Subjects with limited application in the software industry like AI have recently received tremendous boon due to the development and raise of publicity of LLMs. LLM-powered software has a wide array of practical applications that must be taught to Software Engineering students, so that they can be relevant in the field. The speed of technological change is extremely fast, and university curriculums must include those changes. Renewing and creating new methodologies and workshops is a difficult task to complete successfully in such a dynamic environment full of cutting-edge technologies. This paper aims to showcase our approach to using LLM-powered software for AI generated images, like Stable diffusion and code generation tools like ChatGPT in workshops for two relevant subjects – Analysis of Software Requirements and Specifications, as well as Artificial Intelligence. A comparison between the different available LLMs that generate images is made, and the choice between them is explained. Student feedback is shown and a general positive and motivational impact is noted during and after the workshop. A brief introduction that covers the subjects where AI is applied is made. The proposed solutions for several uses of AI in the field of higher education, more specifically software engineering, are presented. Several workshops have been made and included in the curriculum. The results of their application have been noted and an analysis is made. More propositions on further development based on the gained experience, feedback and retrieved data are made. Conclusions are made on the application of AI in higher education and different ways to utilize such tools are presented.

In the paper virtual drill tools measuring algorithm applied on 3D objects has been described and its programming model, developed for training purposes, has been presented. The work with the model will enable students to measure virtual 3D drill tools and it will be also used to check and assess their knowledge.

This report will briefly introduce the flipped classroom and immersive learning approaches in mechanical engineering. A description of some of the problems with the current mechanical engineering education will be provided. Advantages and disadvantages of flipped classroom and immersive learning will be discussed. Several small scale realized programming application modules that allow adopting the methodologies will be included. The measurement of a broach practical exercise will be studied in detail. A database for a web application combining the different modules in a hub will be shown and its further development will be described. Conclusions about the feasibility of the endeavor and its impact will be made.

The Archimedes Optimization Algorithm (AOA) is a recent physics-based metaheuristic inspired by Archimedes’ principle. Since its introduction by Hashim et al. in 2021, it has gained significant attention and has been applied to various real-world optimization problems. Its popularity stems from its simple structure, adaptability, ease of implementation, and satisfactory convergence. This paper presents a comprehensive review of the AOA algorithm, including its modified, multi-objective, and hybrid variants, and examines its applications in several domains such as classification, feature selection, parameter tuning, scheduling, photovoltaic systems, wireless networks, and clustering. The performance of the AOA algorithm is evaluated against some well-known metaheuristic algorithms, including Genetic Algorithm (GA), Differential Evolution (DE), Tabu Search (TS), Firefly Algorithm (FA), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Marine Predators Algorithm (MPA), and results demonstrates its superiority in most cases (72,22%) with stable dispersion in box-plot analyses. Finally, future research directions for improving the effectiveness and applicability of the AOA algorithm are outlined.