improving surgical outcomes. AI can customize surgical plans based on individual patient anatomy and pathology, potentially leading to better outcomes. The current high cost of these new procedures is a potential limitation for their use. Conclusion: Robotic surgery represents significant progress in minimally invasive techniques for the removal of adrenal tumors. It has improved dexterity and precision, and is associated with shorter recovery time compared to laparoscopic adrenalectomy. Collaboration between human surgeons and robotic systems can improve patient care and surgical outcomes. The precision, safety, and effectiveness of the adrenal surgery can be further enhanced by the integration of AI and robotics.Presentation: Saturday, July 12, 2025