Outstanding:What?How?A Study on the Precise Selection and Cultivation Mechanism of Outstanding Engineering Talents Based on Grounded Theory

Research Paper Recommended citation: Wang, J., Yue, S., & Chen, H. (2025). OutstandingWhatHowA Study on the Precise Selection and Cultivation Mechanism of Outstanding Engineering Talents Based on Grounded Theory. In Kangaslampi, R., Langie, G., Järvinen, H.-M., & Nagy, B. (Eds.), SEFI 53rd Annual Conference. European Society for Engineering Education (SEFI), Tampere, Finland. DOI: 10.5281/zenodo.17631756. This Conference Paper is brought to you for open access by the 53rd Annual Conference of the European Society for Engineering Education (SEFI) at Tampere University in Tampere, Finland. This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License. Outstanding：What？How？A Study on the Precise Selection and Cultivation Mechanism of Outstanding Engineering Talents Based on Grounded Theory Wang Jinliang a, 1 , Yue Sicong a, Chen Hao a, a Zhejiang University, People's Republic of China Conference Key Areas: Improving higher engineering education through researching engineering education / Engineering skills, professional skills, and transversal skills Keywords: Engineering Education, Outstanding Engineering talents, Core Competencies, Selection and Cultivation Mechanisms ABSTRACT This Full Paper is rooted in our in-depth exploration of the mechanisms underlying the precise selection and cultivation of outstanding engineering talents. Given the lack of a well-established theory on their core competencies and development, it employs semi-structured interviews and grounded theory for exploratory research. 13 participants from three groups ( namely engineering students, engineering professors, and engineering college administrators ) from different engineering colleges of top-tier universities around the world were interviewed. Employing the grounded theory research method, we have identified that exceptional engineering talents are distinguished by “knowledge, abilities, qualities, and professional ethics ”. Guided by the "goal-selection & cultivation-support" concept, we have developed a precise selection and cultivation mechanism tailored for outstanding engineering talents. This mechanism consists of three integral components: a multi-dimensional 1 Corresponding Author Wang Jinliang [email protected] target system, a multi-dimensional development system, and a multi-dimensional support system. The multi-dimensional development system encompasses three key elements: a multi-dimensional selection mechanism, which ensures the identification of promising candidates; a multi-dimensional cultivation mechanism, designed to foster the growth and development of these talents; and a multi-dimensional evaluation mechanism, used to assess their progress and achievements throughout the process. Based on the research results, we put forward three reform suggestions for the current training mechanism. In the long run, our research subjects should also be extended to industrial enterprises. Moreover, it is necessary to conduct long-term tracking of engineering talents trained under the new mechanism in order to determine the key elements and detailed mechanisms, thereby perfecting this theory. INTRODUCTION AND BACKGROUND Nowadays, the development of engineering science and technology in the world is becoming more and more rapid, in which the cultivation of engineering science and technology talents, which plays a key role, has become the focus of attention of each country（Natanael & Bracha, 2024）. At present, the engineering field of China is facing many severe problems. There is an urgent need for a large number of outstanding engineering talents to solve complex engineering problems and thus promote industrial development(Yang &Chen，2023). Cultivating outstanding engineering talents independently is the foundation project to win the initiative of international competition, and it is the basic strategic work to provide a larger scale of high-quality labor for the development of new productivity. As the main body of engineering education, the engineering colleges and universities with advantages in engineering, are duty-bound to take up the work of selecting and educating engineering talents with excellence. At the same time, in the new round of scientific and technological change, the emergence of new technologies, especially the rapid development of AI, has put forward a brand-new topic for the selection and education of outstanding engineering talents(Barakat et al.,2024). For example, the Nobel Prize in Physics and the Nobel Prize in Chemistry in 2024 are both awarded to scholars engaged in AI-related research work. The impact of emerging technologies such as AI has already brought about systematic and disruptive changes in traditional disciplines. For engineering, which is the most sensitive to social development and technological progress, this is a challenge but also an opportunity, and it is necessary to respond to the wave of the era of global science and education development, and to empower the precise selection and education of outstanding engineering talents