ABSTRACT The modules on “System Stability Criteria” and “Frequency Domain Characteristics” within the “Fundamentals of Mechanical Engineering Control” course constitute essential theoretical components. However, their highly abstract mathematical nature and disconnect from physical intuition present considerable teaching challenges. This study examines the potential of Generative Artificial Intelligence (GAI) to facilitate concrete representation of abstract theories in engineering education. Grounded in cognitive load theory and the SAMR model, this research systematically analyzes key pedagogical obstacles—including the separation between mathematical derivations and physical significance, the abstraction of graphical logic, and insufficient engineering case studies—and harnesses GAI's capabilities in text analysis, dynamic visualization, and case generation to develop an innovative pedagogical framework. Using “System Stability Criteria” and “Frequency Domain Characteristics” as primary examples, this paper demonstrates the integration of text generation models (such as ChatGPT), image and dynamic visualization tools (including Midjourney and Stable Diffusion), and code generation models (like GitHub Copilot) to transform abstract theories into intuitive, interactive learning experiences. Through the design and classroom implementation of this GAI‐enhanced pedagogical framework, its feasibility and perceived utility are evaluated. Qualitative feedback from students and instructor observations indicate that the framework aids in reducing cognitive barriers, strengthening connections between theoretical concepts and engineering applications, and fostering more engaging learning experiences. This study offers a proof‐of‐concept, theoretical insights and practical guidance for reforming the teaching of “Fundamentals of Mechanical Engineering Control” while contributing novel perspectives on GAI's role in the digital transformation of engineering education.

ABSTRACT The study of materials science concepts, such as crystallographic structures, poses well‐known pedagogical challenges due to the spatial visualization demands imposed by traditional two‐dimensional representations. To address these challenges, this paper presents CeldApp , a low‐cost mobile system that integrates interactive visualization and augmented reality to support the learning of core materials science topics. The system provides a set of modular 3D learning environments covering crystallization processes, crystal structures, and phase diagram analysis, enabling students to actively explore abstract concepts through direct manipulation and immediate visual feedback. The educational impact of the proposed system was investigated through a controlled pilot study employing a pretest–posttest design with parallel groups in an undergraduate materials science course. Learning gain, task completion time, and user experience were considered as evaluation measures. While between‐group differences in learning gain did not reach conventional levels of statistical significance, the experimental group exhibited a consistent positive trend with a moderate effect size. Notably, participants using the system demonstrated a statistically significant reduction in task completion time, suggesting improved efficiency in knowledge retrieval and problem‐solving. Qualitative feedback further indicated high perceived usability and strong educational value of the augmented reality components. Taken together, these findings suggest that accessible, low‐cost immersive visualization tools hold promise for mitigating spatial visualization challenges in materials science education and motivate further large‐scale investigation.

ABSTRACT Creating with artificial intelligence (AI) is fundamental to AI literacy through effective teaching methods and programmes. The aim is to suggest strategies for AI literacy education using LearningML software, based on machine learning, which allows users to create artificial intelligence models to recognise text and images without the need for programming knowledge. The study method is based on a systematic review of different databases that have integrated LearningML since 2020, their authors, countries, affiliations, keywords, associated resources, objectives, study methods, and conclusions, in order to determine the impact of the LearningML tool for integrating and developing AI literacy in teachers, students, and any user. The results were 48 documents that position LearningML software as a resource that can be integrated into curricula to promote AI literacy from primary education (K‐8) to university, and even for any citizen. The main conclusions position this software in STEM fields, such as medicine, which recommend this software for understanding the fundamentals of AI. This tool helps address the challenge of preparing citizens for the future and making decisions about the use of AI.

ABSTRACT Laboratories are essential to engineering education, yet traditional setups often face challenges such as limited accessibility for students with disabilities and constraints imposed by events like pandemics. To address these issues, this paper introduces an innovative remote laboratory platform to overcome these barriers. The platform integrates specialized control boards and hardware with advanced laboratory management software, offering a versatile and accessible educational tool. Its fault detection and control system ensures reliable operation while safeguarding both equipment and users from potential risks. The platform supports both in‐person and remote access to laboratory resources, providing a cost‐effective and high‐precision alternative to conventional labs. Furthermore, it can be easily and swiftly implemented in existing traditional labs at a reasonable cost. This remote laboratory system enhances educational equity and operational flexibility, with practical applications in teaching, research, and industrial settings.

