港科大(广州)新教师培训：AI赋能教学的实用指南
主题 Title
AI教学探索：教师主导的“任务匹配”模式
To Teach with AI or Not? Instructor-centered Task Mapping for AI-empowered Teaching
主讲嘉宾 Speaker
许丕文 James SHE
信息枢纽计算媒体与艺术学域 副教授
Associate Professor, Thrust of Computational Media and Arts, INFH
朱俊华 Junhua ZHU
教育科学学院通识教育基柱 社会科学教育实践助理教授
Assistant Professor of Practice in Social Science Education, Pillar of General Education, CES
参加对象 Invitee
全体教师
Faculty members who have an interest in this topic.
主办单位 Organized by
教育创新与实践研究所、教学发展处
Department of Teaching Development (DTD),
Institute of Education Innovation and Practice (IEIP)
时间 Time
2026年5月8日（星期五）
12:00–13:30（北京时间）
8 May 2026 (Friday)
12:00-13:30 (Beijing Time)
午餐安排 Lunch
活动为参会教师提供午餐
Lunch will be provided for all participants.
语言 Language
英语
English
报名方式 Registration
报名截止时间：2026年4月30日 15:00 请通过以下二维码完成注册（报名成功后将收到确认邮件）
Deadline for registration:15:00, 30 Apr.
Please register through the code below.
(A confirmation mail will be delivered to you if registered successfully)
地点 Venue
报名成功后，将通过确认邮件（含日程邀请）发送具体地点信息
Upon successful registration, a confirmation email (calendar invite) with venue details will be delivered to you.
内容简介 Abstract
当前全球高等教育面临一个核心难题：如何引导高校教师自信地将人工智能技术融入教学实践？香港科技大学（广州）的许丕文教授与朱俊华教授提出了一种创新的“任务匹配”理念，以应对此挑战。该理念旨在帮助教师系统地剖析课程内容，将其分解为一系列结构化的教学任务，并为每个任务精准匹配适宜的人工智能工具。这种方法支持教师以自主可控的步调逐步采纳AI技术，同时始终坚守以人为本的教学理念。它着重发挥教师在课程设计、专业知识深度及教学判断方面的独特优势，并获得了广东省、广州市及校内相关教学学术研究（SoTL）项目的鼎力支持。
本次培训将引导参会教师通过实践操作，掌握任务匹配的策略，并体验我们专门开发的教学任务分类工具（Task Classifier）。此工具是教育界与产业界携手共创的成果，旨在为构建高质量的人机协同教学模式开辟一条开放、可持续且易于推广的道路。
A critical challenge facing global higher education is empowering university instructors to take their first confident step toward AI-integrated teaching. At HKUST(GZ), Prof. James She and Prof. Junhua Zhu address this issue through a practical task-mapping concept that helps educators decompose courses into structured teaching tasks and align them with appropriate AI tools. This systematic concept supports gradual, self-paced AI integration while centering the instructor in pedagogy. Supported by regional and institutional SoTL grants, this initiative refocuses on the distinctive instructor capabilities: curriculum design, professional insights, and pedagogical judgment. In this training session, participants will learn the task-mapping concept via hands-on practice, supported by a dedicated task classifier tool developed to operationalize the framework. Developed in collaboration with educational and industrial partners, this tool establishes an open, sustainable, and scalable pathway for high-quality human-AI co-teaching in higher education.
主讲人简介 Speaker
许丕文博士是香港科技大学（广州）计算媒体与艺术学域的副教授，并兼任香港科技大学的教职。他的研究专长涵盖人工智能、数字媒体工具及数字人技术，尤其侧重于跨学科教育情境，包括艺术教育、艺术史教育以及以人类创造力为核心的课程。他在人工智能教育领域的探索获得了广东省、广州市政府以及港科大（广州）实践研究项目的资助。此外，许博士积极与产业界、政府教育政策研究机构及相关培训组织建立合作关系，致力于推动人工智能在教育领域的融合应用与培训项目的发展。
Dr. James Sheis Associate Professor of Computational Media and Arts at HKUST(GZ), with a joint appointment at HKUST. His research focuses on AI, digital media tools, and digital humans for interdisciplinary education, including art education, art history education, and courses centered on human creativity and making. His research in AI education has been supported by the Guangzhou Municipal Government and HKUST(GZ); he also collaborates with industry, educational policy bodies, and training centers to advance AI-integrated education and executive training programs.
朱俊华博士现为香港科技大学（广州）教育科学学院的实践助理教授。她拥有香港大学颁发的高等教育学博士学位，研究领域包括大学改革、创新人才培养模式以及生成式人工智能在高等教育中的应用。朱博士热衷于教学创新实践和行动研究，曾主持多项国家级及区域性的教育教学研究项目，其研究成果已在国内外权威期刊上发表。
Dr. Junhua Zhuis Assistant Professor of Practice at theCollege of Education Sciences, HKUST(GZ).She holds a PhD in Higher Education from the University of Hong Kong. Her research focuses on university reform, innovative talent cultivation, and generative AI applications in higher education.She actively engages in teaching innovation initiatives, with extensive experience in leading national and regional educational research projects and publishing in international and Chinese core journals.