in the new era with new technologies such as AI. Research on the cultivation of outstanding engineering talents has accumulated a large number of results. In terms of the core competencies of outstanding engineering talents, scholars have put forward requirements in terms of knowledge, ability, character(Gao et al.,2020), thinking and values(Lian et al.,2020), which provide directional references for the selection and assessment of engineering excellence talents, but the specific requirements are still relatively broad, which makes it difficult to be implemented in the actual selection and assessment work. In the cultivation of engineering excellence students, scholars have proposed a variety of cultivation modes for engineering excellence students from different perspectives, such as based on discipline crossover(Qi et al., 2023), integrated cultivation of bachelor's degree and master's degree(HUANG et al.,2019), industry-teaching synergy(Fan et al.,2016; Huang et al., 2019), disciplinary competitions(Xia et al.,2019) , and small-group cultivation(Zhang & Guo, 2024), which inspire the ideas of cultivation of engineering excellence students, but from the viewpoint of the whole process of talent cultivation, it is not systematic and complete yet. Therefore, this study focuses on the precise selection and cultivation mechanism of engineering excellence students, focusing on the core issues of “what are the core competencies of outstanding engineering talents” and “how to precisely select and cultivate outstanding engineering talents”. METHODOLOGY AND APPROACH Regarding the core issues, this study adopts semi-structured interviews and the grounded theory research method. Since there is no well-established theory yet for the research on the core competencies and development of outstanding engineering talents, semi-structured interviews and the grounded theory are chosen for exploratory research. The study employs purposive sampling to intentionally select participants from diverse stakeholder groups (undergraduates, postgraduates, professors, administrators) across global engineering institutions. This strategy is justified by the need to capture multi-dimensional perspectives on talent development: students provide insights into competency acquisition at different academic stages, while professors and administrators offer institutional and curricular design perspectives. Though small in size (N=13), the sample ensures theoretical saturation by covering all tiers of engineering education (training to leadership). Data analysis will systematically code responses by role to identify both shared themes and role-specific insights, enhancing the credibility of the grounded theory developed. This approach aligns with exploratory research objectives, where depth of insight from diverse actors outweighs statistical representativeness. We first drafted the interview outline based on the research questions. Then, we interviewed experts in engineering education research and reviewed existing research results together to modify and perfect it. After finalizing the outline, we sent it to the interviewees at least two days in advance. During the interview, we basically followed the interview outline when asking questions, but we also made some minor adjustments based on the interviewee's answers to make the interview more smooth and in-depth. The whole process was recorded, and each interview lasted from half an hour to an hour. Each time, at least two of us were responsible for taking notes to ensure objectivity and accuracy. After the interviews, we conducted inductive coding of the interview data, including joint open coding, axial coding, and selective coding, for in-depth analysis. We used data from the 12th and 13th interviews to test theoretical saturation.Since no new categories emerged, we knew the theory was saturated. During open coding, we labelled interview transcripts line by line to identify initial themes, generating 59 third-level codes for core competencies of outstanding engineering talents and 58 codes for their selection and cultivation. In axial coding, we merged related codes into broader categories, resulting in 20 second-level categories for core competencies and 18 for selection/ cultivation mechanisms. The resulting primary and secondary indicators are shown in tables 1 and 2. Finally, selective coding revealed the core competencies defining these talents and a targeted framework for systematically selecting and cultivating them. Table 1. Core Competencies of Outstanding Engineering Talents Primary Indicator Secondary Indicator Tertiary Indicator Typical Excerpt Knowledge Basic Knowledge Mathematical and Physical Methodology Mathematics and physics serve as the foundational logic for engineering innovation. Hardware and Software Foundation Proficiency in hardware and software across electrical systems, instrumentation, and control is essential. Computational Thinking The ability to abstract complex problems into algorithmic models. Scientific Principle Deduction Deriving engineering constraints from Maxwell's equations. Professional Knowledge Domain Depth Specialized sub-domains