
What happened
A survey of 763 educators from 49 countries conducted between May and October 2025 found that 64 percent have already changed how they teach programming, and 68 percent have adjusted their testing methods. Common shifts include more in-person proctored exams (56 mentions), oral exams and code defense sessions (36), and project-based assessments (34), moving away from traditional take-home coding assignments.
Why it matters
AI tools like ChatGPT and GitHub Copilot can solve typical introductory programming assignments at the level of an average student, making traditional grading unreliable. Research backs educator concerns: an Anthropic study found developers who used AI to learn a new Python library scored 17 percent lower on follow-up knowledge tests, and a Chinese longitudinal study tracking over 26,000 students showed that while AI use boosted homework grades by 18 percent, closed-book exam scores dropped 20 percent after six months.
What to watch
Nearly half of respondents (48 percent) say the biggest barrier to change is lack of proven best practices for integrating AI into courses; 74 percent want training on effective teaching methods and 66 percent want help redesigning assessments. The ACM task force has set up a website collecting practical examples and resources for educators.
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The survey, conducted by the ACM Task Force on Generative AI and Programming Assessment between May and October 2025, captures a profession in rapid transition. The core problem is structural: traditional assessment—homework assignments and take-home coding problems—has become unreliable as a measure of actual skill when AI tools can produce average-quality solutions automatically. Educators recognize this; 69 percent believe AI has changed the skills needed for software development, and the top two concerns are student dependency on technology (87 percent) and cheating or plagiarism (72 percent).
The response is a measured shift toward assessment methods that are harder to game with AI. Oral exams, code defense sessions, and project-based work require real-time reasoning and explanation—tasks where a student cannot simply submit an AI-generated artifact. This mirrors broader moves away from unproctored homework, which the Berkeley analysis showed spiked with top marks after ChatGPT's release. The trade-off is substantial: educators must invest in new methods and training, and 48 percent lack proven best practices to guide them. Notably, 45 percent of respondents work at institutions without formal AI guidelines, suggesting institutional policy is lagging behind classroom practice.
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