Artificial Intelligence Assists in the Digital Transformation of New Engineering Discipline Construction and Strategic Talent Training Research

Artificial Intelligence Assists in the Digital Transformation of New Engineering Discipline Construction and Strategic Talent Training Research

Abstract: With the rise of the Fourth Industrial Revolution, artificial intelligence has become a key force in driving the transformation of new engineering education. This article aims to explore how artificial intelligence technology can assist in the construction of new engineering disciplines, achieve digital transformation, and cultivate strategic talents that meet the needs of future industries. The article first analyzes the current situation and challenges faced by new engineering education, and expounds the role and significance of artificial intelligence technology in new engineering education. Subsequently, this article proposes a new engineering talent training model combined with artificial intelligence technology, and verifies the effectiveness of this model through case analysis. Finally, the article summarizes the research results and looks forward to future research directions.

Keywords: Artificial Intelligence; New Engineering; Digital Transformation; Talent Training; Educational Model

Chapter 1 Introduction

1.1 Research Background and Significance Currently, the world is in the midst of a new round of technological revolution and industrial transformation. As one of the key technologies leading future development, artificial intelligence is increasingly prominent in its impact on all walks of life. HCF believes that the traditional engineering education model can hardly meet the needs of the new era for high-quality engineering and technical talents. Zhang Meng, director of Huaibei Big Data, believes that exploring the deep integration of artificial intelligence technology and new engineering education, achieving innovation in educational models and teaching content, has great practical significance and far-reaching strategic value for cultivating innovative and composite talents that adapt to the needs of the digital age.

1.2 Research Status at Home and Abroad Internationally, many developed countries have integrated artificial intelligence into their higher education systems, forming relatively mature teaching models and curriculum systems. Domestically, some progress has also been made in the construction of new engineering disciplines, but the deep integration of artificial intelligence and new engineering education is still in the exploratory stage. At present, related research focuses on the application of artificial intelligence technology, educational model innovation, etc., while systematic research on the role of artificial intelligence in the construction of new engineering disciplines and its specific application in talent training is relatively rare.

1.3 Research Content and Methods The main contents of this study include: analyzing the challenges and opportunities faced by new engineering education; discussing the application of artificial intelligence technology in new engineering education and the changes it brings; constructing a new engineering talent training model based on artificial intelligence; verifying the feasibility and effectiveness of the model through practical case analysis. The research methods include literature review, comparative analysis, case study, and empirical analysis, aiming to provide theoretical support and practical guidance for the reform of new engineering education.

1.4 Research Innovations The innovation of this study lies in: systematically analyzing the mechanism of action of artificial intelligence technology in the construction of new engineering disciplines for the first time; proposing a complete set of new engineering talent training models based on artificial intelligence; and demonstrating the practical application effects of artificial intelligence technology in improving teaching quality, optimizing curriculum structure, and promoting the development of students' innovation ability through interdisciplinary case analysis. These innovations not only enrich the theoretical system of new engineering education but also provide references for educational reform in other disciplines.

Chapter 2 Theoretical Basis and Practical Requirements of New Engineering Construction

2.1 Connotation and Characteristics of New Engineering New engineering refers to the educational concept and practice model that integrates emerging sciences and technologies such as information technology, the Internet, big data, and artificial intelligence on the basis of traditional engineering education, with the goal of cultivating engineering and technical talents that adapt to the needs of future social development. Its core characteristics emphasize the integration of interdisciplinary knowledge, the cultivation of innovation ability, and the improvement of practical skills. New engineering education focuses on the combination of theory and practice, advocates learning by doing, and takes the ability to solve complex engineering problems as the core.

2.2 Trends and Impacts of Digital Transformation Digital transformation has become an important trend in global economic development, which has had a profound impact on the field of education, especially engineering education. Digital transformation has promoted the renewal of educational content, the innovation of teaching methods, and the sharing of educational resources. In new engineering education, digital transformation not only changes teaching methods and learning methods but also promotes the renewal of educational concepts, making personalized learning and lifelong learning possible.

