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