most cited"The teachers are confused as well": A Multiple-Stakeholder Ethics Discussion on Large Language Models in Computing Education

11 citations · 28 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC20244 cited

User Privacy Harms and Risks in Conversational AI: A Proposed Framework

Ece Gumusel, Kyrie Zhixuan Zhou, Madelyn Rose Sanfilippo

This study presents a unique framework that applies and extends Solove (2006)'s taxonomy to address privacy concerns in interactions with text-based AI chatbots. As chatbot prevale…

cs.CY20242 cited

Revitalizing Sex Education for Chinese Children: A Formative Study

Kyrie Zhixuan Zhou, Yilin Zhu, Jingwen Shan +2

Sex education helps children obtain knowledge and awareness of sexuality, and protects them against sexually transmitted diseases, pregnancy, and sexual abuse. Sex education is not…

cs.CY202411 cited

"The teachers are confused as well": A Multiple-Stakeholder Ethics Discussion on Large Language Models in Computing Education

Kyrie Zhixuan Zhou, Zachary Kilhoffer, Madelyn Rose Sanfilippo +5

Large Language Models (LLMs) are advancing quickly and impacting people's lives for better or worse. In higher education, concerns have emerged such as students' misuse of LLMs and…

cs.AI20237 cited

Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie

Kyrie Zhixuan Zhou, Madelyn Rose Sanfilippo

Large language models are quickly gaining momentum, yet are found to demonstrate gender bias in their responses. In this paper, we conducted a content analysis of social media disc…

cs.HC20234 cited

"I'm Not Confident in Debiasing AI Systems Since I Know Too Little": Teaching AI Creators About Gender Bias Through Hands-on Tutorials

Kyrie Zhixuan Zhou, Jiaxun Cao, Xiaowen Yuan +4

Gender bias is rampant in AI systems, causing bad user experience, injustices, and mental harm to women. School curricula fail to educate AI creators on this topic, leaving them un…