  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.
