arXiv AI By Prerna Ravi, Car\'umey Stevens, Ben Hurt, Brandon Hanks, Grace Lin, Emma Anderson

Exploring How Agent Voice Accents Shape Human-AI Collaboration in K-12 Group Learning

Read the original on arXiv AI →

arXiv:2606. 12805v1 Announce Type: cross Abstract: Collaboration is widely recognized as a cornerstone of 21st-century education, yet teachers still encounter persistent challenges in fostering productive peer interaction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language
Sep 3

AI agents reshape consensus formation in human groups

The study investigates how large language model (LLM) agents influence consensus formation in mixed human‑AI groups during a collaborative description game. Three regimes emerge: low agent proportions lead to human‑led consensus, intermediate proportions disrupt convergence, and high proportions produce strong, agent‑led consensus. The resulting consensus differs in semantic grounding and communicative form, with human‑led consensus being concrete and holistic, and agent‑led consensus being abstract and geometrically segmented.

By Lin Chen, Ziyi Liu, Xia Hu, Yong Li
arXiv AI
Sep 21

Talking Past the Machine: Morality, Politeness, and Alignment in Human-AI Dialogue

The paper examines how conversational AI, specifically ChatGPT, displays aspects of cooperative dialogue such as morality, politeness, and alignment compared to human-human conversations. Using over 26,000 multi‑turn dialogues and mixed‑effects modeling, the authors find that AI mimics the surface features of cooperation—like warmth and hedging—yet lacks the underlying social architecture that drives mutual adaptation. Key findings include a dissociation between AI’s moral output and human negotiation, a decline in linguistic convergence, and a reversal of typical human accommodation mechanisms when interacting with AI.

By Marina Mitiaeva, Lu Xiao