The Study on Quality Assessment Framework of Generative Digital Educational Resources

As the development of information technologies such as large language models progresses, the traditional creation of digital educational resources by humans has gradually evolved into a human-machine collaborative approach, namely generative digital educational resources, where generative AI systems assist humans in resource production. Based on the American Customer Satisfaction Index (ACSI) model, an evaluation model affecting the quality of generative digital educational resources was constructed from the user perspective, mainly including the DER quality system, user perceived value, and user satisfaction. In this work, we present a number of experiments that shed light on this problem. By establishing a satisfaction structural equation model and using SPSS 26.0 software and Smart PLS 3.0 software for data analysis and verification, we further propose tests that demonstrate this evaluation framework has good performance in terms of reliability and validity. The results confirm the applicability of the model and the relationships among the latent variables in the model.