Brainstorming has become a widely adopted technique for idea generation within organizations. However, traditional in-person brainstorming sessions are subject to several well-documented limitations such as production blocking, social anxiety, social loafing, and bias against nonconforming ideas. Recent advancements in large language models (LLMs) offer promising avenues for reimagining and enhancing the brainstorming process. In this research, I aim to achieve new theoretical understanding of how LLM-based brainstorming (LBB) simulates human brainstorming (in conversational and nominal sessions) for idea generation. Hypotheses were developed on the recent breakthroughs in few-shot learning and chain-of-thought prompting. To empirically test these hypotheses, I conducted a series of simulated brainstorming sessions utilizing GPT-4o agents. The output from these sessions was assessed using both an LLM-based evaluator agent and human raters. The findings largely support both hypotheses and reveal several noteworthy insights with implications for both research and practices.

本次讲座将介绍一个用于分析多尺度复杂生物数据的流形拟合框架。将展示三个关键应用：(1) scAMF用于单细胞RNA测序数据去噪和提高聚类准确性，(2) CellScope用于构建揭示细胞层次结构的多层次细胞图谱，(3) 利用英国生物样本库数据进行人群尺度代谢组学分析以识别代谢亚群和疾病关联。

In recent years, predictive AI has been widely used across various domains, while the advent of generative AI, exemplified by ChatGPT, has sparked significant interest. This paper investigates whether findings of algorithm aversion in predictive AI apply to generative AI. We hypothesize that generative AI faces reduced algorithm aversion due to lower reactance and higher trust. Our experiment reveals a reversal of algorithm aversion in generative AI, with trust mediating algorithm appreciation. These findings contribute to understanding algorithm aversion by highlighting the distinction between predictive and generative AI. We also extend the weight-on-advice paradigm to generative AI, offering insights for practitioners leveraging AI advancements.

国际化和全球化背景下，非英语外语教育对提升国家文化软实力和国际传播能力至关重要。当前我国外语教育规模虽大，但非英语语种教育面临教材更新滞后、种类稀缺，师资配置不足，课程体系固化偏重技能训练，以及AI技术支持薄弱等困境。我们认为，需加强本土教材开发共享，优化师资结构，构建模块化课程体系，推进小语种语料库建设和AI技术研发。通过技术赋能打基础、学科交叉拓边界、范式重构育生态的三维协同，最终建立契合国家战略和数字时代需求的新型人才培养体系。

Frontiers in CommunicationStudies

朗诵不仅是语言的艺术展现，更是情感与文化的传递桥梁。本次讲座将深入讲解语音训练的核心要点，包括发音的精准把握、语调的灵活运用以及节奏的巧妙控制等。同时全面剖析朗诵技巧，从对作品的深入理解到情感的真挚表达，从肢体语言的合理运用到舞台表现力的有效提升，结合实例进行生动阐释。旨在帮助将来从事语言教育（包括中小学教育）的同学夯实语音基础，提升语言表达能力与文化传承能力。

正值2025届毕业之际，上外MBA中心筹备已久，为全体毕业生准备了一份毕业惊喜！我们特别邀请到“卫哲大师兄”给大家带来毕业前的最后一课【效率为王的新周期】。相信这份别出心裁的礼物会成为大家职业生涯一份珍贵的财富。
为什么同样的时间，有人创造千万价值，有人却疲于奔命？  
如何让团队效率翻倍，在红海市场中杀出重围？ 
【卫哲《效率为王》新书讲座来袭！颠覆认知，解锁10倍增长秘诀！】

议程安排：
13:30-14:00 活动签到
14:00-14:10 主持人开场
14:10-15:40 卫哲先生讲座——效率为王的新周期
15:40-15:50 观众互动提问赠送卫哲先生签名新书《效率为王——新周期下的企业经营策略》
15:50-16:00 卫哲先生为校友陈蓉颁发“校友会读书事业部”证书
16:00-17:00 2025届毕业生有序领取双证

复杂动态系统理论（Complex Dynamic System Theory, CDST）自Larsen-Freeman于1997年首次引入应用语言学领域以来，日益受到语言研究者和教育实践者的广泛关注。其核心理论构念在语言发展与习得研究中得到了系统化的应用和持续拓展。本报告将聚焦复杂动态系统理论在二语口语发展研究中的应用。首先介绍复杂动态系统理论及其核心概念；其次阐述相关研究方法与最新实证成果；最后展望该理论在未来二语口语发展研究中的应用前景，并提出相关建议。