Search Results for
"Engineering education"

This study explores the integration of the educational philosophy of Science, Technology, engineering, Arts, and Mathematics (STEAM) with deep learning technologies for the development of high school English course resources. By introducing deep learning models, particularly the Transformer architecture, the study enables automatic generation and personalized delivery of instructional content, thereby enhancing the intelligence and relevance of course resources. An innovative Transformer-based framework incorporating STEAM-related semantic features is designed and implemented. The framework is trained on a large-scale, preprocessed English text dataset collected from 300 high school students participating in STEAM-integrated English learning tasks, with data from 150 students forming the core corpus for model training. The model employs an encoder-decoder architecture and is optimized using the Adam optimizer to support the generation of high-quality content tailored to diverse teaching objectives. Through on-site teaching experiments conducted with the 300 high school students, the results demonstrate that, the model’s performance is rigorously evaluated through metrics such as Bilingual Evaluation Understudy score (0.78), innovation index (0.75), and content diversity (0.70). Results reveal that the proposed model outperforms comparative models (Model A and Model B) in language generation accuracy, innovation, and content diversity, highlighting its superior capability in producing high-quality English teaching resources. Although the model demonstrates promising results, its implementation in resource-limited environments presents challenges, highlighting the need for further refinements. Nonetheless, these findings demonstrate the model’s potential to revolutionize English teaching by enhancing resource quality and offering a more efficient, innovative approach to course development. © The Author(s) 2025.

This study addresses the gap between traditional informatics teacher training in Kazakhstan and the practical demands of modern classrooms. It explores the integration of dual education and the informaticedu.kz digital platform as a means to enhance methodological and practical competencies among future teachers. A mixed-methods design was used, involving 24 students from Pavlodar Pedagogical University. Data were collected through structured questionnaires and qualitative interviews. Quantitative responses were analyzed using descriptive statistics, t-tests, and correlation analysis, while qualitative data underwent thematic analysis. The findings showed that the platform significantly supported lesson planning and methodological development, particularly among 4th-year students who rated the tool more positively than 3rd-year students. High correlations were found between understanding lesson structure and effective planning. However, participants reported a lack of interactive content such as case studies and problem-solving tasks. The results suggest that integrating dual education with specialized digital platforms enhances informatics teacher training. Still, to maintain relevance and effectiveness, platforms must evolve to include more interactive and adaptive features tailored to different training stages. © 2025 Institute of Advanced engineering and Science. All rights reserved.

The rapid advancement of artificial intelligence (AI) has significantly transformed various educational domains, including engineering education. Despite AI’s growing prevalence, limited research has explored the determinants influencing engineering students' acceptance of AI. This study investigates the factors shaping AI acceptance among engineering students in Indonesia. Using Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach, data were collected from 158 engineering students across multiple universities. The research model incorporates six constructs: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Social Influence (SI), Facilitating Conditions (FC), Self-Efficacy (SE), and Perceived Risks (PR), each operationalized through seven measurement indicators. The results indicate that PU, PEOU, SI, and SE have significant positive effects on AI acceptance, while PR exerts a significant negative influence. Conversely, FC does not demonstrate a significant impact. These findings offer theoretical and practical implications for fostering AI adoption in engineering education, including strategies for educators, policymakers, and developers of AI-based tools to enhance user acceptance. This study extends the literature on technology acceptance in educational settings, providing actionable insights for improving the integration of AI in higher education. © 2025, The Online Learning Consortium. All rights reserved.

