RCN: Augmenting Intelligence Through Collective Learning

The goal of this project is to create a multidisciplinary network of researchers to explore the potential and challenges of new technologies in fostering collective learning. Collective learning, defined as the ability of human groups to adapt cognitive strategies and social networks to learn from each other, is essential in today's rapidly evolving world. By examining collective learning through the lens of new technologies, the project will develop a theoretical framework to guide the design of technology-enhanced learning environments, create tools to study collective learning in various contexts, and design technological platforms that promote effective collective functioning. These efforts support education, foster diversity, and benefit society by improving coordinated action and decision-making processes. The project's outcomes will provide the groundwork for addressing critical contemporary obstacles such as misinformation, conflicting agendas, and social fragmentation that hinder collective efforts to tackle global challenges, including climate change, distrust in science, political instability, and economic inequality. The project is structured around three interconnected themes. The first theme focuses on developing a theoretical base that integrates existing collective adaptation frameworks with human-AI systems. This framework will address challenges such as multi-task satisficing, path dependence, and collective myopia, considering the influence of new technologies on collective learning processes. The second theme explores how online research platforms, combined with AI agents, can be utilized to investigate the dynamics of collective learning. It involves developing and coordinating platforms to study the co-evolution of social learning strategies, network structures, and the problems faced by collectives. The integration of AI agents in these platforms aims to mitigate challenges such as participants being overburdened and to enable large-scale experiments on interactions between human and artificial intelligence. This theme also examines potential challenges in using AI, such as ensuring the reliability of AI agents, addressing potential biases in AI behavior, and maintaining ethical standards in AI-human interactions. The third theme combines insights from the first two themes to design and implement platforms that facilitate effective collaboration, knowledge sharing, and decision-making. The goal is to create digital spaces that enhance trust and collective efficacy, ultimately leading to more beneficial societal outcomes. The successful implementation of this project will overcome disciplinary silos, address U.S.-centric research limitations, leverage new technologies to foster diverse, inclusive collaborations, and bridge the gap between academic research and real-world community needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.