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Boston Globe
4 days ago
- Business
- Boston Globe
How do you teach computer science in the AI era?
Computer science, more than any other field of study, is being challenged by generative AI. The AI technology behind chatbots such as ChatGPT, which can write essays and answer questions with humanlike fluency, is making inroads across academia. But AI is coming fastest and most forcefully to computer science, which emphasizes writing code, the language of computers. Advertisement Big tech companies and startups have introduced AI assistants that can generate code and are rapidly becoming more capable. And in January, Mark Zuckerberg, Meta's CEO, predicted that AI 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 AI 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 AI economy. 'We're seeing the tip of the AI tsunami,' said Jeannette Wing, a computer science professor and executive vice president of research at Columbia University. Advertisement Heightening the sense of urgency is a tech job market that has tightened in recent years. Computer science graduates are finding that job offers, once plentiful, are often scarce. Tech companies are relying more on AI 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 AI 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 roundtables and producing white papers to share resources and best practices. The NSF-backed initiative was created because of 'a sense of urgency that we need a lot more computing students -- and more people -- who know about AI in the workforce,' said Mary Lou Maher, a computer scientist and a director of the Computing Research Association. The future of computer science education, Maher said, is likely to focus less on coding and more on computational thinking and AI literacy. Computational thinking involves breaking down problems into smaller tasks, developing step-by-step solutions and using data to reach evidence-based conclusions. AI literacy is an understanding -- at varying depths for students at different levels -- of how AI works, how to use it responsibly and how it is affecting society. Nurturing informed skepticism, she said, should be a goal. Advertisement At Carnegie Mellon, as faculty members prepare for their gathering, Cortina said his view was that the coursework should include instruction in the traditional basics of computing and AI 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 AI. Last year, Carnegie Mellon endorsed using AI for introductory courses. Initially, Cortina said, many students regarded AI as a 'magic bullet' to quickly complete homework assignments that 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 AI tools, with some reservations. They say they use AI 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,' Drake, 22, said. 'That's no longer the case.' Advertisement Drake's personal AI-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. 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 AI 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 AI effect yet.' While the road ahead for computer science education may be uncertain, the market for AI-assisted software is poised for growth, experts say. AI 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. Advertisement 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 University. This article originally appeared in .
The Star
6 days ago
- Business
- The Star
How do you teach computer science in the AI era?
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, startups 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 of the university's undergraduate programs. Computer science, more than any other field of study, is being challenged by generative AI. The AI technology behind chatbots such as ChatGPT, which can write essays and answer questions with humanlike fluency, is making inroads across academia. But AI is coming fastest and most forcefully to computer science, which emphasises writing code, the language of computers. Big tech companies and startups have introduced AI assistants that can generate code and are rapidly becoming more capable. And in January, Mark Zuckerberg, Meta's CEO, predicted that AI 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 AI 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 AI economy. 'We're seeing the tip of the AI tsunami,' said Jeannette Wing, a computer science professor and executive vice president of research at Columbia University. Heightening the sense of urgency is a tech job market that has tightened in recent years. Computer science graduates are finding that job offers, once plentiful, are often scarce. Tech companies are relying more on AI 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 AI education. The 18-month project, run by the Computing Research Association, a research and education nonprofit, in partnership with New Mexico State University, is organising conferences and roundtables and producing white papers to share resources and best practices. The NSF-backed initiative was created because of 'a sense of urgency that we need a lot more computing students – and more people – who know about AI in the workforce,' said Mary Lou Maher, a computer scientist and a director of the Computing Research Association. The future of computer science education, Maher said, is likely to focus less on coding and more on computational thinking and AI literacy. Computational thinking involves breaking down problems into smaller tasks, developing step-by-step solutions and using data to reach evidence-based conclusions. AI literacy is an understanding – at varying depths for students at different levels – of how AI 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, Cortina said his view was that the coursework should include instruction in the traditional basics of computing and AI 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 AI. Last year, Carnegie Mellon endorsed using AI for introductory courses. Initially, Cortina said, many students regarded AI as a 'magic bullet' to quickly complete homework assignments that 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 AI tools, with some reservations. They say they use AI 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,' Drake, 22, said. 'That's no longer the case.' Drake's personal AI-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. 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 AI 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% 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% 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%. '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 AI effect yet.' While the road ahead for computer science education may be uncertain, the market for AI-assisted software is poised for growth, experts say. AI 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 University. – ©2025 The New York Times Company This article originally appeared in The New York Times.
Miami Herald
6 days ago
- Business
- Miami Herald
How Do You Teach Computer Science in the AI Era?
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, startups 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 of the university's undergraduate programs. Computer science, more than any other field of study, is being challenged by generative AI. The AI technology behind chatbots such as ChatGPT, which can write essays and answer questions with humanlike fluency, is making inroads across academia. But AI is coming fastest and most forcefully to computer science, which emphasizes writing code, the language of computers. Big tech companies and startups have introduced AI assistants that can generate code and are rapidly becoming more capable. And in January, Mark Zuckerberg, Meta's CEO, predicted that AI 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 AI 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 AI economy. 'We're seeing the tip of the AI tsunami,' said Jeannette Wing, a computer science professor and executive vice president of research at Columbia University. Heightening the sense of urgency is a tech job market that has tightened in recent years. Computer science graduates are finding that job offers, once plentiful, are often scarce. Tech companies are relying more on AI 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 AI 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 roundtables and producing white papers to share resources and best practices. The NSF-backed initiative was created because of 'a sense of urgency that we need a lot more computing students -- and more people -- who know about AI in the workforce,' said Mary Lou Maher, a computer scientist and a director of the Computing Research Association. The future of computer science education, Maher said, is likely to focus less on coding and more on computational thinking and AI literacy. Computational thinking involves breaking down problems into smaller tasks, developing step-by-step solutions and using data to reach evidence-based conclusions. AI literacy is an understanding -- at varying depths for students at different levels -- of how AI 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, Cortina said his view was that the coursework should include instruction in the traditional basics of computing and AI 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 AI. Last year, Carnegie Mellon endorsed using AI for introductory courses. Initially, Cortina said, many students regarded AI as a 'magic bullet' to quickly complete homework assignments that 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 AI tools, with some reservations. They say they use AI 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,' Drake, 22, said. 'That's no longer the case.' Drake's personal AI-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. 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 AI 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% 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% 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%. '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 AI effect yet.' While the road ahead for computer science education may be uncertain, the market for AI-assisted software is poised for growth, experts say. AI 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 University. This article originally appeared in The New York Times. Copyright 2025
