The paper introduces STORYLENSWRITER, a novel framework that significantly improves personalized story rewriting by incorporating context-aware narrative enrichment, outperforming style-only adaptatio…

Analyzing longitudinal data from 12,000 Copilot users, the paper finds that individual user habits regarding LLM interaction are highly sticky and difficult to change, and that existing datasets may o…

This paper identifies and categorizes digital security and privacy questions asked to large language models using a dataset of user-LLM conversations, and evaluates the response quality and consistenc…

This paper analyzes the shift in student adoption and accountability practices of GenAI tools in a graduate-level HCI prototyping course across three cohorts using GitHub data.

The paper introduces PersTurnBench, a novel benchmark and evaluator for assessing personalized user conversation satisfaction at specific turns, addressing the limitation of generic response quality m…

This paper introduces IntentTune, a framework for inferring user intent from under-specified queries in e-commerce search using user-specific behavioral signals and population-level demand patterns.

The paper introduces RHELM, a new benchmark designed to test LLMs' long-term memory by simulating realistic, complex, and evolving dialogues that integrate multiple heterogeneous data sources.

The paper develops an AI-augmented UX Research Point-of-View (PoV) framework to guide the design of digital wellbeing tools for high-stress Emergency and Public Safety Personnel (EPSP), finding that s…

The paper introduces a Generative AI-augmented User Experience Research (UXR) methodology, operationalized through a four-stage process, to create actionable, stigma-aware design guidance for digital…

This paper conducted a randomized controlled trial on Reddit to test the effectiveness of various deescalation strategies in reducing personal insults using automated replies.

The paper introduces DigitalCoach, a dataset of human expert-novice computer use coaching sessions, and evaluates the ability of state-of-the-art models to teach humans how to use computers.

The authors propose a new solution for the content cold-start problem in industry-scale search and recommender systems, reducing bias, improving model prediction, and validating long-term impact.

This paper presents a human-in-the-loop framework for providing personalized recourse in machine learning, using iterative Bayesian inference for causal model approximation.

This paper identifies and validates eight core UX principles for human-AI agent interaction in the workplace using a multi-method approach.

This paper designs and evaluates Fabula, an interactive app for fiction writers using detailed narrative plans and participatory AI.

The paper introduces StreamSynth, a sequential setting for synthetic data generation, and proposes SynLearner, a framework that enables LLMs to improve synthesis performance by accumulating and transf…

This paper proposes a multi-agent framework using LLMs to improve collaborative story generation, demonstrating that an iterative Writer-Editor process significantly enhances narrative quality for you…

Phoenix is a multi-agent system that uses seven safety controls and a test evaluation strategy to resolve GitHub issues, achieving 75% oracle-resolution with no regressions on a curated benchmark and…