Abstract: The permeability coefficient of coal seams serves as a crucial indicator for evaluating the difficulty of gas extraction, the effectiveness of pressure relief in protective layers to enhance permeability, and the severity of coal and gas outburst hazards. This paper briefly reviews the development of coal seam permeability coefficient determination methods, focusing on the research progress and respective characteristics of laboratory methods, radial flow methods, gas pressure recovery curve methods, and gas injection methods. Analysis indicates that laboratory methods offer advantages such as simplicity and low cost, but struggle to replicate underground conditions perfectly; they may serve as important supplementary tools for field measurements in the future. The radial flow method, though widely applied in the field, suffers from cumbersome calculation steps and discontinuous data values. Despite various optimization strategies proposed by scholars, no unified calculation standard has been established. The gas pressure recovery curve method offers strong in-situ testing capabilities and intuitive data interpretation, but it is significantly affected by plugging quality and pressure measurement accuracy, making it difficult to accurately capture the mid-term radial flow slope segment. The gas injection method demonstrates promising potential in complex seepage coal seams, offering a viable approach for anisotropic permeability determination. However, its field measurement process is complex, and its on-site testing accuracy and data stability are limited. Future research on coal seam permeability coefficient determination should focus on several key areas: developing unified theoretical models that integrate multi-field coupling and multi-scale analysis; establishing industry standards and computational specifications for the radial flow method; enhancing the accuracy of pressure recovery curve identification and analysis; optimizing gas injection medium selection and test point layout; and improving field monitoring precision and automation levels.