AutoAnalysis is an automated, self‑updating meta‑analysis that aggregates evidence on human‑AI collaboration. Its latest run includes 1,955 effect sizes from 420 papers, showing that human‑AI combinations outperform humans alone on average but still lag behind the best of either humans or AI alone. The tool continuously incorporates new studies, offering cumulative insights into when and how human‑AI synergy occurs across different agents, tasks, and processes.
By Michelle Vaccaro, Abdullah Almaatouq, Thomas W. Malone
arXiv:2606. 09833v1 Announce Type: cross Abstract: AI agents are reshaping the workspace, leading to drastic change of how humans work.
By Yijia Shao, Zora Zhiruo Wang, Neel Ahuja, Yicheng Wang, Bowen Liu, Diyi Yang
arXiv:2606. 00182v1 Announce Type: cross Abstract: Human-AI collaboration is considered the most promising way to incorporate AI in the workplace.
By Kuntal Ghosh, Marc Hassenzahl, Shadan Sadeghian
arXiv:2608. 00818v2 Announce Type: replace Abstract: The discovery of scaling laws has highlighted the extraordinary potential of AI systems with a striking empirical pattern: as AI systems scale, their capabilities tend to improve predictably.
By Anyan Qi, Mengxin Wang
arXiv:2609.21117v1 Announce Type: cross
Abstract: AI productivity is often measured by task completion time, economic value, or improvements in outcome quality. However, these measures usually treat...
By Saki Imai, Mert \.Inan, Malihe Alikhani
arXiv:2601. 11072v1 Announce Type: cross Abstract: Within journalistic editorial processes, disclosing AI usage is currently limited to simplistic labels, which misses the nuance of how humans and AI collaborated on a news article.
By Amber Kusters, Pooja Prajod, Pablo Cesar, Abdallah El Ali
arXiv:2606. 07489v1 Announce Type: new Abstract: Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute tasks end to end.
By Jeremy Yang, Kate Zyskowski, Noah Yonack, Jerry Ma
arXiv:2607. 27179v1 Announce Type: cross Abstract: Conversational AI is increasingly positioned as a teammate rather than a tool, yet we know little about how its presence reshapes communication among the humans on the team.
By Nia Nixon, Jaeyoon Choi, Pedro Martins De Bastos, Mohammad Amin Samadi, Luise Mehner, Seehee Park, Spencer JaQuay
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:2607. 02198v1 Announce Type: cross Abstract: Human-AI teaming has received increasing attention in the literature.
By Nathan Hughes, Ibrahim Habli
arXiv:2608. 16801v1 Announce Type: new Abstract: We study how teams of AI coding agents coordinate while solving programming tasks.
By Giuseppe Destefanis, Tomaso Aste
Conversational AI is increasingly positioned as a teammate rather than a tool, yet we know little about how its presence reshapes communication among the humans on the team. We examined sociocognitive communication dynamics in team decision-making using Group Communication Analysis (GCA), team surveys, and lexical analyses of team discourse.