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Generative AI is a disruptive technology that has the potential to transform many aspects of how computer science is taught. Like previous innovations such as high-level programming languages and block-based programming languages, generative AI lowers the technical expertise necessary to create working programs, bringing the power of computation to more people. The programming process is already changing as a result of its presence, even for expert programmers. It also poses significant challenges to educators around re-thinking assessment as some well-established approaches may no longer be viable. Many traditional programming assignments can be completed using generative AI tools with minimal effort, thus potentially undermining learning. In this Element, the authors explore both the opportunities and the challenges for computer science education resulting from the widespread availability of generative AI.
Information hazing is the use of information to directly and indirectly harass and/or exclude newcomers. This is common in spaces with strong social cohesion where the dominant group is wary of accepting individuals who do vary from the group. The tech industry and its pipeline, computer science education, are two places where the lack of diverse and varied voices has led to numerous social harms. We have collected 30 syllabi from CS1 courses across the US to explore how the courses governing documents, and syllabi, curate the computer science education knowledge commons. Our evaluation highlights areas of policy, research, and student perspectives that are out of alignment both with practice in academia and industry standards. Requirements stemming from the expectation of independent assessment within the academic environment versus the common practice of open information and collaboration appear to clash within the academic integrity policies of many computer science courses. These competing priorities create opportunities for undue harm that create a fertile ground for the spread of misinformation, disinformation, and malinformation. These are usually the unanticipated consequences of policies written in good faith, but still exhibit the toxic, stressful, and isolating impacts of hazing.
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