Constructing an “Al + Profesonal Sklls” Talent Training System for Intelligent Green Coal Mining: Paths on Mechanisms Based on Industry-University-Research Collaboration

The world of coal mining is undergoing a quiet but profound transformation. For decades, the industry relied on human labor and heavy machinery to extract resources from deep underground. Today, a new era is dawning, driven by the twin forces of intelligence and sustainability. Mines are becoming "smart," utilizing artificial intelligence, robots, and vast networks of sensors to operate with greater precision and less environmental harm. This shift creates a complex challenge for the people who work in the sector. The traditional miner, trained solely in geology or heavy equipment operation, is no longer enough. The industry now needs a new kind of professional: one who understands the deep earth but also speaks the language of data, algorithms, and green technology. This is the gap between the high-tech tools being built and the human skills available to wield them.

To bridge this divide, researchers from Xi'an University of Science and Technology set out to understand how universities and coal companies can work together to train these future experts. They focused on a concept known as "Industry-University-Research collaboration," a method where schools, businesses, and scientific institutes join forces to design education that matches real-world needs. The team wanted to know if this cooperation was actually working, where it was failing, and how the rapid rise of artificial intelligence was changing the rules of the game. They did not just ask for opinions; they gathered hard data from across China to see what was happening on the ground in classrooms, laboratories, and active mines.

The researchers began by listening to the people who live this reality every day. They sent surveys to students, university teachers, researchers, and employees at coal mines across five major regions of China: the east, south, middle, north, and west. In total, they collected 426 valid responses, creating a picture that included the students eager to learn, the professors trying to teach, and the industry workers who need the skills. The results revealed a clear disconnect. While everyone agreed that practical training was essential, the reality fell short. In the curriculum, the balance between theory and practice was off; many students felt their courses were too abstract, while industry workers felt the graduates lacked the hands-on ability to handle complex, underground scenarios. Only a small fraction of respondents felt that existing courses fully met the needs of the modern mine.

The survey also highlighted a shortage of "dual-qualified" teachers—educators who possess both academic expertise and real industry experience. While universities claimed to offer training opportunities for their staff, the actual collaboration was often rare. Many teachers had not spent enough time in a mine to understand the latest technologies, leaving them unable to teach the very skills the industry demanded. Furthermore, the physical spaces where students practiced were often outdated. Satisfaction with internship bases was low, with many students and teachers noting that the equipment did not match the advanced machinery used in actual mines. The gap between the classroom and the coal face was wide, and the tools to cross it were insufficient.

To move beyond simple opinions and find the root causes, the researchers built a statistical model to test how different factors influenced the quality of talent training. They looked at the strength of collaboration between schools and companies, the level of resources dedicated to joint projects, and the degree to which artificial intelligence was integrated into the curriculum. The analysis confirmed that deep cooperation between industry and universities was a decisive factor in producing high-quality graduates. When schools and companies worked closely together, sharing resources and designing courses based on actual work scenarios, the resulting students were better prepared, more employable, and more capable of solving real problems. The data showed that this collaboration was not just a helpful addition but a fundamental driver of success.

However, the study also found that this success was not uniform across the country. The effectiveness of these partnerships depended heavily on where they were located. In the southern region, where the economy was active and market forces were strong, the collaboration worked exceptionally well. Universities and mines there had built tight networks, sharing labs and research projects, which led to the highest quality of training. In the northern region, strong government support and a dense cluster of energy industries also drove positive results. In contrast, the western region faced significant hurdles. There, weak infrastructure and a mismatch between university capabilities and local industrial needs meant that collaboration sometimes struggled to produce results. The study suggested that a single approach could not work everywhere; strategies had to be tailored to the specific economic and industrial conditions of each region.

The researchers also examined how different types of institutions fared. State-owned enterprises and top-tier universities, which often had more resources and established research labs, saw much stronger results from their collaborations than private companies or smaller institutions. These larger entities could afford to build dedicated training mines and fund long-term research, creating a rich environment for students to learn. Private companies, often driven by immediate profit margins and limited budgets, found it harder to invest deeply in these partnerships, leading to a gap in the quality of training they could offer. This finding underscored that the success of the system relied not just on good intentions, but on the availability of resources and the stability of the institutions involved.

To fix these problems, the authors proposed a specific set of solutions grounded in their findings. First, they called for a complete overhaul of the curriculum, moving away from static textbooks toward a dynamic system that updates every two years. This new system would be co-created by teachers and industry experts, ensuring that students learned the latest technologies, such as AI and robotics, alongside traditional mining skills. Second, they advocated for a "dual-teacher" model where university professors and industry engineers swapped roles. Professors would spend months working in mines to learn the latest practices, while engineers would teach in classrooms to bring real-world context to the theory. This would ensure that the people teaching the next generation were themselves up to date.

Third, the study emphasized the need for better physical training grounds. Instead of relying on outdated equipment, universities and companies should build shared practice bases equipped with the latest machinery and virtual reality simulations. These spaces would allow students to practice in safe, controlled environments that mirrored the complexity of a real mine. Finally, the researchers suggested a new mechanism for sharing scientific discoveries. They proposed that universities and companies should form a coordinated network to turn research into practical tools, ensuring that new ideas developed in a lab were quickly tested and used in the field. This would require clear rules on ownership and financial incentives to keep everyone motivated.

The study concludes that the path forward for intelligent and green coal mining lies in breaking down the walls between education and industry. It is not enough to build smarter machines; the people who operate them must be trained in a system that reflects the speed and complexity of the modern world. By aligning what is taught in universities with what is needed in the mines, and by ensuring that teachers and students have access to the latest tools, the industry can cultivate the composite talents it desperately needs. The research offers a clear roadmap: a system where theory and practice are woven together, where resources are shared, and where the training of the future workforce is a joint responsibility of the school and the mine. This approach, the authors argue, provides a model not just for China, but for resource-based industries globally as they navigate the transition to a greener, more intelligent future.