osf.io Collaborative learning – an educational paradigm in which students work together to master content and complete projects – has long been a staple of classroom pedagogy. However, the rapid development of Large Language Model chatbots (LLMs), such as ChatGPT, … Continued
This paper describes seven potential benefits of incorporating agent-based modeling (ABM) as a core research methodology for psychological research on social phenomena: building formal theories of complex social systems, validating multilevel causation, understanding temporal and non-linear dynamics, demonstrating the completeness … Continued
osf.io Collaborative learning – an educational paradigm in which students work together to master content and complete projects – has long been a staple of classroom pedagogy. However, the rapid development of Large Language Model chatbots (LLMs), such as ChatGPT, … Continued
Many domains now employ AI-based decision-making aids, and although the potential for AI systems to assist with decision making is much discussed, human-AI collaboration often underperforms due to factors such as (mis)trust in the AI system and beliefs about AI … Continued
AI writing assistants powered by Large Language Models (LLMs) are increasingly used to make autocomplete suggestions to people as they write. Can these AI writing assistants impact people’s attitudes in this process? In two large-scale preregistered experiments (N=2,582), we exposed … Continued
Compound AI systems that combine multiple LLM calls, such as self-refine and multi-agent-debate, achieve strong performance on many AI tasks. We address a core question in optimizing compound systems: for each LLM call or module in the system, how should … Continued
It is widely believed that outsourcing cognitive work to AI boosts immediate productivity at the expense of long-term human capital development. An opposing possibility is that AI tools can support skill development by providing just-in-time, high-quality, personalized examples. This work … Continued
Trust biases how users rely on AI recommendations in AI-assisted decision-making tasks, with low and high levels of trust resulting in increased under- and over-reliance, respectively. We propose that AI assistants should adapt their behavior through trust-adaptive interventions to mitigate … Continued
AI systems increasingly support human decision-making. In many cases, despite the algorithm’s superior performance, the final decision remains in human hands. For example, an AI may assist doctors in determining which diagnostic tests to run, but the doctor ultimately makes … Continued
Despite widespread speculation about artificial intelligence’s impact on the future of work, we lack systematic empirical evidence about how these systems are actually being used for different tasks. Here, we present a novel framework for measuring AI usage patterns across … Continued