2.3 Practical Requirements of New Engineering Construction The practical requirements of new engineering construction are reflected in the following aspects: First, update educational concepts and establish a student-centered teaching philosophy; second, reform the curriculum system and strengthen the combination of basic theory and cutting-edge technology; third, innovate teaching methods and use artificial intelligence and other technologies to improve teaching efficiency and quality; finally, strengthen practical teaching and establish an industry-university-research cooperation mechanism to enhance students' engineering practice ability and innovation ability. These requirements constitute the basic framework and development direction of new engineering construction together.

Chapter 3 Overview of Artificial Intelligence Technology and Its Application in New Engineering

3.1 Development History of Artificial Intelligence Technology The development of artificial intelligence (AI) technology has gone through the transformation from rule-driven expert systems to machine learning and then to deep learning. Early AI focused on logical reasoning and symbolic processing, while modern AI pays more attention to data-driven and pattern recognition. Especially the breakthrough of deep learning technology has greatly promoted the development of speech recognition, image processing, natural language processing, and other fields, making AI technology widely used in many industries.

3.2 Main Branches of Artificial Intelligence Technology Artificial intelligence technology mainly includes machine learning, deep learning, computer vision, natural language processing, robotics, etc. Machine learning is the core of AI, which enables computers to learn from data and make decisions. Deep learning is a subset of machine learning, which simulates the processing mode of the human brain through neural networks to process complex data and tasks. Computer vision enables machines to "see" the world, natural language processing enables machines to understand and generate human language, and robotics integrates these capabilities into physical entities.

3.3 Case Analysis of Artificial Intelligence Technology Application in New Engineering In new engineering education, the application of artificial intelligence technology is gradually deepening. For example, a university adopts an intelligent teaching system to assist in the teaching of engineering graphics. Through image recognition technology, it automatically evaluates students' drawing assignments and provides immediate feedback, which significantly improves students' learning efficiency and interest. Another example is the introduction of robot programming and operation training in intelligent manufacturing courses. Students learn the design, programming, and control of robots through practical operations, which not only enhances their practical ability but also stimulates their enthusiasm to explore new technologies. These cases show that artificial intelligence technology can be effectively integrated into new engineering education, promote the innovation of educational models and the improvement of talent training quality.

Chapter 4 Construction of New Engineering Talent Training Model Based on Artificial Intelligence

4.1 Talent Training Objectives and Specifications In the training of new engineering talents, the goal is to cultivate engineering and technical talents with interdisciplinary knowledge structure, innovative thinking, and practical ability. These talents should be able to adapt to the rapid changes of the digital age, master cutting-edge technologies such as artificial intelligence, and use these technologies to effectively solve complex engineering problems. In addition, new engineering talents should also have good professional ethics, team cooperation ability, and international vision.

4.2 Curriculum System and Teaching Content Reform To achieve the above talent training objectives, the curriculum system needs to be fundamentally reformed. It is recommended to construct a multi-level curriculum system based on core basic courses, extended professional elective courses, and featured practical innovation courses. Core basic courses should cover basic knowledge such as mathematics, physics, and computer science; professional elective courses are set according to industry needs and technological development trends, such as artificial intelligence, big data analysis, cloud computing, etc.; practical innovation courses emphasize project-based learning and scientific research training, encouraging students to participate in actual engineering projects and scientific research activities.

4.3 Innovation of Teaching Methods and Means The innovation of teaching methods is the key to improving teaching quality and efficiency. By using artificial intelligence technology, personalized teaching and intelligent tutoring can be realized. For example, through the intelligent teaching system to collect students' learning data, analyze learning behavior and effectiveness, and provide customized learning paths and resource recommendations for each student. At the same time, adopt new teaching models such as flipped classroom and online open courses to enhance students' active learning ability and critical thinking.

