Computer Applications in Engineering Education最新文献

Traditional methods for teaching Internet-of-things (IoT) in engineering education often lack interactivity and hands-on engagement, limiting skill development. This study explores Augmented Reality (AR) based learning environment as a solution, enabling students to visualize real-time data flows, interact with virtual components, and configure IoT systems in a risk-free setting. Using tools like Unity 3D, Blender, Vuforia SDK, and Arduino IDE, an AR-based framework is developed and tested against traditional methods. The results show increased student engagement, knowledge retention, and skill acquisition, with a System Usability Score of 82.00%. The paper presents the framework's design, usability assessment, and comparative evaluation, highlighting AR's potential to enhance IoT education. It is observed that the proposed AR-based framework improves the skill retention by 36% over the traditional method. Further, the performance comparison of proposed method with traditional method is evaluated in terms of students' engagement, learning speed, and user satisfaction. In the end, the limitations of proposed study are addressed, and the future directions are presented.

The integration of artificial intelligence (AI) in higher education represents a transformative shift in the way teaching and learning are approached, offering unprecedented opportunities to enhance educational outcomes. One significant issue is the potential for bias in AI algorithms, which can perpetuate existing inequalities if not carefully managed. The objective of this study is to explore and evaluate the integration of AI in higher education to enhance teaching and learning processes. The study aims to identify the most effective AI tools and strategies for improving educational outcomes, assess their impact on student engagement and achievement, and provide actionable recommendations for educators and institutions. To effectively assess the integration of AI in higher education, a multifaceted data collection approach is essential. To ensure the successful integration of AI tools in higher education, a structured implementation plan is crucial. Enhancing teaching and learning involves a comprehensive approach that includes meticulous data collection, rigorous data analysis, strategic implementation and continuous improvement. The implementation phase requires thoughtful planning and execution, with a focus on refining AI systems based on feedback and performance metrics to ensure they effectively support educational goals. The findings show that AI integration in education has improved average grades to 88%, increased retention rates to 85%, and achieved 92% in content customisation and implementation using Python software. The future scope for integrating AI in higher education includes developing advanced AI tools that offer personalized and adaptive learning experiences, enhancing predictive analytics for student performance and retention, and fostering innovative pedagogical approaches through AI-driven insights.

This study investigates the potential of Multimodal Large Language Models to evaluate the quality of Unified Modelling Language (UML) class diagrams, with a focus on their ability to assess class structures and attribute information in alignment with object-oriented design principles. Thirty-four engineering students completed a design task involving the application of five object-oriented design principles known collectively as the S.O.L.I.D. principles (Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion). Their solutions were independently assessed by three expert instructors and four Multimodal Large Language Models: ChatGPTChatGPT-4, Gemini, Amazon AI, and Claude 3.5 Sonnet. Quantitative analysis compared AI-generated scores to instructor consensus ratings using inter-rater reliability metrics, while a grounded theory approach was used to qualitatively identify and classify AI evaluation errors. Results indicate that while MLLMs demonstrate promising partial scoring alignment with experts, they consistently exhibit significant limitations in semantic interpretation and evaluative reasoning, often leading to inconsistencies. These findings highlight that despite their potential, MLLMs are not yet reliable replacements for human expertise and underscore the critical need for improved model alignment with domain-specific assessment practices. They also suggest future directions for carefully integrated hybrid instructor-AI evaluation workflows in educational settings.

Higher education in cybersecurity faces significant challenges in developing practical and innovative offensive-defensive teaching cases. We present an automated framework for generating cybersecurity teaching cases using Large Language Models (LLMs), designed specifically for university-level cybersecurity education. The framework leverages the deep learning capabilities of LLMs and Artificial Intelligence Generated Content (AIGC) technology to enable intelligent construction and assessment of teaching cases. Our system allows instructors to automatically generate multidimensional teaching cases encompassing both known and potentially unknown security threats, based on parameters including network architecture, service configuration, security requirements, and network topology. Through prompt engineering techniques, the system enables fine-tuning of generated cases to accommodate diverse educational objectives and student proficiency levels. The framework incorporates an assessment module employing semantic analysis to provide automated multidimensional evaluation of student solutions, establishing a comprehensive pedagogical cycle. Empirical studies demonstrate that this framework significantly enhances the efficiency and quality of practical cybersecurity education, provides a replicable paradigm for vertical AI applications in higher education, and offers a novel approach to addressing resource constraints in university-level cybersecurity talent development.

