Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Course

TL;DR

This study investigates how social factors shape undergraduate students' use of large language models (LLMs) in a Python programming course, using SST theory to move beyond technological determinism. It employs a mixed-methods design with an anonymous survey (n=158), 10 interviews, and regression analyses of midterm performance, revealing that career expectations and perceived peer use strongly influence LLM adoption, while early use relates to lower self-efficacy and poorer performance on Midterm #1. The findings highlight the complex interplay between social context and learning outcomes, suggesting careful curricular guidance and attention to student agency in integrating LLMs into programming education. By applying the technology appropriation model, the work offers nuanced insights into how students appropriate AI tools in learning and how those practices may evolve over time and across contexts.

Abstract

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.

Figures (3)

• Figure 1: The technology appropriation conceptual model (diagram modified and annotated for clarity from carroll2002just).
• Figure 2: Ratio of students citing LLM usage per each homework assignment/project.
• Figure 3: Factors of the Technology Appropriation Model (adapted from carroll2002just).