Risks and Regulations for Application of the LLaMA Model in University Future Learning Centers - QIAO Jinhua, MA Xueyun (Nongye tushu qingbao xuebao)
[Purpose/Significance] The rapid advancement of artificial intelligence (AI) technology is transforming various sectors, particularly in higher education. The LLaMA (Large Language Model Meta AI) represents a significant innovation in this arena, making its application within university future learning centers increasingly important. As institutions of higher education strive to create environments conducive to learning and growth, understanding the construction requirements of future learning centers becomes paramount. This study delves into the integration of LLaMA core technologies in these learning spaces and emphasizes the importance of evolving libraries into intelligent learning support systems. [Method/Process] The methodology employed in this research combines technical deconstruction and scenes for validation, allowing for a comprehensive analysis of the legal risks associated with embedding advanced technologies in educational frameworks. By systematically examining these potential risks, the study aims to establish a well-rounded perspective on the implications of AI deployment in educational settings. [Results/Conclusions] The study identifies three principal challenges encountered in the application of the LLaMA within university learning centers. The first challenge arises from reliability risks linked to content generated by the AI, which may be affected by biases present in the training data. Such biases can lead to the dissemination of inaccurate or misleading information, undermining the trustworthiness of educational resources. Secondly, there are privacy leakage risks, particularly associated with the retention of user behavioral data. As AI systems analyze user interactions, there is a potential for sensitive information to be exposed or misused, raising concerns about student privacy and data security. The third challenge involves ownership determination dilemmas regarding the content generated through AI-driven creative processes. These dilemmas are intricately tied to existing copyright law frameworks, which may not adequately address the complexities introduced by human-machine collaboration in content creation. In response to these challenges, the study proposes several pathways for governance aimed at effectively navigating the landscape of AI in education. It suggests the implementation of dynamic data cleansing mechanisms to address reliability risks and inaccuracies. Additionally, establishing tiered privacy protection systems can help safeguard against user data breaches. Legal frameworks also need refinement to ensure clear ownership distribution for outputs of human-machine collaboration. Ultimately, optimizing the application of the LLaMA model in university future learning centers necessitates a careful balance between technological innovation and legal regulation. By focusing on technical refinement, risk control, and relevant regulatory measures, the development and application of AI can be advanced, facilitating a more integrated evolution of artificial intelligence and educational practices.