With the rapid advancement of artificial intelligence, generative AI (AIGC) has emerged as a transformative tool in education, particularly in engineering disciplines where it demonstrates significant pedagogical potential. The CDIO (Conceive–Design–Implement–Operate) teaching model, rooted in experiential and project-based learning, emphasizes the development of students' integrated engineering competencies. However, engineering education remains challenging for many Chinese university students, despite the availability of online and collaborative learning resources, thereby underscoring the need for enhanced instructional strategies supported by advanced technologies. However, empirical research on the application of generative artificial intelligence in engineering education, particularly regarding its effects on individual psychological constructs, remains limited. This study, conducted over one semester in a data mining course, examines the impact of the AIGC-supported CDIO teaching model on students' computational thinking, learning motivation, engagement, and cognitive load. The participants included 76 s-year undergraduates from a teacher training university in China. The experimental group (n = 27) adopted the AIGC-CDIO teaching model, while Control Group 1 (n = 24) followed the traditional CDIO model, and Control Group 2 (n = 25) engaged solely in collaborative learning. Results from ANOVA analysis revealed that the experimental group demonstrated significant improvements in intrinsic motivation, behavioral and emotional engagement, and computational thinking abilities (including algorithmic thinking, critical thinking, and problem-solving skills), outperforming both control groups. Moreover, the experimental group exhibited significantly lower cognitive load. These major findings highlight the pedagogical effectiveness of the AIGC-CDIO approach in enhancing student engagement and reducing mental effort. The findings provide robust empirical support for the integration of AIGC into CDIO-based engineering education. This study contributes to the emerging literature on AI-assisted pedagogy by offering evidence-based insights into the interplay between technological mediation, psychological factors, and cognitive skill development. Implications for future research include deeper investigations into the mechanisms linking AIGC use to learning outcomes, longitudinal tracking of computational thinking development, and the refinement of adaptive instructional models across diverse learner profiles.

This paper tackles the common challenges in traditional manufacturing education, such as high equipment costs, significant operational risks, and limited hands-on opportunities. It proposes the design and development of a virtual simulation cognition system for a cylinder head production line, built on Unity3D. The system fully leverages virtual simulation technology to accurately replicate the complete cylinder head manufacturing process, encompassing critical stages like casting, machining, and assembly. In its design, the system deeply integrates situated cognition learning theory and educational game theory. By creating realistic operational scenarios, incorporating motivational mechanisms, and providing immediate feedback, it establishes a highly immersive and interactive learning environment. This empowers students to engage in self-directed learning and practical operations within a safe, controlled virtual space, thereby fostering the internalization of knowledge and the mastery of skills. The paper meticulously details the system's development process, core functional design, instructional content arrangement, and an analysis of teaching feedback. Our aim is to use technological means to optimize traditional manufacturing education, enhance teaching efficiency, and improve students' practical abilities, ultimately offering an innovative digital solution for nurturing talent in the manufacturing industry. This study begins by analyzing the application background of virtual simulation technology in intelligent manufacturing and educational training. It clarifies both the academic significance and practical necessity of designing and implementing a virtual simulation-based teaching system. The system's requirements analysis thoroughly considers theories like educational game theory and situated cognition learning. Based on actual teaching needs, it delineates functional modules including equipment cognition, layout construction, and task assessment. Compared to existing similar systems, this study optimizes the realism of the simulation, the depth of interaction, and the integration of learning theories, striving to provide a more effective learning experience. Throughout the development process, the system underwent multiple rounds of testing and verification, ensuring its functional completeness and performance stability. Ultimately, the virtual simulation system successfully achieved dynamic simulation of an automotive engine cylinder head production line, providing students with a realistic and highly interactive educational experience that helps them better understand and master complex manufacturing operations.

Intelligent manufacturing training rooms are pivotal for translating theoretical knowledge into practical applications and enhancing operational skills. To address the limitations of traditional teaching models—specifically their inability to facilitate large-scale, complex, or high-risk professional experiments, which result in insufficient comprehensive practical training for students—this study introduces an augmented reality (AR)-based intelligent manufacturing comprehensive teaching and training system (AR-IMCTTS). The system establishes an integrated framework for teaching, hands-on practice, and assessment, enabling students to master the knowledge and production processes of intelligent manufacturing equipment comprehensively. Three aspects of experimental verification were conducted. First, questionnaire results indicate that students widely acknowledge the system's effectiveness in deepening their understanding of IM equipment operations and improving practical skills. Second, quantitative data reveal that students using AR-IMCTTS achieved an average score increase of 10.82% compared to traditional teaching methods. Third, results demonstrate that the hybrid approach of traditional education and AR-IMCTTS significantly enhances participants' learning motivation and practical knowledge retention. Through interactive engagement with virtual objects in AR environments, students develop a profound grasp of experimental equipment. Simultaneously, the experimental group's increased practice opportunities boost learning confidence and reduce cognitive load.

Improving student engagement in engineering education is crucial for better learning outcomes. Engineering education integrates the latest technologies with gamification that fosters student engagement in innovative learning. Gamification involves incorporating game design elements, making learning more interactive and motivating. This paper explores gamification to transform engineering education, improving students' participation, understanding, and overall learning outcomes. Combining Edge computing with artificial intelligence will enhance the gamification of education by addressing two primary challenges: real-time interaction and personalized service. An Edge-AI-driven gamification framework is proposed for improving student engagement through interactive tools like educational games, quizzes, and competitive learning environments. An algorithm related to the Edge-AI Gamification Optimizer is proposed to improve the overall performance. Results show that the proposed framework improves student engagement by nearly 51%, performance by 30.7% and reduces cognitive load by 57%. A comparative study with traditional learning approaches shows that AI-based gamification significantly enhances knowledge retention, boosts collaborative learning, increases student motivation, and, through statistical analysis, demonstrates a validated increase in student engagement over conventional methods. Future studies will investigate the extendability of this framework to different fields of engineering and learning environments. The proposed framework has the potential to transform educational practices by providing personalized, interactive, and motivating learning experiences, leading to better educational outcomes. Future research could explore broader applications and long-term impacts of Edge-AI-driven gamification.