This study investigates the factors influencing undergraduate students’ self-directed learning (SDL) abilities in generative Artificial Intelligence (AI)-driven interactive learning environments. The advent of generative AI has revolutionized interactive learning environments, offering unprecedented opportunities for personalized and adaptive education. Generative AI supports teachers in delivering smart education, enhancing students’ acceptance of technology, and providing personalized, adaptive learning experiences. Nevertheless, the application of generative AI in higher education is underexplored. This study explores how these AI-driven platforms impact undergraduate students’ self-directed learning (SDL) abilities, focusing on the key factors of teacher support, learning strategies, and technology acceptance. Through a quantitative approach involving surveys of 306 undergraduates, we identified the key factors of motivation, technological familiarity, and the quality of AI interaction. The findings reveal the mediating roles of self-efficacy and learning motivation. Also, the findings confirmed that improvements in teacher support and learning strategies within generative AI-enhanced learning environments contribute to increasing students’ self-efficacy, technology acceptance, and learning motivation. This study contributes to uncovering the influencing factors that can inform the design of more effective educational technologies and strategies to enhance student autonomy and learning outcomes. Our theoretical model and research findings deepen the understanding of applying generative AI in higher education while offering important research contributions and managerial implications.

The popularity of green innovation has dramatically increased in the recent times because of the potential benefits attached with it. Therefore, in order to make the technology more affordable, green innovation is the key to enhancing the affordability factor. On the other hand, in order to safeguard the environment, the role of media is one of fundamental importance. In contrast, energy consumption is often regarded as a key indicator of economic prosperity, mostly at the cost of the environment. Hence, the present study attempts to explore the asymmetric effect of the film and drama industry, energy efficiency, and economic growth on green innovation, with the help of the latest quantile autoregressive distributed lag (QARDL) method for the period 2000Q1 to 2019Q4. The results have reported a positive and significant association of the Film and Drama Industry, Energy Efficiency, and economic growth on the quantiles of Green Innovation. Based on the findings, it is recommended that there is a dire need to develop content that promotes the green innovation, whereas, more investments are to be sought after, so as to enhance the level of energy efficiency.

The creation of new artistic forms is an important aspect of the classification of artistic construction. The training of animation talents should be based on the market demand for professionals in the animation industry. While emphasizing the training of professional characteristics, it is important to consider the cross-complementation of related disciplines. The animation major at the Zhongnan University of Economics and Law has taken the initiative to investigate the market. They started by addressing the challenges faced in the development of the animation major and focused on the classification training of animation talents within the newly established new art department. As a result, they have developed a new professional curriculum system, a system for cross-integration of different disciplines, and a practical training system. These initiatives have facilitated the classification of platforms, the integration of multiple disciplines, and the development and training of practical skills within the animation major. We are actively exploring and practicing a new training method for animation talents in the art department. Our goal is to train high-quality applied animation talents who can contribute to local economic and social development. By doing so, we aim to promote the coordinated development of the art department and the field of animation. In order to quantify the teaching effect, we introduced a dynamic method to model the teaching system. This model was mathematically established as a dynamic system. Through modeling and analysis, the data reflected the positive impact of educational innovation and reform on teaching, thereby demonstrating the effectiveness of our innovation and reform.