Since the first instances of online education, where courses were uploaded to accessible and shared online platforms, this form of scaling the dissemination of human knowledge to reach a broader audience has sparked extensive discussion and widespread adoption. Since personalized learning still holds significant potential for improvement, new artificial intelligence (AI) technologies have been continuously integrated into this learning format, resulting in a variety of educational AI applications such as educational recommendation and intelligent tutoring. The emergence of intelligence in large language models (LLMs) has allowed these educational enhancements to be built upon a unified foundational model, enabling deeper integration. In this context, we propose MAIC (Massive AI-Empowered Course), a new form of online education that leverages LLM-driven multi-agent systems to construct an AI-augmented classroom, balancing scalability with adaptivity. Beyond exploring the conceptual framework and technical innovations, we conduct preliminary experiments at Tsinghua University, Beijing, one of the leading universities in China. Drawing from more than 100000 learning records of more than 500 students, we obtain a series of valuable observations and initial analyses. This project will continue to evolve, ultimately aiming to establish a comprehensive open platform that supports and unifies research, technology, and applications to explore the possibilities of online education in the era of large-model AI. We envision this platform as a collaborative hub that brings together educators, researchers, and innovators to collectively explore the future of AI-driven online education.

Word embedding has drawn a lot of attention due to its usefulness in many NLP tasks. So far a handful of neural-network based word embedding algorithms have been proposed without considering the effects of pronouns in the training corpus. In this paper, we propose using co-reference resolution to improve the word embedding by extracting better context. We evaluate four word embeddings with considerations of co-reference resolution and compare the quality of word embedding on the task of word analogy and word similarity on multiple data sets. Experiments show that by using co-reference resolution, the word embedding performance in the word analogy task can be improved by around 1.88 $\mathrm{\%}$. We find that the words that are names of countries are affected the most, which is as expected.

The efficiency of QoS-aware service composition is important since most service composition problems are known to be NP-hard. With the growing number of web services, service composition is like a decision problem on selecting services or/and execution plans to satisfy the users' end-to-end QoS requirements (e.g. response time, throughput). Composite services with the same functionality may have different execution plans, which may cause different end-to-end QoS. This paper presents a model combining semantic data-links and QoS, which leads to an efficient approach to automatic construction of a composite service with optimal end-to-end QoS. The approach is based on a greedy algorithm to select both services and execution plans for composite services. Empirical and theoretical analyses of the approach show that its time complexity is O(mn²) for a repository with n services and an ontology with m concepts. Moreover, the approach increases linearly in time when using an index to search services in the repository. Tests with a repository with 20000 services and an ontology with 300000 concepts show that the algorithm significantly outperforms current existing algorithms in terms of composition efficiency while achieving optimal end-to-end QoS.