Artificial intelligence models are increasingly writing their own code, leading to speculation about shifts in the computer science job market.<\/p>\n
Some engineers at Anthropic and OpenAI<\/a> recently said they are using AI to write 100% of their code, causing some to predict changes<\/a> to the computer science job market. Pitt professors in the computer science field are considering the effects of this shift, adapting their curriculum and emphasizing the value of an education in computer science.\u00a0<\/p>\n Adriana Kovashka, associate professor and chair of the department of computer science<\/a> in the School of Computing Information<\/a>, said the department is working on creating an official AI policy as well as new courses that highlight professional uses of AI.\u00a0<\/p>\n \u201cWe will be introducing courses that involve topics such as data science or programming plus a little bit of AI,\u201d she said. \u201cYou can think of this as being like a vibe coding<\/a> course where you don\u2019t have to understand as much about programming, but just kind of feel your way through the AI outputs or have the AI model produce it for you based on some specification of a problem.\u201d<\/p>\n The course CS 1699 Special Topics in Computer Science<\/a> allows the department to test out a course like the one Kovashka mentioned before permanently deciding to add it to the curriculum.<\/p>\n \u201cWe\u2019re trying to keep these courses agile as much as possible,\u201d Kovashka said. \u201cBut it is tricky, because course design takes a while.\u201d<\/p>\n Kovashka said she believes students sometimes think they can \u201coffload the thinking part to AI,\u201d but it\u2019s necessary to understand the AI output and be able to fact-check it.\u00a0<\/p>\n \u201cI think it would be cool if we could decouple teaching students a program and teaching students to think. For example, we can teach a course that uses no programming but still uses logic,\u201d Kovashka said. \u201cOne of the values of the CS education is that you obviously need to know how something works, how something should work, to be able to see when it doesn\u2019t work well.\u201d<\/p>\n Morgan Frank, an assistant professor in the Department of Informatics and Networked Systems<\/a> in SCI, said he believes teaching the fundamentals of coding is still a valuable practice.<\/p>\n \u201cThe effective use of these tools requires that you have some good contextual knowledge,\u201d Frank said. \u201cSo when the AI does a poor job, you can catch it and you can be very critical of the results that it gives you and ask what happened to help identify how to do better.\u201d<\/p>\n According to Frank, the effects of AI tools like Claude Code<\/a> are yet to be seen in research.<\/p>\n \u201cBut I think there\u2019s a lot of room for people who are good thinkers and creative and asking important questions to be made more powerful by these tools,\u201d Frank said.<\/p>\n Frank said he believes AI can complete the more tedious aspects of scientific research, like cleaning data sets, running statistical analyses and summarizing results.<\/p>\n \u201cIt\u2019s really bad at coming up with interesting questions or ideas, and it\u2019s really bad at taking results and using them to update the hypothesis,\u201d Frank said.<\/p>\n Patrick Skeba, a teaching assistant professor in the CS department in SCI, is teaching his students how to use AI creatively and effectively.\u00a0<\/p>\n \u201cWhat I tell a lot of students is that you need to start thinking about what it is you want to get into,\u201d Skeba said. \u201cDo you want to study environmental science? Do you want to study public health? Do you want to study computers themselves? Then think about how machine learning can help you with that.\u201d<\/p>\n Skeba said there are many applications for AI, but CS students need to know when and how to use them.<\/p>\n \u201cThere\u2019s more of a need for people to come up with creative applications and experiments than pioneer the algorithms themselves, although that\u2019s still taking place,\u201d Skeba said.<\/p>\n Todd Underwood, a former lead at Anthropic<\/a> and OpenAI<\/a> and former co-site leader at Google\u2019s AI headquarters<\/a> in Pittsburgh, sees computer science jobs changing, but not necessarily being replaced. Underwood explained that people with computing backgrounds will need to oversee the AI\u2019s code from start to finish, prompting and fact-checking its work, which requires a basic understanding of coding.<\/p>\n \u201cThe general principles of computing \u2026 are relevant no matter what happens,\u201d Underwood said. \u201cBut there will be more managers of AI agents than they will be themselves the authors of software.\u201d<\/p>\n Underwood said he believes universities should focus more on promoting broad principles of computer science rather than solely placing emphasis on coding, believing it\u2019s difficult to know what will be relevant in the technology field in a year.<\/p>\n \u201c[Universities should] de-emphasize mastery of personal skill in writing a lot of software, increase the amount of principles based in broad education and principles of computer science, principles of software and systems architecture,\u201d Underwood said. \u201cI think that would set students up for more success.\u201d<\/p>\n Katie Whiteford, a junior computer science major, said her professors discourage the use of AI when completing coding assignments. But for longer assignments, Whiteford said AI is a time-saver.<\/p>\n \u201cThe hardest thing is, I don\u2019t want to use AI because I want to learn. That\u2019s my goal,\u201d Whiteford said. \u201cIt makes me feel bad [to use AI], but it is what it is. I need to save time.\u201d<\/p>\n For most of her co-op work, Whiteford said her computer science education was crucial for her understanding.\u00a0<\/p>\n \u201cThe consensus is, yes, AI is smart, but it\u2019s not perfect, so we do need the skills,\u201d she said. \u201cAlso \u2014 if you\u2019re totally relying on AI \u2014\u00a0you\u2019re not really learning, and that\u2019s going to mess you up in the future.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"Artificial intelligence models are increasingly writing their own code, leading to speculation about shifts in the computer science…\n","protected":false},"author":2,"featured_media":146982,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[278,22266,21331,73,75,74],"class_list":["post-146981","post","type-post","status-publish","format-standard","has-post-thumbnail","category-pittsburgh","tag-ai","tag-coding","tag-computer-science","tag-pittsburgh","tag-pittsburgh-headlines","tag-pittsburgh-news"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/us-pa\/wp-json\/wp\/v2\/posts\/146981","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/us-pa\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/us-pa\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us-pa\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us-pa\/wp-json\/wp\/v2\/comments?post=146981"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/us-pa\/wp-json\/wp\/v2\/posts\/146981\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us-pa\/wp-json\/wp\/v2\/media\/146982"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/us-pa\/wp-json\/wp\/v2\/media?parent=146981"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us-pa\/wp-json\/wp\/v2\/categories?post=146981"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us-pa\/wp-json\/wp\/v2\/tags?post=146981"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}