must be analyzed with reference to international top-tier standards. Engineering Knowledge System A complete knowledge chain from theoretical formulas to process parameters. Technical Standard Mastery Familiarity with the rules for formulating international standards such as ISO/IEC. Cutting-edge Technology Tracking Updating the database of top-tier conference papers in the field weekly. Interdisciplinary Knowledge Domain Terminology Transformation The ability to explain engineering principles in medical language. Toolchain Integration Integrating multidisciplinary simulation platforms. Knowledge Transfer Ability Applying aerospace material technology to medical devices. Abilities Learning and Iteration Rapid Learning The ability to master a new algorithm framework within two days. Critical Reconstruction The courage to question the rationality of classical theories. Continuous Thirst for Knowledge A 50-year-old professor self-studying quantum computing. Metacognitive Regulation The self-awareness to regularly reflect on loopholes in the knowledge system. Knowledge Graph Construction Establishing a network of domain knowledge associations. Innovation and Entrepreneurshi p Risk-taking Willingness to invest three years in a project with a 10% success rate. Achievement Transformation The ability to transform laboratory results into industrial standards. Business Model Construction The planning ability from technological invention to a commercial closed loop. Intellectual Property Layout Proactively filing global PCT patent portfolios. Engineering Practice Hands-on Ability Debugging equipment personally until 3 am. Scenario Application Validating theoretical models in real oilfield environments. Process Implementation Ability Transforming design drawings into massproducible processes. Fault Diagnosis Ability Identifying equipment faults by analyzing noise spectra. Communication and Coordination Cross-domain Collaboration Coordinating three teams of machinery, electronics, and clinical personnel simultaneously. International Dialogue The ability to write international standard drafts in English. Conflict Resolution Ability Resolving stakeholder conflicts in industryuniversity-research cooperation. Leadership and Planning Project Coordination Ability The systematic integration ability to lead national major special engineering projects. Cost Control Ability Reducing the cost of a prototype by 23 times. Summary and Induction Innovation Methodology Establishing reusable technological breakthrough frameworks. Experience Condensation Ability Forming reusable technical method documentation. Problem Solving Problem Insight The insight to trace a single-point fault back to supply chain defects. Trial and Error Optimization Finding the optimal solution after multiple experimental failures. Emergency Response Ability Solving a nuclear power plant control system vulnerability within 72 hours. Qualities Good Habits Time Management Ability Daily recording and analysis of scientific research logs. Scientific Research Habit Formation Regularly organizing experimental data into standard processes. Exploration Spirit Frontier Sensitivity Laying out hydrogen energy storage technology three years in advance. Reverse Thinking Ability Discovering new research directions from failed cases. Technical Intuition Intuiting material failure points without prior calculation. Strong Internal Drive Stress Resilience Self-funding research after a project is canceled. Delayed Gratification Ability Enduring a decade of obscurity to develop photoresist. Self-motivation Mechanism Setting personal technological breakthrough milestones. Grand System View Global Vision Balancing technical feasibility and social acceptance. Full Lifecycle Perspective Considering the full impact of products from R&D to recycling. Complex System Modeling Developing digital twins for city-scale energy networks. Judgment and Perception Academic Aesthetic Knowing what is good and why. Demand Perception Ability Knowing what society needs and what is meaningful. International Vision Cross-cultural Dialogue and Collaboration Dialectically evaluating foreign theories within local political, economic, and cultural contexts. Global Technology Benchmarking Ability Benchmarking against the R&D standards of international top-tier laboratories. Physical and Mental Health Physical Fitness Maintaining high efficiency for 30 consecutive years of work. Psychological Resilience Maintaining stable decision-making ability under high pressure. Professiona l Ethics Sense of National Responsibility Industry Mission Giving up a tenured position abroad to return and build a laboratory platform. Social Responsibility Designing low-cost water purification systems for remote mountainous areas. Cultural Heritage Awareness Incorporating traditional craft elements into industrial design. Engineering Ethics Value Judgment Comprehensively considering multiple impacts such as economic, social, and environmental factors. Sustainable Development Outlook The environmental responsibility to research and develop degradable electronic components. Intellectual Property Ethics Upholding the legal