Learning and Teaching in the Era of Generative Artificial Intelligence Technologies: An In-Depth Exploration Using Multi-Analytical SEM-ANN Approach

Abstract

The arrival of generative artificial intelligence (GAI) technologies marks a significant transformation in the educational landscape, with implications for teaching and learning performance. These technologies can generate content, simulate interactions, and adapt to learners' needs, offering opportunities for interactive learning experiences. In China's education sector, incorporating GAI technologies can address educational challenges, enhance teaching practices, and improve performance. This study scrutinises the impact of GAI technologies on learning performance in the education sector, focusing on the mediating roles of e-learning competence (EC), desire for learning (DL), and beliefs about the future (BF), as well as the moderating role of facilitating conditions amongst Chinese educators. Data was collected from 411 teachers across various educational institutions in China using purposive sampling. PLS-SEM and ANN were employed to assess the suggested structural model. The study results indicate that GAI technologies significantly influence learning performance by mediating EC, DL, and BF roles. Furthermore, facilitating conditions positively moderate the association amongst GAI technologies and EC, DL, and BF. This study underscores the critical role of self-determination theory in shaping the effective incorporation of GAI technologies in education, offering valuable insights to improve teaching and learning outcomes in the Chinese education sector.