摘要：Computer science education will probably focus less on coding and more on computational thinking and A.I. literacy, said Mary Lou
The New York Times｜Business Day
纽约时报｜商业版
How Do You Teach Computer Science in the A.I. Era?
人工智能时代，如何教授计算机科学？
Universities across the country are scrambling to understand the implications of generative A.I.’s transformation of technology.
全美各地的大学正争相理解生成式人工智能（A.I.）对技术变革的影响。
By Steve Lohr
Steve Lohr has written about the implications of technology for the work force for more than 20 years.
史蒂夫·罗尔报道技术对劳动力的影响已超过20年。
Computer science education will probably focus less on coding and more on computational thinking and A.I. literacy, said Mary Lou Maher, a director of the Computing Research Association.
计算研究协会（Computing Research Association）的一位主任玛丽·卢·马厄（Mary Lou Maher）表示，计算机科学教育可能会减少对编程的重视，而更多地关注计算思维和人工智能素养。
卡内基梅隆大学（Carnegie Mellon University）作为全美顶尖的计算机科学院校之一，实至名归。其毕业生进入全球大型科技公司、初创企业和研究实验室工作。
然而，尽管过去取得了巨大成功，该系的教职员工计划在今年夏天进行一次务虚会，重新思考学校应该教授什么内容，以适应生成式人工智能（A.I.）的快速发展。
该大学本科生项目的教授兼副院长托马斯·科尔蒂纳（Thomas Cortina）表示，这项技术“确实撼动了计算机科学教育”。
计算机科学，比任何其他研究领域都更受生成式人工智能的挑战。
ChatGPT等聊天机器人背后的人工智能技术，能够以类人的流畅度撰写文章和回答问题，正在整个学术界取得进展。但人工智能正以最快、最猛烈的势头冲击着强调编写代码（计算机语言）的计算机科学领域。
大型科技公司和初创企业已经推出了可以生成代码的人工智能助手，并且其能力正在迅速提升。今年1月，Meta的首席执行官马克·扎克伯格（Mark Zuckerberg）预测，人工智能技术将在今年某个时候有效地达到中级软件工程师的水平。
全美各大学的计算机科学项目现在正争相理解这场技术变革的影响，努力应对在人工智能时代应该继续教授什么内容。随着教育工作者思考在人工智能经济中未来的技术工作将是什么样子，各种想法层出不穷，从减少对掌握编程语言的强调，到设计旨在将计算融入各行各业的混合课程。
“我们看到的是人工智能海啸的冰山一角，”哥伦比亚大学研究执行副校长、计算机科学教授珍妮特·温（Jeannette Wing）说。
加剧这种紧迫感的是近年来趋紧的科技就业市场。计算机科学毕业生发现，曾经充裕的工作机会现在常常变得稀缺。科技公司已经在编码的某些方面更多地依赖人工智能，淘汰了一些初级工作。
一些教育工作者现在认为，该学科可以拓宽范围，变得更像一门文科（liberal arts）学位，更加注重批判性思维和沟通技巧。
美国国家科学基金会（National Science Foundation）正在资助一个名为“升级人工智能”（Level Up AI）的项目，旨在召集大学和社区大学的教育工作者及研究人员，就人工智能教育的核心要素达成共同愿景。这个为期18个月的项目由非营利性研究与教育组织计算研究协会与新墨西哥州立大学（New Mexico State University）合作运营，正在组织会议和圆桌讨论，并发布白皮书以共享资源和最佳实践。
获得国家科学基金会支持的这项倡议之所以成立，是因为“我们迫切需要更多学习计算的学生——以及更多了解人工智能的劳动力”，计算研究协会的计算机科学家兼主任玛丽·卢·马厄说。
马厄博士表示，计算机科学教育的未来可能会减少对编程的关注，而更多地关注计算思维和人工智能素养。计算思维涉及将问题分解为更小的任务，开发分步解决方案，并使用数据得出基于证据的结论。
人工智能素养是指对人工智能如何运作、如何负责任地使用它以及它如何影响社会的理解——对于不同水平的学生，理解的深度不同。她说，培养明智的怀疑态度应该成为一个目标。
在卡内基梅隆大学，教职员工为聚会做准备之际，科尔蒂纳博士表示，他个人的观点是，课程应包括传统计算机基础和人工智能原理的教学，然后辅以大量使用新工具设计软件的实践经验。
“我们认为这就是未来的方向，”他说。“但我们是否需要对课程进行更深刻的变革？”
目前，由个别计算机科学教授自行决定是否允许学生使用人工智能。去年，卡内基梅隆大学批准在入门课程中使用人工智能。科尔蒂纳博士说，最初，许多学生将人工智能视为快速完成涉及编写程序的作业的“万能钥匙”。
“但他们对自己写的代码一半都不理解，”他说，这导致许多人意识到自己掌握编写和调试代码能力的价值。“学生们正在重新调整（自己的认知）。”
许多接受新人工智能工具的计算机科学学生确实如此，尽管他们有所保留。他们说，他们使用人工智能来构建初始原型程序、检查代码错误以及作为回答问题的数字导师。但他们不愿意过度依赖它，担心这会削弱他们的计算能力。
许多学生表示，他们会投出100到200份申请来争取暑期实习和第一份工作。明年秋天将成为北卡罗来纳大学夏洛特分校（University of North Carolina at Charlotte）大四学生的康纳·德雷克（Connor Drake）觉得自己很幸运，在提交了仅30份申请后就获得了面试机会。今年夏天，他获得了夏洛特市大型公用事业公司杜克能源（Duke Energy）的网络安全实习生职位。
