Update research evaluation, funding, and training policies to address AI-driven changes in team assembly¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article recommends that science policy adapt to the AI-driven shift toward younger, smaller, but highly impactful teams. Its conclusion states: "Our findings call for policy improvements in research evaluation, funding, and training to address this emerging trend." The strategy directs research-evaluation bodies, funders, and training programs to account for the fact that AI-assisted writing changes how research teams are formed and assembled, rather than assuming ever-larger, senior-led teams are the norm. The article ties this to effective team assembly and evaluation criteria in science.

Design Implications¶

Context¶

Requirements¶

• Recognition that AI-assisted writing reshapes team formation and assembly in science

Constraints¶

• The article states the policy need at the level of research evaluation, funding, and training without prescribing specific instruments

Target Learners¶

• junior researchers
• research teams in science

Target Learning Goals¶

• professional development under AI-assisted writing
• research training and evaluation practices

Claims¶

• AI-assisted teams comprise more junior authors and fewer senior authors [+W]

Related Strategies¶

Examples¶

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Key Sources¶

• Haoyang Wang, Mingze Zhang, Yi Bu, Star Xing Zhao, Meijun Liu. (2026). Smaller, Younger, and More Impactful: How AI-Assisted Writing Transforms Research Teams. https://arxiv.org/abs/2605.27404