12 papers
(Im)Paired Programming: Coding Agents Improve Productivity but Harm Understanding
Nishant Balepur, Connor Baumler, Valerie Chen +3
The study finds that AI coding assistants help students finish a programming task faster but reduce their understanding of the code, making it harder for them to extend it later.
It Matters How You Say It: Exploring Rhetorical Patterns for AI-Assisted Information Evaluation
Sadra Sabouri, Zeinabsadat Saghi, Jordan Lee Boyd-Graber +3
Prior work on AI-assisted information evaluation has largely focused on what AI systems communicate, comparing explanation types and formats, with responses predominantly cast in d…
Measuring User's Mental Models of Speech Translation in Human-AI Collaboration
HyoJung Han, Nishant Balepur, Jordan Boyd-Graber +1
Millions of people use machine translation (MT) tools daily, yet little is known about their perception of what systems can and cannot do. This paper studies users' mental models o…
AI, Take the Wheel: What Drives Delegation and Trust in Human-Computer Cooperative Question Answering?
Maharshi Gor, Yoo Yeon Sung, Yu Hou +4
AI systems are fallible, and humans can make mistakes in deciding whether to trust AI over their own judgment. Thus, improving human-AI collaboration requires understanding when, w…
DRACULA: Hunting for the Actions Users Want Deep Research Agents to Execute
Nishant Balepur, Malachi Hamada, Varsha Kishore +9
Scientific Deep Research (DR) agents answer user queries by synthesizing research papers into multi-section reports. User feedback can improve their utility, but existing protocols…
Language Models Don't Know What You Want: Evaluating Personalization in Deep Research Needs Real Users
Nishant Balepur, Malachi Hamada, Varsha Kishore +7
Deep Research (DR) systems help researchers cope with ballooning publishing counts. Such tools synthesize scientific papers to answer research queries, but lack understanding of th…