ABSTRACT Computational thinking (CT) and programming are essential competencies in the Fourth Industrial Revolution, spurring interest in learner‐centered and artificial intelligence (AI)‐based education. Combining the active environment of flipped learning with the personalized support of generative AI is a promising new strategy for programming education; however, research integrating these elements remains limited. This study investigated the effects of programming education that incorporates both flipped learning and generative AI on students' CT and problem‐solving skills, with particular attention to variations by university size and educational setting. Participants were students enrolled in introductory software courses at two universities (K and J). Programming lessons integrating generative AI and flipped learning were developed, and changes in students' CT and problem‐solving skills were evaluated using pre‐ and post‐surveys and analyses of programming assignments. The study also examined the relationship between programming‐related CT components (problem decomposition, abstraction, algorithm design, automation, and simulation) and students' programming difficulties, as well as key factors contributing to differences in learning outcomes between institutes. The results revealed significant improvements in the CT and problem‐solving skills of students. Based on these findings, pedagogical strategies were proposed to optimize the use of generative AI and flipped learning in diverse educational contexts. This study provides valuable insights into the application of active, personalized learning in various settings, including introductory software education and online learning.

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ABSTRACT This paper presents a project‐based learning approach to improve students' ability to apply systematic design methodologies during their transition from general Electrical Engineering (EE) courses to Integrated Circuit (IC) design programs. With the advent of Electronic Design Automation (EDA) tools and Artificial Intelligence (AI) techniques, there is a growing need for students to develop a more systematic understanding of circuit design. A structured training program is designed to enhance the ability of mixed IC design of fourth‐year students, who are expected to use open source tools to design key circuit components which are aligned with MATLAB. With these components, the mixed signal system can be built and optimized top‐down, allowing significantly accelerated development and deeper knowledge learning. A team of students is required to design MATLAB‐aligned circuit components and a benchmark testing framework is developed to ensure the alignment of functionality between the proposed work and MATLAB Simulink. The results indicate that the proposed project‐based learning approach effectively supports fourth‐year students in transitioning to IC design, with strong recommendations for its continued implementation in future academic years. The project establishes a comprehensive and free EDA framework for teaching mixed signal IC design, offering significant advantages over traditional commercial tools. Moreover, the use of hardware‐defined code provides greater flexibility in the design environment, allowing students to utilize the EDA framework across multiple platforms without the need for expensive commercial licenses.

ABSTRACT The rapid expansion of the Internet of Things (IoT) demands instructional models that effectively bridge theory and practice. This study explores the integration of Project‐Based Learning (PBL) with the ADDIE instructional design model in an undergraduate Information Technology Projects course. As a capstone activity, students designed and built a Smart Lamp using an ESP32 microcontroller, sensors, MQTT communication, and a mobile user interface, iterating through ADDIE phases with formative feedback. A mixed‐methods evaluation combined SAM‐based rubrics, user‐experience testing, and self‐ and peer‐assessment ( n = 30 students; n = 20 external users). Results indicated strong technical performance (overall M = 4.2/5 across competencies), high collaboration indicators ( M = 4.4/5), and positive end‐user satisfaction ( M = 4.4/5). A nonparametric exploratory comparison revealed no significant differences between students with and without prior IoT experience, highlighting the inclusiveness of the approach. Overall, findings demonstrate that coupling PBL with ADDIE fosters motivation, knowledge transfer, and effective hardware–software integration while providing a replicable structure for IoT education. Implications for course design include aligning learning outcomes with ADDIE phases, incorporating iterative usability testing, and integrating cloud‐connected prototypes as authentic assessments for future implementations.

ABSTRACT This work describes the procedure for the gamification of Computational Fluid Dynamics (CFD) seminars, with the aim of increasing student motivation and simplifying the learning and acquisition of skills related to the use of CFD tools. The seminar activity is divided into two parts: (i) autonomous work along the semester , in which students receive video tutorials and manuals to prepare them to solve CFD simulation cases, these cover software handling and the necessary data processing; (ii) collaborative work in the computer lab , where students are grouped to address CFD cases within a video‐game‐style environment created in Genially . Each group assumes the role of an avatar solving different quests and challenges related to the simulated cases and unlocking rewards (motivational element and part of the activity mark), progressing until the end of the game. After implementing this gamified CFD seminar approach over two academic years in two subjects of different bachelor's degree programmes ( Fluid Mechanics in Aerospace Engineering in Air Navigation and Fluid Engineering in Industrial Electronic and Automatic Engineering ), the results show improved student performance. Furthermore, students report a very favourable perception of the activity, noting that the game‐like settings of the seminars increase the level of immersion in the activity and enhance teamwork skills.