61 citations · 121 across the 15 of their papers we have counts for
9 papers · 1 filter
Impact of Expertise and Trust on AI-assisted Work
Qianou Ma, Pranav Khape, Rock Pang +4
Artificial intelligence (AI) agents promise to extend what users can accomplish beyond their existing expertise, but they also raise the risk of producing plausible-looking work th…
The Work Behind Delegation: A Framework for Supervising AI Coding Agents
Yeon Su Park, Nadia Arvi, Hae Ri Lee +3
As AI coding agents carry out development tasks with greater autonomy, developers are shifting from direct implementation toward supervising delegated work. Yet existing research o…
Demonstrably Informed Consent in Privacy Policy Flows: Evidence from a Randomized Experiment
Qian Ma, Aditya Majumdar, Sarah Rajtmajer +1
Privacy policies govern how personal data is collected, used, and shared. Yet, in most privacy-policy consent flows, agreement is operationalized as a single click at the end of a…
Not Everyone Wins with LLMs: Behavioral Patterns and Pedagogical Implications for AI Literacy in Programmatic Data Science
Qianou Ma, Kenneth Koedinger, Tongshuang Wu
LLMs promise to democratize technical work in complex domains like programmatic data analysis, but not everyone benefits equally. We study how students with varied experiences use…
"GenAI Defaults to Bias!" Gamify AI Literacy Through Reflections on Prompts
Qianou Ma, Megan Chai, Yike Tan +5
As Generative AI (GenAI) becomes widespread, it is increasingly important for the public to understand the model's behaviors and biases. However, existing AI literacy efforts miss…
SPHERE: An Evaluation Card for Human-AI Systems
Qianou Ma, Dora Zhao, Xinran Zhao +6
In the era of Large Language Models (LLMs), establishing effective evaluation methods and standards for diverse human-AI interaction systems is increasingly challenging. To encoura…
What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use
Qianou Ma, Weirui Peng, Chenyang Yang +3
Prompting LLMs for complex tasks (e.g., building a trip advisor chatbot) needs humans to clearly articulate customized requirements (e.g., "start the response with a tl;dr"). Howev…
How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging
Qianou Ma, Hua Shen, Kenneth Koedinger +1
Large Language Models (LLMs) now excel at generative skills and can create content at impeccable speeds. However, they are imperfect and still make various mistakes. In a Computer…
Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming
Qianou Ma, Tongshuang Wu, Kenneth Koedinger
The emergence of large-language models (LLMs) that excel at code generation and commercial products such as GitHub's Copilot has sparked interest in human-AI pair programming (refe…