30 citations · 37 across the 3 of their papers we have counts for
3 papers
cs.CY2024
Uncovering Name-Based Biases in Large Language Models Through Simulated Trust Game
Yumou Wei, Paulo F. Carvalho, John Stamper
Gender and race inferred from an individual's name are a notable source of stereotypes and biases that subtly influence social interactions. Abundant evidence from human experiment…
cs.HC2024★ 30 cited
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…
cs.CL2023★ 7 cited
Assessing the Quality of Multiple-Choice Questions Using GPT-4 and Rule-Based Methods
Steven Moore, Huy A. Nguyen, Tianying Chen +1
Multiple-choice questions with item-writing flaws can negatively impact student learning and skew analytics. These flaws are often present in student-generated questions, making it…