This study addresses an unexplored intersection of vocational education and training (VET), motor skill learning, craftsmanship and the architecture, engineering and construction (AEC) industry as well as expertise sharing. Acquiring proficiency in craftsmanship involves not only theoretical knowledge but also a multitude of physical skills, such as chiselling, which are honed through repetition. Mastering these manual skills and combining them with knowledge of materials, such as wood, is essential for an effective executing of craftmanship. We developed an augmented reality (AR) prototype through the design case study approach to design a support for beginners, especially apprentices, in woodworking education where the connection between craftspeople and material is important for the end results. The prototype was evaluated by 28 apprentices inside and outside a woodwork shop setting. While participants praised the prototype’s 3D visualisations and interactive models, challenges in compatibility with woodwork shop environments were evident. Our findings highlight the importance of on-site evaluations in the craftsmanship context and the significance of direct interaction with real world materials. We provide a set of seven design guidelines derived from our empirical findings to inform the development of AR-based learning applications in craftsmanship. Our discussion opens up the question of the role of interactive 3D models for collaborative learning. The research extends AR’s impact in craftsmanship and AEC industry. Our study underscores the importance of expert knowledge in handling certain materials and the potential of AR to reshape work practices and visualisations, contributing to expertise sharing in this domain. © The Author(s) 2025.

The aim of the study is to substantiate the features of active implementation of competence-based chemistry teaching in secondary education institutions of the Republic of Kazakhstan. The methodological approach of the study is based on the following research methods: analysis, synthesis, induction, modelling, comparison, and generalisation. The content of the concept of "competence approach"is defined; its signs and features are revealed, the necessity of introducing the competence approach in the process of teaching chemistry is theoretically proved and the prospects of using the specified approach to improve the study of chemistry among secondary school students of the Republic of Kazakhstan are determined. In the course of theoretical research, the key competences in the process of chemistry teaching, which should be formed in secondary school students of the Republic of Kazakhstan, have been identified. The study identified the contradictions concerning the development of the competence approach in the study of chemistry and outlined ways to solve them. Perspective directions of improvement of competence-based chemistry teaching are defined, and specific recommendations on measures of effective implementation of such an approach in secondary education institutions of the Republic of Kazakhstan are given. © 2025 Galiya Alzhanova et al., published by Society of Ecological Chemistry and engineering.

This study integrates BERT with multi-source data fusion technology to construct a chain-mediated model linking practical education base construction, teacher guidance effectiveness, collaborative education, and employability improvement. It uses a domain-adapted fine-tuned BERT for deep semantic encoding of unstructured internship texts and designs a "feature-layer attention + decision-layer GBDT"framework to integrate diverse data, building a high-precision employability prediction model. Empirical findings show: (1) Practical education base construction directly enhances employability, with its interaction with teacher guidance effectiveness indirectly influencing employability through the chain-mediation path of collaborative education (accounting for 59.15% of total mediation effect); (2) BERT-parsed textual semantic features contribute most to employability prediction (38.7%), verifying the technology's key role in evaluating implicit abilities. The study provides quantitative evidence for collaborative education mechanisms, offers scalable technical solutions for multi-source data fusion and intelligent analysis, and proposes technology-empowered optimization paths covering institutional guarantees, teacher evaluation reform, enterprise platform development, and students' reflective practice. © 2025 Copyright held by the owner/author(s).

With the deep penetration of artificial intelligence technology in the field of education, human-machine collaborative education models have gradually become a research hotspot. However, the inadequacy of technology application and educational scenario compatibility remains the core bottleneck restricting its development. Based on the theory of cognitive symbiosis, this article constructs a dynamic matching matrix as an intelligent decision-making model. By following the technical path of "dimension design-rule modeling-scenario validation", it achieves precise matching of three major mechanisms: coexistence of educational scene characteristics and knowledge network, dynamic transfer of control, and maintenance of emotional connections. Research shows that this matrix is compatible in liberal arts, science, and interdisciplinary educational scenarios, balancing technological possibilities with educational necessities, and providing a replicable technical paradigm for the deep integration of artificial intelligence and teaching and learning. © 2025 Copyright held by the owner/author(s).

