一、嘉宾介绍：

Dr. Iceman Leung Hoi Yung is Assistant Professor at Beijing Normal-Hong Kong Baptist University  (BNBU) and Director of its Entrepreneurship Cooperation Centre. or of its Entrepreneurship Cooperation Centre. His academic journey spans three disciplines: design (BA, School of Design, The Hong Kong Polytechnic University), business and design management (MSc, Graduate School of Business, PolyU), and educational measurement (Doctor of Education in Psychometrics, The Education University of Hong Kong). This transdisciplinary foundation grounds his signature work in Rasch/IRT measurement and AIGC-integrated pedagogy. He serves as Adobe Certified Professional Senior Expert and Adobe Certified Educator. He coached the ACP World Championship China National Champion. An International Judge for the QS Reimagine Education Awards 2026 (QS × Wharton School), he is completing the monograph Quantum Thinking for AIGC Pedagogy.

     梁海勇博士，现任北师香港浸会大学(BNBU)文化与创意学院助理教授、创业合作中心主任。 学术历程横跨三个学科：香港理工大学设计学院、香港理工理工大学商学院、香港教育大学研究生院。 这一跨学科根基塑造了其以 Rasch/IRT 测量与 AIGC 教学法为标志的研究方向。 现任 Adobe Certified Professional 高级专家及 Adobe Certified Educator，并指导学生夺得 ACP 世界大赛中国总决赛冠军。 2026年 QS Reimagine Education 国际评委，正撰写专著《量子思维导向的AIGC教学法》。

二、讲座介绍：

时间&地点：北京大学燕南园51号院    9.21日 13:30-15:00

分享主题：What Makes Research World-Class? Quantum Thinking for Human–AI Collaboration

迈向世界级学术影响：AIGC时代量子思维与人机协作的融合创新

三、讲座要点：

“Superposition is not just a physics concept — it is how every good research decision begins.” It can be argued that we are now training a new generation of scholars who have never known research without generative AI. But here’s what often gets missed: using AI in research is not the same as thinking with AI. There’s a persistent myth that more generation means more impact. Like most myths, it contains a grain of truth wrapped in a lot of oversimplification.

       One question I’ve been getting a lot lately, especially from early-career researchers facing RAE/REF reviews and H-index comparisons, is deceptively simple: what actually separates world-class scholarship from high-volume output? In this lecture, we provide answers drawn from measurement theory and classroom practice, organized around one framework we call quantum thinking. Most frameworks for AI-era research comprise three components. First, AI generates superposed possibilities at scale. Second, a deliberate human observation — a judgment, a critique, an assessment panel — collapses those possibilities into committed insight. Third, the impact must be evidenced, not asserted, using Wilson’s (2005) four building blocks: construct map, items design, outcome space, and measurement model.

      The practical implication is important: if we want to demonstrate world-class impact, we need to measure it developmentally, not just count it bibliometrically. H-index and Google Scholar analytics tell you something that raw output alone doesn’t — but they don’t tell you everything. Fluency of publication is like fluency of ideas: necessary, but insufficient. Originality and developmental validity carry the unique variance. The same principle holds across the four domains we cover — impact measurement, AIGC knowledge distillation, research impacts scale framework, and 24-hour multi-agent quantum workflows.

     The good news is that none of this requires expensive infrastructure or genius-level talent. If impact is a decision, then impact can be engineered. The native Generative AI researcher — the scholar who pairs machine-scale generation with human-scale judgment — is not a personality type. It’s a practice. And practices can be taught.

    “叠加态不仅是物理学概念——它是每一个优秀研究决策的起点。”可以说，我们正在培养新一代的学者，他们从未经历过没有生成式 AI 的研究世界。 但这里有一个常被忽略的问题：在研究中使用 AI，不等于与 AI 一同思考。 坊间流传一种迷思：生成得越多，影响力就越大。 与大多数迷思一样，它包含了一点点事实，却包裹着大量的过度简化。

     最近我经常被问到一个问题——特别是来自那些正面对 RAE/REF 评审与 H-index 比较的年轻学者——这个问题看似简单：究竟什么真正区分了世界级学术贡献与高产量的平庸输出？ 在本讲座中，我们从测量理论与课堂实践中提取答案，并以一个名为“量子思维”的框架加以组织。 适用于 AI 时代的研究框架包含三个要素：第一，AI 以机器规模生成叠加的可能性; 第二，一次审慎的人为观测——一个判断、一次同行评议、一个评审 panel——将这些可能性坍缩为确定的洞见 ; 第三，影响力必须被证据化，而非仅仅宣称，其操作工具是 Wilson（2005）的四大建构基石：构念图谱（construct map）、题项设计（items design）、结果空间（outcome space）与测量模型（measurement model）。

     实践含义十分重要：若要展示世界级影响力，我们必须以发展性的方式去测量它，而不是仅用书目计量去计算它。H-index 与 Google Scholar 引用指标能告诉你一些单纯产出量无法告诉你的东西——但它们并不能告诉你全部。 发表的流畅度正如想法的流畅度：必要，但不充分。 原创性与发展效度，才承载着独特的变异量。 同一原则贯穿本讲座的四个实作模块：研究影响力测量、AIGC 知识蒸馏、量表框架，以及 24 小时多智能体量子工作流。

     好消息是：这一切既不需要昂贵的基础设施，也不需要天才级的头脑。 如果影响力是一种决策，那么影响力就可以被设计。 AIGC原生研究者(AI Native Researcher) ——那位将机器规模的生成与人类规模的判断相结合的学者——不是一种人格类型，而是一种实践。 而实践，是可以被传授的。

供稿：庞洁

 编辑：陈文琪 裴倍萱

 审核：张亚欧 熊哲航