arXiv AI
Investigating the Impacts of Generative AI on Information Seeking
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arXiv AI
Sep 24
Safety Nudges: User-Facing Interventions for Real-Time AI Risk Awareness
The paper introduces Safety Nudges, a browser-based tool that displays lightweight, in situ flags when a conversational AI exhibits risky behavior such as hallucination or overconfidence. In a two‑week field study with 45 frequent chatbot users, participants reported that the nudges were useful, clear, and minimally disruptive, and most felt more aware of potential AI harms. However, increased awareness did not automatically translate into measurable changes in user behavior, underscoring the need for relevance, calibration, and user control in nudge design.
arXiv AI
Sep 15
More Than Just Access: Generative AI as Communication Intermediary for Blind and Low-Vision Users
arXiv:2609.15696v1 Announce Type: cross Abstract: Generative AI (GenAI) tools are increasingly woven into how blind and low-vision (BLV) people communicate, not only with digital information, but wit...
arXiv AI
Aug 13
Toward Meaningful Transparency for AI Chatbots: Disclosing Persuasive Intent Reduces Persuasion
arXiv:2608. 11794v1 Announce Type: cross Abstract: The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content provenance and AI involvement.
arXiv AI
Aug 12
The GenAI Catch-22: Use of Generative Artificial Intelligence in Norwegian Newsrooms During the 2025 Parliamentary Election
arXiv:2608. 10773v1 Announce Type: cross Abstract: The increasing use of Generative Artificial Intelligence (GenAI) in journalism raises concerns about possible detrimental effects both on journalism and its democratic function.
arXiv AI
Oct 27
The Impact of Personalized AI Response Suggestions on Content Creators' Direct Messaging
arXiv:2610.34770v1 Announce Type: cross Abstract: Direct messaging is often employed by social media content creators to directly interact with followers. Those creators attempt to engage with follow...
arXiv AI
Sep 24
How Children Design and Reason about Trustworthy AI Chatbots
The study explores how children design AI chatbots and what they consider trustworthy. Using a custom chatbot-building environment, 115 learners aged 8‑18 created 119 chatbots and adjusted traits such as confidence, transparency, and formality. Findings show younger children equate trust with purpose‑fulfillment, while older children focus on transparent, calibrated design, and that students calibrate academic chatbots to be more formal and transparent than hobby ones.