The rapid development of artificial intelligence technology has propelled the automated, humanized, and personalized learning services to become a core topic in the transformation of education. Generative artificial intelligence (GenAI), represented by large language models (LLMs), has provided opportunities for reshaping the methods for setting personalized learning objectives, learning patterns, construction of learning resources, and evaluation systems. However, it still faces significant limitations in understanding the differences in individual static characteristics, dynamic learning processes, and students’ literacy goals, as well as in actively differentiating and adapting to these differences. The study has clarified the technical strategies and application services of GenAI-empowered personalized learning, and analyzed the challenges in areas such as the lag in theoretical foundations and lack of practical guidance, weak autonomy and controllability of key technologies, insufficient understanding of the learning process, lack of mechanisms for enhancing higher-order literacy, and deficiencies in safety and ethical regulations. It has proposed implementation paths around interdisciplinary theoretical innovation, development of LLMs, enhancement of personalized basic services, improvement of higher-order literacy, optimization of long-term evidence-based effects, and establishment of a safety and ethical value regulation system, aiming to promote the realization of safe, efficient, and sustainable personalized learning.

Generative artificial intelligence (GenAI) models, such as ChatGPT, have rapidly gained popularity. Despite this widespread usage, there is still a limited understanding of how this emerging technology impacts different stakeholders in higher education. While extensive research exists on the general opportunities and risks in education, there is often a lack of specificity regarding the target audience—namely, students, educators, and institutions—and concrete solution strategies and recommendations are typically absent. Our goal is to address the perspectives of students and educators separately and offer tailored solutions for each of these two stakeholder groups. This study employs a mixed-method approach that integrates a detailed online questionnaire of 188 students with a scenario analysis to examine potential benefits and drawbacks introduced by GenAI. The findings indicate that students utilize the technology for tasks such as assignment writing and exam preparation, seeing it as an effective tool for achieving academic goals. Subsequent the scenario analysis provided insights into possible future scenarios, highlighting both opportunities and challenges of integrating GenAI within higher education for students as well as educators. The primary aim is to offer a clear and precise understanding of the potential implications for students and educators separately while providing recommendations and solution strategies. The results suggest that irresponsible and excessive use of the technology could pose significant challenges. Therefore, educators need to establish clear policies, reevaluate learning objectives, enhance AI skills, update curricula, and reconsider examination methods.

Xinjiang long-staple cotton is widely used in the production of high-end textiles due to its excellent quality. However, foreign fibers such as plastic film, cotton boll hull, human hair, and polypropylene fibers are easily mixed in during mechanical harvesting and processing.