In today’s world, artificial intelligence (AI) and human intelligence coexist, and no field is free from the impact of AI. At present, education cannot be discussed without mentioning AI, which has an omnidirectional impact on all its areas, including the purpose, content, method, and evaluation system. This study aimed to explore the future direction of education by examining the current impact and predicting future impacts of AI. It also examined research trends and collaboration status by country through network analysis, topic modeling and global research trends in AI in education (AIED), by applying the Latent Dirichlet Allocation algorithm. Over the past 20 years, the number of papers on AIED has steadily increased, with a dramatic rise since 2015. The research can be broadly classified into eight topics, including “changes in the content of teaching and learning.” Using a linear regression model, three hot topics, two cold topics and trend changes for each research topic were identified. The study found that AIED research should be more thematically diversified and in-depth; this directly applies AI algorithms and technologies to education, which should be further promoted. This study provides a reference for exploring the direction of future AIED research.

This study investigates the application of large language models (LLMs) in sentiment analysis of financial news and their use in developing effective investment strategies. We conducted sentiment analysis on news articles related to the top 30 companies listed on Nasdaq using both discriminative models such as BERT and FinBERT, and generative models including Llama 3.1, Mistral, and Gemma 2. To enhance the robustness of the analysis, advanced prompting techniques—such as Chain of Thought (CoT), Super In-Context Learning (SuperICL), and Bootstrapping—were applied to generative LLMs. The results demonstrate that long strategies generally yield superior portfolio performance compared to short and long–short strategies. Notably, generative LLMs outperformed discriminative models in this context. We also found that the application of SuperICL to generative LLMs led to significant performance improvements, with further enhancements noted when both SuperICL and Bootstrapping were applied together. These findings highlight the profitability and stability of the proposed approach. Additionally, this study examines the explainability of LLMs by identifying critical data considerations and potential risks associated with their use. The research highlights the potential of integrating LLMs into financial strategy development to provide a data-driven foundation for informed decision-making in financial markets.

Recently, there has been growing educational interest in competency. Global organizations, such as the United Nations (UN) and Organization for Economic Co-operation and Development (OECD), which are leading the discourse on education reform, are undertaking the lead in spreading awareness regarding competency education. Since 2015, the number of published articles on competency education has been rapidly increasing. This paper aims to provide significant implications for creating a sustainable future of competency education. A topic modeling method was used to empirically analyze latent topics and international research trends in 26,532 articles published on competency-based education (CBE). As a result of the analysis, 15 topics were derived, including “approach to competency development.” In addition, five topics including “learning skills” and “teacher training” were found to be hot topics with the increasing article publication. The rapidly changing modern society is calling for a transformation in education. We hope that the results of this study paves the way for further research exploring new directions for education, such as competency education.

Multiplayer Online Battle Arena (MOBA) games, exemplified by titles such as League of Legends and Dota 2, have attained global popularity and have been formally recognized as an official event in the 2022 Hangzhou Asian Games, thus establishing their significance in the esports industry. In this study, we proposed a machine learning-based model for predicting comeback victories by leveraging the object bounty mechanism, a critical yet underexplored aspect of previous research. By closely examining the game environment following the activation of the bounty system, we identified pivotal variables and constructed novel indicators that contribute to successful comebacks. Furthermore, an individualized case analysis based on SHapley Additive exPlanations (SHAP) provides new insights to support strategic in-game decision-making and enhance the player experience. The experimental results demonstrate that the indicators introduced in this study, such as the weighted team champion mastery and similarity in champion mastery among the team’s main champions, significantly influence the likelihood of a comeback victory. By capturing the intrinsic dynamism of MOBA games, the proposed model is expected to improve player engagement and satisfaction.