Four projects from Xi'an Jiaotong University (XJTU) have been selected as 2025 theme case projects, as announced by the Ministry of Education's China Academic Degrees and Graduate Education Development Center (CDGDC).

These projects include two in modernized industrial systems and two in AI+, covering energy and power, resources and environment, and accounting. This marks a significant achievement in the university's reform of professional graduate cultivation and case-based teaching.

Overview of the approved projects

1. R&D and application of mechanism-data dual-driven digital twin nuclear reactor technology

Project description: Addressing the gap between theory and engineering practice in the cultivation of high-level nuclear energy talent, this project uses the self-developed core analysis software NECP-Bamboo. It focuses on four scenarios: the Hualong One digital twin implementation, error tracing, precise fuel-leak localization, and fast-reactor twin development, and aims to fill the gap in professional degree cases for digital twin nuclear energy.

Academic value: Establishes a theoretical framework for dual-driven digital twins based on original Chinese technical practices, reveals the coupling mechanisms between physical deviations and data noise, and provides a methodological paradigm for intelligent advanced nuclear energy.

2. Cultivation of excellent engineering talent in flexible, efficient, clean, and low-carbon energy power systems

Project description: Focused on the global energy transition and China's "Dual Carbon" goals, this project integrates generative AI into curriculum teaching. It features a teaching case on AI-empowered modeling and two research cases on multispectral intelligent sensing and multi-energy complementary systems (Polygeneration).

Academic value: Responds to the fragmentation of traditional teaching by promoting human-machine collaboration, shifting the focus from mechanism derivation to active cognitive construction, and helping students solve complex engineering problems in extreme environments (high temperature/pressure) and in source-grid-load-storage synergy.

3. AI-driven smart operation and maintenance (O&M) of industrial circulating water

Project description: Using a fictional entity, "Donghua Chemical," this project develops four progressive cases that address delayed perception, data silos, and decision-making blind spots in industrial water systems. It covers electrochemical fingerprinting for water quality, digital twins for small-sample models, and deep reinforcement learning for global optimization of chemical dosing.

Academic value: Breaks the limitation of "emphasizing algorithms while neglecting mechanisms" by embedding electrochemical equations into deep learning models, and creates an interdisciplinary perception-decision closed loop that integrates environmental engineering with AI.

4. Theme case study on AI-driven credit model transformation

Project description: This project focuses on how smart banking can address the financing challenges faced by small and medium-sized enterprises (SMEs). Taking Chongqing Rural Commercial Bank as an example, it illustrates how enterprise operating data is generated, valued, and integrated into bank credit systems through AI.

Academic value: Expands traditional credit management theory by incorporating certified data assets as independent credit factors. It reveals the mechanism of "data credit enhancement" and provides a new paradigm for SME financing in the digital age.

So far, XJTU has established a three-tier development system including 142 teaching case libraries at the university level, 81 professional degree case projects at the provincial level, and 22 theme case projects at the national level.

The university will continue to align case construction with national strategic needs, encourage faculty to explore excellent teaching cases, and strengthen the practical abilities of professional degree graduates to ensure high-quality development in education.