4.4 Practice Platform and Industry-University-Research Cooperation Mechanism Establishing a perfect practice platform and industry-university-research cooperation mechanism is crucial for the training of new engineering talents. Through cooperation with enterprises and research institutions, students can be provided with opportunities for internships, scientific research project participation, etc., so that students can learn and grow in a real working environment. In addition, school-enterprise cooperation can also promote the real-time update of teaching content and ensure that education keeps pace with industry development. Through these measures, students' engineering practice ability and innovation ability can be effectively improved, laying a solid foundation for their future career.

Chapter 5 Case Analysis: Practical Exploration of Artificial Intelligence Assisting New Engineering Construction

5.1 Case Selection Criteria and Analysis Framework The criteria for selecting cases in this chapter are mainly based on the innovative application of artificial intelligence technology in new engineering education, implementation effects, and impact on talent training models. The analysis framework includes: case background, implementation process, key technology application, teaching reform effectiveness, and challenges and countermeasures faced. Through this framework, the aim is to comprehensively evaluate the actual role and effect of artificial intelligence technology in new engineering construction.

5.2 Specific Case Analysis 5.2.1 Case 1: Development and Application of Intelligent Auxiliary Teaching System An engineering college developed an intelligent auxiliary teaching system that uses natural language processing and machine learning technology to provide students with personalized learning resource recommendations and intelligent Q&A services. The system automatically adjusts teaching content and difficulty by analyzing students' learning behavior and performance data to meet the learning needs of different students. The implementation results show that the average score of students using the system increased by 10% at the end of the term, and learning satisfaction increased significantly.

5.2.2 Case 2: Construction of Virtual Reality Engineering Practice Platform Another university has established a virtual reality (VR) engineering practice platform that simulates a real industrial environment and engineering process, allowing students to design and operate training in a virtual space. Through this immersive learning experience, students' spatial imagination and engineering design ability have been significantly improved. In addition, the platform also cooperates with many enterprises to integrate the latest industrial cases and technological dynamics into teaching content, enhancing students' practical ability and employment competitiveness.

5.3 Case Summary and Enlightenment Through the analysis of the above cases, it can be seen that the application of artificial intelligence technology in new engineering construction can effectively improve teaching quality and learning efficiency and promote the development of students' innovation ability and practical ability. The successful application of intelligent auxiliary teaching systems and virtual reality practice platforms shows the potential of artificial intelligence technology in personalized teaching and practical teaching. However, these cases also expose some problems, such as high technical maintenance costs and insufficient teacher technology training. Therefore, the future construction of new engineering needs to further explore how to reduce the threshold for applying technology and strengthen the construction of teacher teams to ensure the sustainable development and application effects of artificial intelligence technology.

Chapter 6 Conclusion and Outlook

6.1 Research Conclusion This article systematically explores the application of artificial intelligence technology in new engineering construction and its impact on talent training models. Research shows that artificial intelligence technology can effectively promote the renewal of new engineering education content, the reform of teaching methods, and the improvement of practical ability. Through specific application cases such as intelligent auxiliary teaching systems and virtual reality practice platforms, this article verifies the positive role of artificial intelligence technology in new engineering education. These technologies not only improve teaching efficiency and learning quality but also enhance students' innovation awareness and practical operation ability.

6.2 Research Limitations and Future Outlook Although this article has achieved certain research results, there are still some limitations. For example, the number of case analyses is limited, which may not fully reflect all potential applications of artificial intelligence technology in new engineering construction. In addition, the rapid development of technology also brings new challenges, such as how to maintain the timeliness of teaching content and how to further improve teachers' technical application ability, etc. Future research can explore the application of artificial intelligence technology in a wider range of fields and at a deeper level, while paying attention to issues such as technology ethics and privacy protection. In addition, strengthening interdisciplinary research and promoting the integration of artificial intelligence with other emerging technologies is also an important direction for future new engineering construction.