Use of Artificial Intelligence in Virtual Practical Labs

This chapter considers the current state, types, and relevance of modern virtual laboratories, virtual practices, and training in higher engineering education. The four types of virtual laboratories are considering. This work also offers examples of virtual scientific and engineering processes simulation laboratories and virtual remote laboratories, virtual practices, and internship. It analyzes the experience of universities and companies in the virtual laboratories, virtual practices, and internship. Particularly interesting for online learning platforms are the virtual laboratories of edX and the National Platform for Open Education. Finally, the chapter provides recommendations on the development of shared knowledge centers for collective use of virtual installations and laboratories, on ways of remote participation in collaborative work with real unique installations, and on participation in the distributed research unique installations and data processing tools. The authors also indicate directions of the development of supportive virtual internship programs for students.

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.

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.

The integration of Artificial Intelligence (AI) in education has transformed traditional language learning approaches, offering innovative methods to develop English-speaking skills. This article explores how AI technologies can enhance the speaking abilities of students in grades 9–11, emphasizing their adaptability, efficiency, and personalized learning experiences. Additionally, this paper discusses ethical considerations, emerging trends in AI-based language learning, and future implications for educators and policymakers. AI not only offers interactive and engaging tools but also significantly contributes to fostering an inclusive learning environment, helping students of various proficiency levels and linguistic backgrounds improve their speaking skills. The widespread adoption of AI in education has the potential to reshape traditional teaching methodologies, making learning more accessible, effective, and tailored to individual needs.

Artificial Intelligence (AI) plays a crucial role in our ever-evolving digital world. It benefits 21st-century citizens by offering a wide range of tools that drive technological advancements across various fields, including medicine, science, and, most importantly, the educational landscape. The integration of AI in education supports practice teachers by enabling innovative teaching practices, personalized learning experiences, and data-driven decision-making. Accordingly, this study explored the integration of artificial intelligence into the lesson demonstrations of practice teachers. It is anchored in George Siemens’ Connectivism Theory, which posits that knowledge is distributed across networks and that learning occurs through connections between people, ideas, and resources. Employing a descriptive qualitative approach, the study identified five major themes and fourteen subthemes. Findings conclude that AI serves as a transformative tool that enhances instructional design, classroom engagement, and teacher confidence when thoughtfully integrated into lesson demonstrations. It is recommended that future research involve a larger and more diverse sample and explore broader dimensions of AI in education to deepen understanding and support effective implementation.

Artificial Intelligence (AI) is rapidly inducing revolutionary transformations in entire aspects of life. Integrating AI into the educational landscape is heralding a new era of pedagogical innovations and redefining learning experiences. As AI technologies continuously evolve, the integration of AI-driven systems in the instructional settings is an inevitable progression and a formidable opportunity. This study is a scholarly attempt to explore the multi-faceted implications of AI integration in the spectrum of education highlighting its potential to revamp the traditional models. Thorough analysis of extant literature reveals that AI-driven platforms facilitate personalized learning experience which can accommodate diverse needs and learning paces of individual students. In addition to this, AI technologies have the potential to revolutionize assessment techniques. As the conventional assessment models fail to capture the true reflections of learning progress, AI assisted models can track, measure, analyze the academic output and provide real-time feedback which is transparent and free form bias. Moreover, AI-powered models have streamlined the administrative tasks as well. By automating routine jobs, optimizing resource allocation, and improving decision making, AI is minimizing human effort and maximizing productivity. However, AI integration poses some challenges and drawbacks. The concerns like data privacy and security, inequality, accessibility, algorithm bias, dehumanization, over-reliance on AI technologies and other ethical considerations are also looming. This study also presents certain future directions and policy perspectives on how to overcome these challenges and build an educational landscape which has the potential to fully utilize the AI-driven systems for academic achievement and learning progress of the students. As AI is revolutionizing entire spheres of life including education, it is imperative to critically engage with both opportunities and challenges posed by AI ensuring the principles of fairness, inclusivity, and integrity.

This paper describes a practical case study on the benefits and feasibility of a virtual laboratory as a part of chemistry laboratory exercises. Three different objectives that must fit together to create an efficient Virtual Reality (VR) learning experience were found: relevant information content, learning design, and technical feasibility. To achieve these multidisciplinary goals, a simple framework for designing VR learning materials was created. A 360-VR version of a chemistry laboratory exercise was designed and created following this framework. Data on its effectiveness was collected on a laboratory course with over 150 first-year chemical engineering students. The students completed the same laboratory exercise both as a virtual laboratory and in a real student laboratory. In the student feedback, students clearly stated that virtual laboratories cannot replace the experience in a real laboratory, and that the virtual laboratory exercises did not directly increase student motivation. Nevertheless, students showed a very positive attitude towards virtual learning materials and suggested including even more activating materials such as quizzes and interactive videos in the learning materials. Only a few students reported any downsides related to the virtual laboratory exercise. Overall, it was shown that our design principles work in practice as the students reported several real benefits when they completed a virtual laboratory exercise before the real-life laboratory exercise. These benefits included learning the correct way that the laboratory exercise proceeds and how to perform certain tasks correctly.

