Angela L. Duckworth’s research while affiliated with University of Pennsylvania and other places

Even in environments offering ample opportunities to interact with people from diverse backgrounds, people differ in their tendency to form intergroup friendships. Whereas some develop intergroup friendships, others prefer befriending ingroup members, contributing to prejudice and polarization. We identify open-mindedness—an inclination to engage with and understand different perspectives—as an individual difference predicting the racial, political, and socioeconomic diversity of real-world friendship networks. In a longitudinal study of 1,423 eighth–ninth graders, more open-minded adolescents developed more racially diverse friendship networks over 2 years. Two additional studies (total N = 1,585 adults) replicated and extended this finding: Open-mindedness predicted greater racial, political, and socioeconomic diversity of friends, and was more consistently associated with friendship diversity than Big Five openness to experience. The associations between open-mindedness and friendship diversity were partly explained by open-minded individuals’ lower avoidance of interaction with outgroup members. Building open-mindedness may be one individual-level approach to promote friendships across divides.

Though theoretical accounts describe self-regulation as dynamic, empirical studies typically rely on static measures that fail to capture changes in self-regulation from day to day or moment to moment. As a result, little is known about how or why children’s self-regulation may vary within-person, despite clear relevance for educators. In this paper, we capitalized on repeated observations from wearable devices to test the idea that daily increases in activity (DIA) across the school day could reflect a school-age child’s inability to regulate their physical activity to be appropriate to the school setting. In a national sample followed from birth to age 26 (N = 747; 49% female, 76% White, 13% Black, 6% Hispanic, 5% Other), children showing greater DIA in third grade, objectively measured using actigraphy at school and charted across hourly intervals, were rated as more impulsive and disruptive by teachers and classroom observers, had lower academic achievement in high school (β = -0.11), and completed fewer years of education as adults (β = -0.05). These findings reveal a temporal dimension to children’s behavioral regulation at school. Findings suggest that children’s behavioral regulation, proxied by the ability to inhibit motor activity, deteriorates across the school day and that children who can sustain behavioral regulation for longer go on to greater educational success long-term. Findings also reveal temporal patterns of behavior in third grade that motivate future investigations into daily experiences that could restore children’s behavioral regulation.

In response to the alarming recent decline in US math achievement, we conducted a national megastudy in which 140,461 elementary school teachers who collectively taught 2,992,027 students were randomly assigned to receive a variety of behaviorally informed email nudges aimed at improving students’ progress in math. Specifically, we partnered with the nonprofit educational platform Zearn Math to compare the impact of 15 different interventions with a reminder-only megastudy control condition. All 16 conditions entailed weekly emails delivered to teachers over 4-wk in the fall of 2021. The best-performing intervention, which encouraged teachers to log into Zearn Math for an updated report on how their students were doing that week, produced a 5.06% increase in students’ math progress (3.30% after accounting for the winner’s curse). In exploratory analyses, teachers who received any behaviorally informed email nudge (vs. a reminder-only megastudy control) saw their students’ math progress boosted by an average of 1.89% during the 4-wk intervention period; emails referencing personalized data (i.e., classroom-specific statistics) outperformed emails that did not by 2.26%. While small in size, these intervention effects were consistent across school socioeconomic status and school type (public, private, etc.) and, further, persisted in the 8-wk post-intervention period. Collectively, these findings underscore both how difficult it is to change behavior and the need for large-scale, rigorous, empirical research of the sort undertaken in this megastudy.

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 explores whether using an AI writing tool undermines or supports performance on later unaided writing. In Study 1, forecasters predicted that practicing writing cover letters with an AI tool would impair learning compared to practicing alone. However, in Study 2, participants randomly assigned to practice writing with AI improved more on a subsequent writing test than those assigned to practice without AI (d = 0.40***) -- despite exerting less effort, whether measured by time on task, keystrokes, or subjective ratings. In Study 3, participants who had practiced writing with AI again outperformed those who practiced without AI (d = 0.31***). Consistent with the positive impact of exposure to high-quality examples, these participants performed just as well as those who viewed -- but could not edit -- an AI-generated cover letter (d = 0.03, ns). In both Studies 2 and 3, the benefits of practicing with AI persisted in a one-day follow-up writing test. Collectively, these findings constitute an existence proof that, contrary to participants' intuition, using AI tools can improve, rather than undermine, learning.

The goal of psychological science is to discover truths about human nature, and the typical form of empirical insights is a simple statement of the form x relates to y. We suggest that such “one-liners” imply much larger x- y relationships than those we typically study. Given the multitude of factors that compete and interact to influence any human outcome, small effect sizes should not surprise us. And yet they do—as evidenced by the persistent and systematic underpowering of research studies in psychological science. We suggest an explanation. Effect size magnification is the tendency to exaggerate the importance of the variable under investigation because of the momentary neglect of others. Although problematic, this attentional focus serves a purpose akin to that of the eye’s fovea. We see a particular x-y relationship with greater acuity when it is the center of our attention. Debiasing remedies are not straightforward, but we recommend (a) recalibrating expectations about the effect sizes we study, (b) proactively exploring moderators and boundary conditions, and (c) periodically toggling our focus from the x variable we happen to study to the non- x variables we do not.

People now commonly interact with Artificial Intelligence (AI) agents. How do these interactions shape how humans perceive each other? In two pre-registered studies (total N = 1,261), we show that people evaluate other humans more harshly after interacting with an AI (compared with an unrelated purported human). In Study 1, participants who worked on a creative task with AIs (versus purported humans) subsequently rated another purported human’s work more negatively. Study 2 replicated this effect and demonstrated that the results hold even when participants believed their evaluation would not be shared with the purported human. Exploratory analyses of participants’ conversations show that prior to their human evaluations they were more demanding, more instrumental and displayed less positive affect towards AIs (versus purported humans). These findings point to a potentially worrisome side effect of the exponential rise in human-AI interactions.

Though theoretical accounts describe self-regulation as dynamic, empirical studies typically rely on static measures that fail to capture changes in self-regulation from day to day or moment to moment. As a result, little is known about how or why children’s self-regulation may vary within-person, despite clear relevance for educators. In this paper, we capitalized on repeated observations from wearable devices to test the idea that daily increases in activity (DIA) across the school day could reflect a school-age child’s inability to regulate their physical activity to be appropriate to the school setting. In a national sample followed from birth to age 26 (N = 747; 49% female, 76% White, 13% Black, 6% Hispanic, 5% Other), children showing greater DIA in third grade, objectively measured using actigraphy at school and charted across hourly intervals, were rated as more impulsive and disruptive by teachers and classroom observers, had lower academic achievement in high school (β = -0.11), and completed fewer years of education as adults (β = -0.05). These findings reveal a temporal dimension to children’s behavioral regulation at school. Findings suggest that children’s behavioral regulation, proxied by the ability to inhibit motor activity, deteriorates across the school day and that children who can sustain behavioral regulation for longer go on to greater educational success long-term. Findings also reveal temporal patterns of behavior in third grade that motivate future investigations into daily experiences that could restore children’s behavioral regulation.