principles of open-source licenses. Craftsman Spirit Pursuit of Excellence The perseverance to control part precision to 1/100 of a hair's width. Pragmatic Persistence Rejecting high-paying positions to commit to engineering research for decades. Table 2. Precise Selection and Cultivation System for Outstanding Engineering Talents Primary Indicator Secondary Indicator Tertiary Indicator Typical Excerpt Multidimensional Target System Cultivation Focus Research-Oriented Establishing "basic research special zones" with 3-year "silent innovation" periods for undisturbed inquiry. Application-Oriented Targeted training for engineers addressing chip manufacturing process pain points. Comprehensive Mechanical Engineering + Clinical Medicine dual doctoral pathways. Career Trajectories Qualified Engineer Training to become practicing engineers rather than pure scientists. Engineering Technologist Requiring both R&D design capabilities and hands-on technical skills. Engineering Scientist Conducting R&D within engineering domains, equivalent to scientific leadership in engineering fields. Chief Engineering Leading national major projects with systematic integration capabilities. Multidimensional Selection Mechanism Short-Term Screening College Entrance Examination Enhancing the weighting of mathematics and physics in college admissions. University Secondary Selection Secondary major selection processes within universities. Long-Term Tracking Long-Term Talent Development Database Establishing delayed evaluation databases to assess cultivation quality over time. Dynamic Full-Cycle Project Evaluation Identifying talent through tracking performance in complete project lifecycles. Social Need Matching Analysis Assessing potential to solve "necklace" (critical bottleneck) technological challenges. Multidimensional Cultivation Mechanism Project-Based Curriculum Real-Problem-Driven Courses Directly addressing Huawei's 5G baseband chip design challenges in coursework. Full-Chain Engineering Practice Courses Involving students in end-to-end processes from lab prototypes to pilot production. Technical Ethics Integration Courses Making "Artificial Intelligence Ethics and Engineering Practice" a compulsory course. Flexible Teaching Methods Case-Based Learning Using both positive and negative major technical case studies as teaching materials. Interdisciplinary Tool Training Requiring proficiency in modeling tools from at least 3 disciplines. Scenario-Based Flipped Classrooms Student-teacher role reversal to foster active learning. Hybrid Online-Offline Learning Combining online and offline modalities to enhance learning efficiency. Hands-On Practice Engineering Field Projects Participating in overseas projects under the Belt and Road Initiative. Simulated Drills Realistic emergency response drills for nuclear power plant accidents. Research 实战 (Practical Research) Urban Information Model (IM) implementation projects. Interdisciplinary Integration Interdisciplinary Courses Offering courses on medical-engineering academic language translation. Cross-Disciplinary Projects Mechanical Engineering + Clinical Medicine dual doctoral programs. Interdisciplinary Platforms Establishing cross-disciplinary smart medical innovation hubs. New Technology Enablement AI Tool-Integrated Teaching Incorporating AI tool usage and ethics into curricula. Discipline Digitization Integrating intelligent technologies into traditional majors. Embedded Precision Assessment Implementing adaptive evaluation with real-time feedback to guide inquiry. Ideological and Political Education Great Artisan Case Studies Integrating "national treasure" role model education into specialized courses. Compulsory Engineering Philosophy Courses Making "Engineering Philosophy" a required course. Multidimensional Evaluation Mechanism Long-Term Evaluation Philosophy Dynamic Competency Assessment Evaluating comprehensive capabilities through long-term project participation. Long-Term Social Impact Assessment Measuring contributions to overcoming critical technological bottlenecks. Multidimensional Evaluation Indicators Research Innovation Outputs While moving beyond the "Five only" (publications, patents, etc.), these metrics remain relevant for baseline assessment. Knowledge Systematization Evaluating the systematic organization of technical documentation. Comprehensive Literacy (Physical, Aesthetic, Labor) Incorporating labor practice into credit systems. Dedicated Tracks for Specialized Talents Establishing independent evaluation pathways for algorithmic prodigies. Multidimensional Support System Philosophical Foundations High-Risk Tolerance Mechanism Granting failure immunity with special funds for high-risk projects. Equal Opportunity Culture Shifting from research-only evaluation to pluralistic success pathways. Infrastructure Three-Tier Laboratory System Tiered management for basic research, engineering validation, and industrial transformation. Shared Physical Platforms Building school-wide or inter-faculty innovation hubs. Digital Simulation Platforms Extreme climate disaster chain simulation systems. Management Systems Flexible Academic Calendar Allowing