The virtual simulation laboratory is a process of transforming the one-dimensional text on the original textbook, to the two-dimensional picture projected by PPT, into a lifelike three-dimensional image. Through the multimedia platform, the knowledge and models learned by the students are displayed in front of the students, so that the experiencers can feel a near-real experience on this virtual simulation platform, which can greatly deepen our imagination and understanding of things, let the experience learning is fun. According to the existing situation of experimental teaching in local colleges and universities, this study proposes the establishment of an automobile engine simulation laboratory. On the basis of analyzing the technical implementation status of automobile engine virtual laboratory at home and abroad, comparing the system composition of the traditional laboratory system, synthesizing the current needs, researching and exploring relevant solutions, and finally deciding to conduct a simulation laboratory exploration of the engine, explaining the composition of the virtual laboratory, and understand the utilization and management of laboratories. The basic operations of the virtual laboratory are studied based on real objects and actual operations, and the application of the virtual laboratory is discussed with the engine crankshaft as the research object.

Virtual labs have emerged as transformative tools in modern education, offering immersive, interactive, and cost-effective learning experiences. By integrating virtual labs into teaching and learning, educators can bridge the gap between theoretical knowledge and practical application, particularly in science, technology, engineering, and mathematics (STEM) disciplines. These digital platforms simulate real-world experiments, enabling students to conduct complex procedures in a risk-free, controlled environment. Virtual lab integration enhances accessibility by overcoming geographical and resource-related constraints, ensuring equitable learning opportunities for diverse student populations. It also fosters personalized learning, allowing learners to progress at their own pace and revisit concepts as needed. Educators benefit from data analytics provided by virtual lab platforms, enabling them to tailor instructional strategies based on student performance. The integration of virtual labs into curricula promotes critical thinking, problem-solving skills, and technological literacy, aligning with the demands of the 21st-century workforce. Despite challenges such as initial setup costs and the need for digital infrastructure, the long-term benefits—ranging from increased student engagement to the ability to simulate real-world scenarios—underscore their significance in modern pedagogy. This abstract explores the potential of virtual labs to revolutionize education, paving the way for a more inclusive, engaging, and innovative learning environment.

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.

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

The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.

The integration of artificial intelligence (AI) technologies into education systems has been a transformative force across multiple disciplines, with chemistry education proving particularly fertile ground for innovation. As we consider the prospects of AI in chemistry education, it is important to recognize the unique opportunities and challenges that this integration brings. AI technologies, ranging from machine learning algorithms to advanced simulation tools, offer unprecedented opportunities to improve the way chemistry is taught and learned. By introducing interactive content, personalized learning experiences, and improved visualization techniques, these technologies not only enable deeper understanding of complex chemical concepts but also transform traditional teaching methods. This article examines the current state of AI applications in chemistry education and highlights the significant benefits and potential future developments that could further transform this field. As AI continues to advance, its role in educational contexts, particularly in chemistry, promises to be both transformative and far-reaching, paving the way for more effective educational technologies. Keywords: chemistry, artificial intelligence, chemistry education, modern education trends, methods of teaching chemistry.

The integration of Artificial Intelligence (AI) in healthcare is revolutionizing diagnostics, offering important opportunities while emerging challenges. This essay explores the potential and obstacles of AI-based diagnostics. Key problems include technological and ethical issues such as data privacy, compatibility of devices , and regulatory issues. Ethical considerations also arise around patient privacy, accountability for AI errors, legal frameworks, and algorithm transparency. Despite these challenges, AI showcases substantial opportunities for the improvement of diagnostics and treatment techniques. Its capabilities include earlier disease detection, personalized treatment pathways, and healthcare professional support, particularly in radiology, pathology, and genomics. Additionally, AI can help and reduce the cost of healthcare access by streamlining resources, optimizing staff deployment, and facilitating remote monitoring and telemedicine, especially for developing countries. With the analysis of data from wearable devices and enabling remote consultations, AI helps us in the proactive management of chronic conditions. AI-based diagnostics can help overcome long term healthcare challenges, enhancing precision and treatment efficiency. A balanced approach is essential, addressing ethical, privacy, and legal considerations while emphasizing AI’s role as a support for healthcare professionals. Future research should focus on AI algorithm transparency, ethics, and the expansion of telemedicine, especially in underserved areas.

With recent advances in information technologies, a new mode of laboratory known as the “virtual laboratory” has begun to revolutionise engineering education. This development has generated discussion about the fundamental learning outcomes of laboratory training courses and, ultimately, an interest in the consequent changes to the student’s learning experiences.This exploratory case study describes the initial phase of a research agenda that is focused on investigating the effectiveness of virtual laboratories in the Department of Electrical and Electronic Engineering (EEE) in a research-intensive university. The long-term goals of the agenda are to add to the literature of how effective virtual EEE laboratories are (in terms of delivering specific learning outcomes, and also engaging and motivating students and teachers), and to discover whether they can ultimately become a substitute for traditional laboratory training by providing an equivalent and comparable learning experience for students.