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20242026
most citedAssistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support

30 citations · 43 across the 21 of their papers we have counts for

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Showing 2026Show all

9 papers · 1 filter

cs.RO2026

From Small Talk to Rapport: Exploring Robot Self-Disclosure in Collaborative Tasks

Kaitlynn Taylor Pineda, Anvii Mishra, Brian Chien +4

People naturally chat while collaborating and share personal information (i.e., self-disclose) to build rapport and maintain social connections. As robots are increasingly develope…

cs.HC2026

Informal Learning Emerges in Everyday Human-LLM Interaction

Zixin Chen, Haotian Li, Ziang Xiao +2

As LLMs become increasingly capable of completing tasks for users, a central concern is that everyday AI use may become primarily cognitive offloading, eroding the opportunities th…

cs.AI2026

Can We Trust Item Response Theory for AI Evaluation?

Han Jiang, Sunbeom Kwon, Jinwen Luo +2

AI benchmarks increasingly leverage item-level statistical models, particularly item response theory (IRT), to estimate capabilities, rank systems, select informative examples, and…

cs.HC2026

AI-Mediated Negotiation: Design Reflections and Lessons

Veda Duddu, Jash Rajesh Parekh, Andy Mao +4

Conversational AI promises a new kind of preparation for high-stakes workplace negotiations -- personalized, interactive, and capable of simulating realistic resistance. That promi…

cs.CL2026

On Defining Erasure Harms for NLP

Yu Lu Liu, Arnav Goel, Jackie Chi Kit Cheung +3

The deployment of NLP systems has raised concerns about harms they might produce, including representational harms. Recent literature has begun to conceptualize and measure one suc…

cs.AI2026★ 1 cited

AI Evaluation Should Require Standardized Item-Level Data Releases

Han Jiang, Susu Zhang, Dongyao Zhu +6

This position paper argues that standardized item-level benchmark data should become the default infrastructure for AI evaluation. Current evaluations suffer from underspecified it…

cs.CL2026

What Makes a Good Response? An Empirical Analysis of Quality in Qualitative Interviews

Jonathan Ivey, Anjalie Field, Ziang Xiao

Qualitative interviews provide essential insights into human experiences when they elicit high-quality responses. While qualitative and NLP researchers have proposed various measur…

cs.HC2026

Not My Truce: Personality Differences in AI-Mediated Workplace Negotiation

Veda Duddu, Jash Rajesh Parekh, Andy Mao +4

AI-driven conversational coaching is increasingly used to support workplace negotiation, yet prior work assumes uniform effectiveness across users. We challenge this assumption by…

cs.HC2026

Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents

Nikhil Sharma, Zheng Zhang, Daniel Lee +4

High-quality feedback is essential for effective human-AI interaction. It bridges knowledge gaps, corrects digressions, and shapes system behavior; both during interaction and thro…