Lorenzo Betti, Iacopo Caporossi, Carsten Källner, Karolina Levanaitė, Chenyu Li, Xuan-Chen Liu, Giulia Lorenzini, Vittoria Socci, Michele Re Fiorentin, Ilaria Stanzani, Marta Baratto

The development of problem-solving competence (PSC) among high school students is foundational for preparing resilient and adaptive citizens. Generative artificial intelligence (GenAI) can support this process, but it may also encourage students to offload part of the cognitive work that is necessary for deep learning. While the individual effects of GenAI use are increasingly studied, its collective consequences for competence development within classroom environments remain underexplored. In this study, we use an agent-based model to simulate the evolution of PSC in a high school physics classroom, where students complete tasks individually, in collaboration with peers, or with the support of GenAI. By comparing classrooms with and without access to GenAI across different peer-network structures, we show that GenAI use can diminish competence development and increase the share of students remaining in lower competence tiers. These results suggest that the educational impact of GenAI should be assessed not only through individual learning outcomes but also through its effects on collective competence dynamics.

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Malvina Bozhidarova, Yanpei Cai, Ricardo M.S. Carvalho, Daniele Cirulli, Quentin Dehaene, Martin Diaz, Alexandra Krasnokutskaya, Bernardo Pereira, Onkar Sadekar, Federico Battiston

Modern play-by-play data make it possible to test long-standing intuitions about basketball with the same statistical rigour now routinely applied to other professional sports. Using play-by-play data from 7,054 regular-season and 504 playoff NBA games spanning the 2020-2025 seasons, we provide quantitative insights into scoring patterns and the performance of individual players and teams through methods from statistics, network science, and complexity science. Our findings offer an evidence-based perspective on in-season and in-game performance that can inform coaching strategies, player evaluation, and tactical decision-making.

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Gamal Adel, Shrichand Bhuria, Alessandro Catalano, Liber Dorizzi, Leonardo Federici, Theodora Moldovan, Berné Nortier, Chara Deanna Punzal, Giulia de Meijere, Gerardo Iñiguez

Ego networks capture the variety of structural patterns in the social interactions of individuals. Recently it has been shown that ego networks in online settings display universal patterns of tie strength distributions, but it is unclear how constraints such as spatial proximity and bounded social bandwidth affect such generic behaviour in offline settings. Here, we analyse the time evolution of interaction activity in ego networks constructed from offline face-to-face and colocation data, compare them to online communication networks, and explore simple cumulative advantage models that capture the varying preferentiality of individuals for specific social ties. We find that patterns of preferentiality at the population level are similar for online and face-to-face networks, but not for colocation data, suggesting that the latter is a poor proxy of social network structure. We also provide evidence that empirical ego networks exhibit a bandwidth in the way communication events are allocated across connections. A model implementing this notion uncovers evidence of universal scaling between the tie preferentiality and bandwidth of individuals, common to all online and offline systems explored. Our findings strengthen our understanding of the fundamental mechanisms governing human communication and help disentangle the internal and external factors shaping tie evolution across social contexts.

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Cosimo Agostinelli, Ivan Casanovas, Lochan Chaudhari, Arda Ergin, Pablo Estévez-Gutiérrez, Akanksha Gupta, Juliane T. Moraes, Mario Edoardo Pandolfo, Carlos Gershenson, Haily Merritt, Andreia Sofia Teixeira

How are psychosocial profiles, mental health, and brain functional connectivity related? Studies have been dedicated to unraveling the associations of social support perception and neural functional connectivity. Additionally, personality traits have been explored by examining brain networks. Research on mental health has been developed using a broad range of methods and different approaches. However, little attention has been devoted to understanding how personality traits and social variables are related, and to what extent these components are reflected in brain functional connectivity and mental health outcomes. In this work, we aim to address these complex relations by using data from the Human Connectome Project, both from surveys and resting-state fMRI. The survey data includes personality traits measures and self-reported social support-related variables, which we will refer to as inner- and outer-self, respectively. It also includes data on mental health outcomes. Using z-score standardized measures, we analyze correlation matrices to evaluate the association between the inner- and outer-self domains. Our results show that the social indicators are more evidently grouped by impact on social experience than by the duality of inner-outer selves. Using a k-means clustering algorithm, we separate individuals into two groups according to social profiles. When confronting these results with the mental health outcomes, we show that the more socially desirable cluster exhibited a higher score on positive aspects such as life satisfaction and purpose in life. In the functional brain connectivity, we observe that the cluster with a more socially beneficial profile exhibits lower interconnectivity, especially in the default mode network. The pipeline we present uses a combined analysis of both fMRI and psychosocial variables, which could open the path for more extensive analysis.

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