Tag
A pilot study with 24 college students examines how varying levels of LLM access (none, limited, unlimited) affect essay writing quality, behavior, and perceived authorship, finding that constrained access preserves authorship confidence while unlimited access reduces creative expression and ownership.
This paper presents PrivacyAkinator, an interactive tool that helps novice developers articulate privacy design decisions via LLM-generated multiple-choice questions, achieving 47% more key decisions in 73% less time compared to NIST's PRAM methodology.
This paper presents a multimodal emotion recognition module for proactive conversational agents, using facial recognition and linguistic analysis. A user study with 20 participants reveals a 'poker face' effect where visual cues are unreliable, while linguistic analysis proves more accurate; the study also shows agents can elicit emotions through conversational adaptation.
Introduces CoTrace, a framework for goal-level attribution in human-AI collaboration, which analyzes how large language models shape goals by contributing concrete requirements and indirect influences in dialogue turns.
The COWCORPUS project, a study of 4,200 human-AI interactions, found that agents predicting their own failures and intervention moments are more useful than those simply trying to avoid errors. Researchers identified four stable trust patterns in human-AI collaboration and developed the Perfect Timing Score (PTS) to measure intervention prediction accuracy.
Anthropic presents research on how users seek personal guidance from Claude, highlighting findings on sycophancy rates across domains. The study informed the training of Claude Opus 4.7 and Mythos Preview to better protect user wellbeing.