AI+ digital experimental design and analysis focusing on practical ability cultivation of postgraduates

The rapid advancement of artificial intelligence (AI) and digital technologies has necessitated the enhancement of the practical skills of graduate students, particularly in applying theoretical knowledge to real-world problems. However, traditional training models face several challenges, such as insufficient guidance, delayed feedback, inefficient supervision, and a lack of personalized learning experiences. This paper addresses these limitations by proposing an AI+ digitalization-based model designed to improve the practical ability of graduate students. This model integrates AI technologies with a digitalized experimental environment to provide a more personalized, efficient, and data-driven learning process.

The AI+ digitalization model is composed of three main layers: the data layer, the capability layer, and the application layer. The data layer gathers information on students, learning resources, experimental data, and equipment. In the capability layer, AI technologies such as deep learning and cloud computing are used to process the gathered information, providing insights that guide experimental decisions. The application layer presents these insights in the form of interactive learning tools, including virtual lab assistants and personalized learning services. Key features of the model include automated lab access, smart equipment management, and AI-driven real-time feedback on experimental progress. AI technologies allow for the continuous monitoring of students’ experiments, identifying issues, and delivering tailored feedback to enhance their learning experience. To evaluate the model, a case study was conducted using a signal amplification experiment. Students engaged in prelab activities, including reviewing digital learning materials, designing circuit diagrams, and simulating experimental setups. During the lab session, they worked in a digitally enabled environment where the system automatically recorded their progress, provided AI-assisted troubleshooting, and generated real-time analysis of their experimental data. After the experiment, the students received personalized feedback, and their instructors monitored their progress remotely, ensuring timely and data-informed evaluations.

The experimental results highlighted several key benefits of the AI+ digitalization model compared with traditional training methods. First, the model significantly improved lab management efficiency by automating routine tasks such as equipment distribution, lab access, and progress tracking. Second, students benefited from personalized learning experiences, with the system offering targeted feedback based on their performance and learning patterns. High-performing students were identified for more advanced experimental opportunities, whereas students requiring additional support were provided with extra resources and guidance. Third, the AI-driven system’s real-time tracking of experimental data allowed instructors to intervene promptly in cases of incorrect setups or safety concerns, improving the overall quality of the experimental process and helping students more deeply understand key concepts.

The AI+ digitalization-based model for practical ability training offers a transformative approach to graduate education. By integrating AI technologies into experimental designs and execution processes, the model enhances both the efficiency and effectiveness of teaching and learning. It equips students with the skills necessary to conduct independent research, troubleshoot complex problems, and apply theoretical knowledge to practical situations. The model also provides instructors with valuable data, enabling more informed decisions regarding curriculum development, student assessment, and resource management. Ultimately, the AI+ digitalization model can significantly improve the practical abilities of graduate students, fostering innovation and preparing them to meet the demands of an increasingly digital and technology-driven world.