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"ChatGPT Is Here to Help, Not to Replace Anybody" -- An Evaluation of Students' Opinions On Integrating ChatGPT In CS Courses
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Large Language Models (LLMs) like GPT and Bard are capable of producing code based on textual descriptions, with remarkable efficacy. Such technology will have profound implications for computing education, raising concerns about cheating, excessive dependence, and a decline in computational thinking skills, among others. There has been extensive research on how teachers should handle this challenge but it is also important to understand how students feel about this paradigm shift. In this research, 52 first-year CS students were surveyed in order to assess their views on technologies with code-generation capabilities, both from academic and professional perspectives. Our findings indicate that while students generally favor the academic use of GPT, they don't over rely on it, only mildly asking for its help. Although most students benefit from GPT, some struggle to use it effectively, urging the need for specific GPT training. Opinions on GPT's impact on their professional lives vary, but there is a consensus on its importance in academic practice.
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Cited by 2 Pith papers
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Evaluating Code Generation of LLMs in Advanced Computer Science Problems
LLM code generators solve most introductory programming assignments but rarely produce fully correct solutions to advanced CS4 and CS5 assignments, often identifying the algorithm while missing problem-specific constraints.
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Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools
Computing educators are adopting GenAI faster than they are formalizing policies, and both educators and developers see code reading, evaluation, and problem decomposition as rising in importance over syntax recall.
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