“计算机科学学位曾经是通往就业‘应许之地’的金色门票——但现在情况不同了，”北卡罗来纳大学夏洛特分校计算机科学学位项目的大四学生康纳·德雷克说。
杰西·巴伯（Jesse Barber）为《纽约时报》摄影
“计算机科学学位曾经是通往就业‘应许之地’的金色门票，”22岁的德雷克先生说。“但现在情况不同了。”
德雷克先生个人的“人工智能防御策略”是扩展自己的技能组合。除了主修计算机科学外，他还辅修了政治学，专攻安全与情报研究——这个领域非常可能应用他的网络安全专业知识。他担任大学网络安全俱乐部的主席，并在学生会任职。
像其他计算机科学学生一样，德雷克先生被迫适应日益严峻的科技就业市场。劳工专家表示，有几个因素在起作用。特别是大型科技公司，在过去几年里抑制了招聘，这与其在疫情爆发初期的繁荣年份相比是急剧收缩。例外情况是对相对少数最抢手的人工智能专家的疯狂招募，他们获得了丰厚的薪酬待遇。
但大多数技术工作者并不在科技公司工作。总体而言，直到最近，从事科技职业的工人就业率基本保持稳定——但根据政府统计数据，自2月份以来下降了6%。
雇主们通过大幅减少科技职位招聘发出了更明确的信号。技术研究与教育组织CompTIA的分析显示，过去三年，寻求拥有两年或以下经验员工的公司发布的职位数量下降了65%。招聘所有经验水平技术工作者的职位数量下降了58%。
“我们主要看到的是疫情后招聘热潮的消退以及当前经济不确定性的影响，”CompTIA的首席研究官蒂姆·赫伯特（Tim Herbert）说。“我们还没有真正看到明确的人工智能效应。”
专家们表示，尽管计算机科学教育的未来道路可能充满不确定性，但人工智能辅助软件市场有望增长。人工智能是一种生产力工具，而每一波新的计算浪潮——个人电脑、互联网、智能手机——都增加了对软件和程序员的需求。
他们说，这一次，结果可能是技术民主化的一次爆发，因为从医学到市场营销等各个领域的人们都将使用类似聊天机器人的工具，利用特定行业的数据集，创建适合其行业的定制程序。
“软件工程职位的增长可能会放缓，但参与编程的总人数将会增加，”斯坦福大学计算机科学教授亚历克斯·艾肯（Alex Aiken）说。◾
Carnegie Mellon University has a well-earned reputation as one of the nation’s top schools for computer science. Its graduates go on to work at big tech companies, start-ups and research labs worldwide.
Still, for all its past success, the department’s faculty is planning a retreat this summer to rethink what the school should be teaching to adapt to the rapid advancement of generative artificial intelligence.
The technology has “really shaken computer science education,” said Thomas Cortina, a professor and an associate dean for the university’s undergraduate programs.
computer science, more than any other field of study, is being challenged by generative A.I.
The A.I. technology behind chatbots like ChatGPT, which can write essays and answer questions with humanlike fluency, is making inroads across academia. But A.I. is coming fastest and most forcefully to computer science, which emphasizes writing code, the language of computers.
Big tech companies and start-ups have introduced A.I. assistants that can generate code and are rapidly becoming more capable. And in January, Mark Zuckerberg, Meta’s chief executive, predicted that A.I. technology would effectively match the performance of a midlevel software engineer sometime this year.
Computer science programs at universities across the country are now scrambling to understand the implications of the technological transformation, grappling with what to keep teaching in the A.I. era. Ideas range from less emphasis on mastering programming languages to focusing on hybrid courses designed to inject computing into every profession, as educators ponder what the tech jobs of the future will look like in an A.I. economy.
