  The capability of large language models (LLMs) to generate, debug, and
explain code has sparked the interest of researchers and educators in
undergraduate programming, with many anticipating their transformative
potential in programming education. However, decisions about why and how to use
LLMs in programming education may involve more than just the assessment of an
LLM's technical capabilities. Using the social shaping of technology theory as
a guiding framework, our study explores how students' social perceptions
influence their own LLM usage. We then examine the correlation of self-reported
LLM usage with students' self-efficacy and midterm performances in an
undergraduate programming course. Triangulating data from an anonymous
end-of-course student survey (n = 158), a mid-course self-efficacy survey
(n=158), student interviews (n = 10), self-reported LLM usage on homework, and
midterm performances, we discovered that students' use of LLMs was associated
with their expectations for their future careers and their perceptions of peer
usage. Additionally, early self-reported LLM usage in our context correlated
with lower self-efficacy and lower midterm scores, while students' perceived
over-reliance on LLMs, rather than their usage itself, correlated with
decreased self-efficacy later in the course.