course selection flexibility and adjustable study durations. Credit Bank System Converting corporate practice experience into course credits. Authorship Reform Enabling students to be primary contributors in national award applications. Mentor-Student Conflict Arbitration Establishing third-party academic arbitration committees. Research Support Services Assigning full-time research assistants for administrative tasks. Strategic Consulting Providing international patent portfolio planning services. Cultural Environment Open Innovation Workshops 24/7 accessible cross-disciplinary innovation communities. Silent Innovation Protection Periods Granting 3-year non-evaluative periods for deep research. Engineering Spirit Advocacy Establishing special awards to honor engineering contributions. Faculty Development Mentor Engineering Practice Certification Requiring cumulative 6-month corporate internships every 5 years for faculty. Two-Way Mentor-Student Matching Optimizing algorithmic models for mentorstudent pairing. Engineering Academic Rank System Creating separate promotion pathways for engineering faculty. Partnership Ecosystem Industry Associations Participating in standard-setting with the Chinese Institute of Engineers. Professional Societies Collaborating with mechanical engineering societies on technical challenges. Corporate Partnerships Jointly establishing intelligent computing labs with Huawei. Research Institutions Collaborating with CAS on extreme environment materials research. University Alliances Building shared manufacturing platforms with domestic universities and dual-mentorship programs with MIT. RESULTS AND DISCUSSION As illustrated in Figure 1, the core competencies of exceptional engineering talent are grouped into four interconnected dimensions: Knowledge, Abilities, Qualities, and Professional Ethics. These four pillars complement and strengthen each other, forming a unified framework for cultivating engineering excellence. Knowledge serves as the bedrock for outstanding engineers. In today’s fast-evolving world—where physical sciences, social systems, and technology are constantly transforming—students must master three knowledge domains. First, a solid grasp of foundational math and science, the building blocks of engineering innovation. Second, deep expertise in specialized engineering disciplines, aligned with global standards and including technical norms (e.g., ISO/IEC) and cutting-edge trends. Third, the ability to integrate knowledge across fields, like translating engineering concepts into medical terms or blending methodologies from different disciplines (Li et al., 2021). Together, these create a robust knowledge base characterized by strength, depth, and breadth. Abilities are the visible expression of engineering excellence. In an era of exponential technological growth, students need adaptive learning skills—the capacity to quickly absorb new tools, challenge existing theories, and regularly refine their knowledge systems (Van den Broeck et al., 2024). Hands-on engineering skills are equally vital, from debugging equipment to validating models in real-world settings. Innovation and entrepreneurship mean taking calculated risks on highstakes projects, protecting intellectual property, and turning lab discoveries into realworld applications. Good engineers also need leadership and collaboration skills: coordinating multi-disciplinary teams, communicating across cultures, and resolving conflicts in industry-university partnerships. As the Washington Accord (2013) emphasizes, problem-solving lies at the heart of engineering: tackling complex, multi-faceted challenges requires blending knowledge from multiple fields, balancing theory and practice, and leveraging creative innovation (Lin & Hu, 2017). This process mirrors a cycle of "learn-practice-refine," much like digesting information to produce new solutions. Qualities are the invisible drivers that set top engineers apart. Physical and mental resilience provides the stamina for long-term achievement, while consistent habits ensure efficient, sustainable learning. An exploratory mindset fuels curiosity— spotting emerging trends, learning from failures, and trusting technical intuition to identify weaknesses in systems. Systems thinking allows engineers to evaluate projects holistically, balancing technical viability with societal and environmental impact. A sharp judgment helps them distinguish meaningful problems from trivial ones and predict technological trajectories, while a global perspective enables them to adapt international ideas to local contexts and benchmark against the world’s best. Professional Ethics act as the guiding compass for engineering work. Since engineering aims to benefit humanity, top talent must feel a deep sense of mission— prioritizing national needs and addressing societal gaps. This means upholding strict ethical standards: prioritizing sustainability, respecting intellectual property, and embracing the "craftsman spirit"—striving for precision, even at the micron level, and committing to long-term technical progress over short-term gains (Martin et al., 2021; Man & Sunyu, 2018).