In this paper, we proposed an interval degradation model to improve the reliability of the classical single point degradation model. The interval degradation model is very flexible when model parameters follows different distributions. Twenty-five types of interval Gamma degradation models are considered and discussed under different conditions. The reliabilities of interval Gamma degradation models are obtained. The Monte Carlo method has been studied to compute the reliability and lifetime of interval Gamma degradation model. The numerical examples are conducted to compare the interval degradation model with the classical single point degradation model. Simulation results reveal that the performance of reliability and mean lifetime of interval Gamma degradation model are much better than those of the single Gamma degradation model. Finally, we applied our model to a real data example and demonstrated the effectiveness and feasibility of the interval Gamma degradation model.

With the rapid advancement of digital technologies and the growing emphasis on high-quality and sustainable higher education, application-oriented universities are undergoing profound digital transformation. This conceptual study develops a sustainability-oriented framework grounded in Socio-Technical Systems Theory and the Dynamic Capabilities perspective, with China’s Four Futures initiative serving as a contextual lens for identifying key transformation domains. The framework integrates four institutional dimensions: faculty development, pedagogical innovation, institutional governance, and digital resource integration. It distinguishes existing digital foundations from prospective AI-enabled extensions and illustrates selected elements through an exploratory smart laboratory governance pilot. The pilot demonstrates how shared data standards, university–college workflows, laboratory management, safety management, teaching operations, and mobile visualization can be aligned with the proposed framework. However, this study does not claim improvements in teaching quality, operational efficiency, or resource utilization. Instead, it identifies key implementation conditions related to data quality, privacy, security, model validation, human oversight, accessibility, life cycle costs, and environmental impact. The framework provides a basis for future longitudinal and multi-institutional evaluation of responsible AI-enabled digital transformation in application-oriented higher education.