“We’re seeing the tip of the A.I. tsunami,” said Jeannette Wing, a computer science professor who is executive vice president of research at Columbia University.
Heightening the sense of urgency is a tech job market that has tightenedin recent years. Computer science graduates are finding that job offers, once plentiful, are often scarce. Tech companies are already relying more on A.I. for some aspects of coding, eliminating some entry-level work.
Some educators now believe the discipline could broaden to become more like a liberal arts degree, with a greater emphasis on critical thinking and communication skills.
The National Science Foundation is funding a program, Level Up AI, to bring together university and community college educators and researchers to move toward a shared vision of the essentials of A.I. education. The 18-month project, run by the Computing Research Association, a research and education nonprofit, in partnership with New Mexico State University, is organizing conferences and round tables and producing white papers to share resources and best practices.
The N.S.F.-backed initiative was created because of “a sense of urgency that we need a lot more computing students — and more people — who know about A.I. in the work force,” said Mary Lou Maher, a computer scientist and a director of the Computing Research Association.
The future of computer science education, Dr. Maher said, is likely to focus less on coding and more on computational thinking and A.I. literacy. Computational thinking involves breaking down problems into smaller tasks, developing step-by-step solutions and using data to reach evidence-based conclusions.
A.I. literacy is an understanding — at varying depths for students at different levels — of how A.I. works, how to use it responsibly and how it is affecting society. Nurturing informed skepticism, she said, should be a goal.
At Carnegie Mellon, as faculty members prepare for their gathering, Dr. Cortina said his own view was that the coursework should include instruction in the traditional basics of computing and A.I. principles, followed by plenty of hands-on experience designing software using the new tools.
“We think that’s where it’s going,” he said. “But do we need a more profound change in the curriculum?”
Currently, individual computer science professors choose whether to allow students to use A.I. Last year, Carnegie Mellon endorsed using A.I. for introductory courses. Initially, Dr. Cortina said, many of the students regarded A.I. as a “magic bullet” to quickly complete homework assignments, which involved writing programs.
“But they didn’t understand half of what the code was,” he said, leading many to realize the value of knowing how to write and debug code themselves. “The students are resetting.”
That’s true for many computer science students embracing the new A.I. tools, with some reservations. They say they use A.I for building initial prototype programs, for checking for errors in code and as a digital tutor to answer questions. But they are reluctant to rely on it too much, fearing it dulls their computing acumen.
Many students say they send out 100 to 200 applications for summer internships and first jobs. Connor Drake, who will be a senior next fall at the University of North Carolina at Charlotte, counts himself lucky, having scored an interview after submitting only 30 applications. He was offered a job as a cybersecurity intern this summer for Duke Energy, a large utility company, in Charlotte.
“A computer science degree used to be a golden ticket to the promised land of jobs,” Mr. Drake, 22, said. “That’s no longer the case.”
Mr. Drake’s personal A.I.-defense strategy is to expand his skill set. In addition to his computer science major, he has minored in political science with a specialty in security and intelligence studies — a field where his expertise in cybersecurity could well be applied. He is president of a university cybersecurity club and has served in student government.
Mr. Drake, like other computer science students, has been forced to adjust to an increasingly tough tech job market. Several factors, labor experts say, are at work. Big tech companies, in particular, have curbed their hiring for the past few years, a sharp pullback from the pandemic-era boom years. The exception is the hectic recruiting of a relatively small number of the most coveted A.I. experts, who are being offered lucrative pay packages.
But most technology workers do not work for tech companies. Overall employment for workers in tech occupations had generally held up until recently — down 6 percent since February, according to government statistics.
Employers have sent a sharper signal with a significant pullback in tech job listings. In the past three years, there has been a 65 percent drop from companies seeking workers with two years of experience or less, according to an analysis by CompTIA, a technology research and education organization. The decline in listings for tech workers with all levels of experience is down 58 percent.
“We’re mainly seeing a postpandemic unwinding of hiring and the impact of the current economic uncertainty,” said Tim Herbert, chief research officer at CompTIA. “We don’t really have a clear A.I. effect yet.”
While the road ahead for computer science education may be uncertain, the market for A.I.-assisted software is poised for growth, experts say. A.I. is a productivity tool, and every new wave of computing — the personal computer, the internet, the smartphone — has increased the demand for software and for programmers.
This time, they say, the result may be a burst of technology democratization as chatbot-style tools are used by people in fields from medicine to marketing to create their own programs, tailored for their industry, fed by industry-specific data sets.
“The growth in software engineering jobs may decline, but the total number of people involved in programming will increase,” said Alex Aiken, a professor of computer science at Stanford.
来源：左右图史
