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.

Open-source large language models (LLMs) research has made significant progress, but most studies predominantly focus on general-purpose English data, which poses challenges for LLM research in Chinese education. To address this, this research first reviewed and synthesized the core technologies of representative open-source LLMs, and designed an advanced 1.5B-parameter LLM tailored for the Chinese education field. Chinese education large language model (CELLM) is trained from scratch, involving two stages, namely, pre-training and instruction fine-tuning. In the pre-training phase, an open-source dataset is utilized for the Chinese education domain. During the instruction fine-tuning stage, the Chinese instruction dataset is developed and open-sourced, comprising over 258,000 data entries. Finally, the results and analysis of CELLM across multiple evaluation datasets are presented, which provides a reference baseline performance for future research. All of the models, data, and codes are open-source to foster community research on LLMs in the Chinese education domain. 

The Quantum Education Summit event, hosted at CosmoCaixa Science Museum of Barcelona, will gather more than 300 participants from five different continents.

The topics of the conference will cover the full spectrum of quantum education:  from foundational curriculum design, workforce training and inclusive global participation to policy strategies, educational innovations, and outreach efforts that advance accessibility and broaden the impact of quantum learning worldwide.

Keynote speakers include Nobel Laureate Carl Wieman, from Stanford University, Maité Depuis, from Perimeter Institute of Canada, and Eboney Hearn, from MIT Introduction to Technology, Engineering and Science.