42 citations · 125 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint Novices
Ruiwei Xiao, Xinying Hou, John Stamper
Recent studies have integrated large language models (LLMs) into diverse educational contexts, including providing adaptive programming hints, a type of feedback focuses on helping…
Integrating Personalized Parsons Problems with Multi-Level Textual Explanations to Scaffold Code Writing
Xinying Hou, Barbara J. Ericson, Xu Wang
Novice programmers need to write basic code as part of the learning process, but they often face difficulties. To assist struggling students, we recently implemented personalized P…
CodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning Programming
Xinying Hou, Zihan Wu, Xu Wang +1
Learning to program can be challenging, and providing high-quality and timely support at scale is hard. Generative AI and its products, like ChatGPT, can create a solution for most…