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美国俄克拉荷马大学孙河山教授学术讲座
- 来源：
- 学校官网
- 收录时间：
- 2025-10-18 15:46:06
- 时间：
- 2025-06-26 09:30:00
- 地点：
- B校区经管学院106室
- 报告人：
- 孙河山
- 学校：
- -/-
- 关键词：
- brainstorming, large language models, LLM, idea generation, human-computer interaction, AI-assisted creativity, few-shot learning, chain-of-thought prompting
- 简介：
- 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 based 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.
- -/- 14
报告介绍：
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 based 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.
报告人介绍：
孙河山教授目前任职于美国俄克拉荷马大学普莱斯商学院信息管理系统系。他的研究聚焦于信息技术对个人、组织和社会的深刻影响和相互作用，具体包括人机交互、商业分析、在线/数字行为等。他在MIS Quarterly（5篇，其中独作发表2篇）、Information Systems Research（4篇）、Journal of the Association for Information Systems、Decision Support Systems、International Journal of Human-Computer Studies、Journal of the American Society for Information Science and Technology等信息管理领域的国际顶级学术期刊发表了多篇学术论文，在2018-2022年“Worldwide on the Top IS Researcher List”中排名第45位。他目前是Information Systems Research的Editorial Review Board Member，并担任MIS Quarterly、Journal of the Association for Information Systems和AIS Transactions on HCI的Senior Editor。
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