“我们希望让学生成为 AI 的批判性思考者, 而不仅仅是技术的使用者,” 麻省理工学院苏世民学院学术副院长兼 EECS 系主任 Asu Ozdaglar, 表示。
“We want to empower students to become critical thinkers about AI, not just users of the technology,” says Asu Ozdaglar, deputy dean of academics for the MIT Schwarzman College and department head of EECS.
扩大人工智能教育的协作模式
A collaborative model for expanding AI education
将该计划付诸实践需要整个学院, 的广泛合作，包括领导层, 员工, 的支持以及来自金融、计算机科学和可持续发展等领域的六位以上讲师的贡献。他们一起, 帮助举办了一个研讨会，将核心技术概念与适用于各种课堂环境的示例和教材相结合。
Bringing the program to life required broad collaboration across the college, including support from leadership, staff, and contributions from more than half a dozen instructors in fields ranging from finance and computer science to sustainability. Together, they helped shape a workshop that paired core technical concepts with examples and teaching materials adaptable to a range of classroom settings.
在 Jake 和 Robin Reynolds, 的支持下，该试点项目于 7 月份汇集了来自艾伦大学, 巴布森学院, 布兰迪斯大学, 马歇尔大学,、马萨诸塞大学洛厄尔分校,、北德克萨斯大学, 和温特沃斯理工学院的 19 名参与者。参与者与麻省理工学院的教师和讲师, 一起通过演示, 视频, 和练习, 的组合探索机器学习建模背后的教学法，并在实践活动中进行合作，重点是将课程的材料和方法翻译到他们自己的课堂上。
With support provided by Jake and Robin Reynolds, the pilot brought together 19 participants in July from Allen University, Babson College, Brandeis University, Marshall University, the University of Massachusetts at Lowell, the University of North Texas, and Wentworth Institute of Technology. Working alongside MIT faculty and instructors, participants explored the pedagogy behind Modeling with Machine Learning through a mix of demos, videos, and exercises, and collaborated in hands-on activities focused on translating the course的 materials and methods to their own classrooms.
“这个机会非常及时，因为我们正在我的部门启动人工智能和数据科学项目,” 麻省大学洛厄尔分校计算机科学助理教授 Wenjin Zhou, 说。 “我们’已经在思考:我们如何在人工智能领域教授下一代计算机科学家?我们如何将人工智能融入教学?我想了解更多关于其他人是如何做的,，特别是回答这个问题:如果人工智能现在可以为任何人创建工具,计算机科学家做什么?”
“This opportunity has been very timely because we are starting an AI and data science program in my department,” says Wenjin Zhou, assistant professor of computer science at UMass Lowell. “We’ve already been thinking about: How do we teach our next generation of computer scientists within the area of AI? How do we integrate AI in the teaching? I wanted to learn more about how other people are doing it, and especially answer the question: If AI can create tools for anyone now, what does a computer scientist do?”
当谈到 AI, Amin 指出, 并不缺乏高质量的材料。通常缺少的是背景: 教师和学生将人工智能概念与特定学科, 问题, 和思维方式联系起来的机会。这些联系通常是通过对话和推理建立的,，而不是通过将人工智能呈现为一组固定的想法来建立。但教练能力仍然是最稀缺的资源之一。
When it comes to AI, Amin notes, there is no shortage of high-quality material. What is usually missing is context: Opportunities for instructors and students to connect AI concepts to specific disciplines, problems, and ways of thinking. Those connections are often built through dialogue and reasoning, rather than by presenting AI as a fixed set of ideas to be received. But instructor capacity remains one of the scarcest resources.
“ 稀缺的是教育工作者准备好将人工智能教授为不仅仅是一套固定的概念和工具, 将其扎根于自己的领域, 帮助学生以判断力使用它, 并揭开它的神秘面纱, 这样学生不仅能应用模型，还能学会质疑, 适应, 并与它们一起构建,” Amin 解释道。
“What is scarce are educators prepared to teach AI as more than a fixed body of concepts and tools, to ground it in their own field, help students use it with judgment, and demystify it, so students do not just apply models but learn to question, adapt, and build with them,” explains Amin.
Shen Shen, EECS 讲师和研讨会讲师之一, 添加, “我们如何确保机器学习不仅仅是一个黑匣子, 也不是这项神奇的新技术? 您可以将其视为一种工具, 或一个新框架来帮助您解决特定领域中的问题。”
Shen Shen, an EECS lecturer and one of the workshop instructors, adds, “How do we make sure that machine learning is not just a black box, nor this magic piece of new technology? You can think of it as a tool, or a new framing to help you solve the problem in your specific domain.”
从试点研讨会到教育者网络
From pilot workshop to educator network
参与者在本周结束时反思了他们计划适应其学科和课程的研讨会材料和教学方法。他们的反馈将有助于塑造试点的未来迭代，并支持更广泛的教育工作者网络的发展，致力于在不同的学习环境中扩展人工智能教育。
Participants ended the week by reflecting on which workshop materials and teaching approaches they planned to adapt for their disciplines and courses. Their feedback will help shape future iterations of the pilot and support the development of a broader network of educators committed to expanding AI education across diverse learning environments.
Weijie Pang, 是温特沃斯理工学院计算机科学助理教授，参加了研讨会, 最期待正在进行的社区建设活动。 “这是一个与来自不同专业和领域的其他教师交流的非常宝贵的机会。我可以看到其他大学在做什么，以及我们可以互相学习,”，她说。
Weijie Pang, an assistant professor of computer science at the Wentworth Institute of Technology who attended the workshop, looks most forward to ongoing community building activities. “This is a really valuable opportunity to communicate with other faculty from different majors and areas. I can see what other universities are doing and what we can learn from each other,” she says.
“It 了解不同大学不同学科的每个人都在努力解决同样的问题，即随着技术的变化，我们如何才能最好地为学生服务，这很有帮助。希望,我们可以通过对人工智能的用途更加前瞻性和预见性来帮助他们取得成功,”布兰迪斯大学计算机科学助理教授迪伦·卡什曼,说。
“It的 helpful to know that everybody within different disciplines at different universities is struggling with the same questions of how we can best serve our students as the technology is changing. Hopefully, we can set them up for success by being a little bit more forward and anticipatory of what the AI use is going to be,” says Dylan Cashman, an assistant professor of computer science at Brandeis University.