This study presents the design, implementation, and evaluation of a sustainability-centered Course-Based Undergraduate Research Experience (CURE) embedded within a first-year engineering course at North Carolina State University. The goal of the intervention was to introduce authentic, project-based inquiry early in the engineering curriculum while aligning student work with the institutional carbon neutrality goals. Over 100 students participated in semester-long projects addressing campus sustainability challenges in energy efficiency, waste management, and biodiversity. A structured instructional framework guided students through each phase of the engineering design cycle, integrating a literature review, data analysis, iterative prototyping, and knowledge transfer. Instructional tools such as worksheets, templates, and design frameworks scaffolded student progress, while ensuring alignment with defined course competencies. To assess the impact of this approach, pre- and post course surveys were administered to measure changes in students’ self-reported confidence, interdisciplinary collaboration, and perceptions of sustainability in engineering. Statistically significant gains were observed in competencies related to research, design, and project planning. Students also reported increased recognition of sustainability’s relevance to engineering practice and a greater preference for hands-on, collaborative learning environments. By embedding sustainability as a central axis of inquiry and positioning students as contributors to real campus challenges, this course model offers a scalable framework for integrating systems thinking and research-based learning into early engineering education. The findings provide insight into how first-year CUREs can support the simultaneous development of technical competencies and environmentally conscious mindsets in future engineers. The approach may serve as a replicable model for embedding systems thinking and carbon neutrality into introductory STEM curricula. © 2025 American Chemical Society and Division of Chemical education, Inc.

This paper explores innovative teaching approaches for mechatronics engineering courses in the AI era. It proposes an innovative teaching model encompassing the following aspects: First, leveraging AI-assisted personalized learning platforms to provide customized learning plans and resources for students with diverse backgrounds and abilities, thereby enhancing learning efficiency; Second, AI-driven intelligent design tools - such as computer-aided design software and structural optimization tools - are introduced to help students complete design tasks rapidly and accurately, thereby enhancing their design capabilities. Third, virtual simulation technology and online collaboration platforms provide hands-on practice opportunities, overcoming the limitations of insufficient practical training in traditional classrooms and strengthening students' practical skills. Fourth, interdisciplinary project collaboration encourages mechanical engineering students to work together on design tasks, fostering teamwork and innovation abilities. Research indicates that AI technology significantly enhances mechanical engineering students' learning engagement and practical abilities, particularly demonstrating notable progress in understanding and applying mechatronics engineering. This paper offers new perspectives and practical insights for teaching reform in mechatronics engineering courses within mechanical engineering programs, holding significant theoretical and practical value. © 2025 Copyright held by the owner/author(s).

This book embarks on an in-depth exploration of Problem-Based Learning (PBL) in the context of engineering education. Its overarching goal is to provide a comprehensive understanding of the genesis, potential, challenges, contemporary relevance, and methodological approaches of PBL. The impetus behind this undertaking arises from the acknowledged challenges within engineering education, necessitating a fundamental paradigm shift. The study's justification lies in the imperative adoption of innovative teaching methodologies for student performance improvement and fostering competency among engineering graduates. The narrative unfolds across four distinct sections: Background and Theoretical Perspectives; Methodological Aspects of PBL; Implications of PBL in engineering education and Case Studies; and Plausible Examples and Reflections and Future Direction. Part I establishes a robust foundation by defining PBL, tracing its historical roots, and weighing its potential against challenges. A critical examination of PBL's relevance underscores the urgent need for a paradigm shift in engineering education. The part weaves a "golden thread" that permeates subsequent explorations. A theoretical perspective of PBL follows, exploring its underpinnings and theoretical frameworks. This is complemented by a comparative analysis, offering insights into PBL's efficacy compared to traditional learning methods. The second part examines the alignment of PBL with Outcomes-Based education, lays the groundwork for a coherent and practical integration of these educational approaches within the engineering curriculum, and also offers methodological approaches to deliver and practice PBL. Part III explores PBL's application for reinforcing engineering education, emphasising its tangible impact. It probes into PBL as a means to instil entrepreneurial attributes in students and further expands to encompass the decolonisation and internationalisation of engineering education through PBL. The final part provides case studies, offer plausible examples, reflects on the lessons learned and draws insightful conclusions, synthesising the findings into a cohesive whole. Overall, this book seeks to make a substantial contribution to the ongoing discourse on innovative teaching methodologies, particularly within the context of engineering education. Through a meticulous examination of theoretical foundations, comparative analyses, and the provision of practical methodologies, it aims to serve as a valuable resource for educators, researchers, and policymakers who seek to enhance the effectiveness and relevance of engineering education in our ever-evolving world.. © 2026 Dillip K. Das. All rights reserved.