The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the “big picture” considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query “artificial intelligence and teaching” over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field.

Testing and visual inspection of printed circuit boards (PCBs) and printed circuit board assemblies (PCBAs) are important procedures in the manufacturing process of electronic modules and devices related to locating and identifying possible defects and failures. Earlier defects detection leads to decreasing expenses, time and used resources to produce high quality electronics. In this paper an exploration and analysis about the current research regarding methods for PCB and PCBA testing, techniques for defects detection and vusial inspection is performed. The impact of machine and deep learning for testing and visual inspection procedures is also investigated. The used methodology comprises bibliometric approach and content analysis of papers, indexed in scientific database Scopus, considering the queries: “PCB and testing” and “PCB and testing”, “printed circuit board assembly and testing” and “PCBA and testing”, “PCB defect detection” and “PCBA defect detection”, “PCB and visual inspection”, and “PCBA and visual inspection”. The findings are presented in the form of a framework, which summarizes the contemporary landscape of methods for PCBs and PCBAs testing and visual inspection.

Publisher: Vilnius University Institute of Data Science and Digital Technologies, Journal: Informatics in Education, Title: eLearning Informatics - From Automation of Educational Activities to Intelligent Solutions Building, Authors: Malinka IVANOVA , eLearning is fast progressing scientific field proposing novel and specific approaches in a range of domains. It is well established practice in universities, schools and organizations for delivering interactive, adaptive and flexible training, taking advantage of contemporary and emerging technologies. Informatics is a continuously evolving science presenting its theoretical and practical advances applicable in various research areas, including in eLearning. The paper presents an exploration focused on the symbiotic connection between Informatics and eLearning that leads to contemporary and innovative solutions, facilitating and automating a wide variety of activities at information processing. The term eLearning Informatics is conceptualized and explained as a scientific field outlining the current research achievements and further directions for development. The applied research methodology is based on outlining the main vision in the domain eLearning Informatics through utilization of bibliometric approach and construction of bibliometric networks as well as on detailed examination of topic-related scientific papers.

The development of Personal Learning Environments (PLEs) is in the scope of research groups and educators aiming to propose suitable mechanisms for the organization of self-controlled and self-directed learning, providing students with tools and services for access to content and human intelligence inside and outside the educational institutions. The students' understanding about the PLE purpose, tools, and applications utilization according to their preferences and learning styles, arrangement of a learning process, social functions empowering is an important phase of the design of personal learning space. In the development and adoption of such environments, it is essential that students' opinion and viewpoints are taken into consideration. This provokes the present exploration of students' preferences, learning styles and needs in the point of view of their preliminary preparation and conceptual understanding of the technical and pedagogical aspects of PLE building. In this article, a model of PLE conceptual understanding is proposed. It is created according to students' perspective during the past year and it is elaborated with new students this year. Several surveys are developed to support students' self-cognition about learning needs, styles, and goals and to construct the vision of PLE organization. The results from preliminary understanding of PLE and opinions after working with the Personal Learning Environment Framework (PLEF), developed at RWTH Aachen University, Germany, are compared and analyzed. A 2-year project was held with bachelor degree students at Technical University–Sofia, College of Energetics and Electronics, Department of Electronics and Computer Science.

The paper presents an analysis and summary of the current research state concerning the application of machine learning and fuzzy logic for solving problems in electronics. The investigated domain is conceptualized with aim the achievements, trending topics and future research directions to be outlined. The applied research methodology includes a bibliographic approach in combination with a detailed examination of 66 selected papers. The findings reveal the gradually increasing interest over the last 10 years in the machine learning and fuzzy logic techniques for modeling, implementing and improving different hardware-based intelligent systems.

E-Assessment is an innovative form for the evaluation of learners' knowledge and skills in online education, as well as in blended-learning environments, where part of the assessment activities is carried out online. As e-assessment involves online communication channel between learners and educators, as well as data transfer and storage, security measures are required to protect the environment against system and network attacks. The issue concerning security is challenging from both educational and technical point of views. Such issues are discussed under the scope of the TeSLA project. Educational challenging problems at e-assessment are analyzed and technical architectural recommendations for securing the e-assessment system according to the General Data Protection Regulation are provided.