338 citations · 494 across the 10 of their papers we have counts for
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Personalized Parsons Puzzles as Scaffolding Enhance Practice Engagement Over Just Showing LLM-Powered Solutions
Xinying Hou, Zihan Wu, Xu Wang +1
As generative AI products could generate code and assist students with programming learning seamlessly, integrating AI into programming education contexts has driven much attention…
Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Course
Aadarsh Padiyath, Xinying Hou, Amy Pang +6
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 an…
How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment
Majeed Kazemitabaar, Xinying Hou, Austin Henley +3
As Large Language Models (LLMs) gain in popularity, it is important to understand how novice programmers use them. We present a thematic analysis of 33 learners, aged 10-17, indepe…
Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming
Majeed Kazemitabaar, Justin Chow, Carl Ka To Ma +3
AI code generators like OpenAI Codex have the potential to assist novice programmers by generating code from natural language descriptions, however, over-reliance might negatively…