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cs.AI2026
The TEA Nets framework combines AI and cognitive network science to model targets, events and actors in text
Sebastiano Franchini, Alexis Carrillo, Edoardo Sebastiano De Duro +3
We introduce Target-Event-Agent Networks (TEA Nets) as a computational framework to extract subjects (``Agents"), verbs (``Events"), and objects (``Targets") from texts. Grounded i…
cs.AI2026
LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies
Alexis Carrillo, Salvatore Citraro, Ali Aghazhadeh Ardebili +5
Scarce longitudinal evidence examines LLMs' persuasiveness and humanness along time-evolving psychological frameworks. We introduce Talk2AI, a longitudinal framework quantifying ps…
cs.AI2026
Math Education Digital Shadows for Investigating Learning with GenAI: Mathematics Performance, Anxiety, and Confidence in LLMs
Naomi Esposito, Anthony Tricarico, Luisa Porzio +2
Understanding the impact of large language models (LLMs) on mathematics education requires data on LLMs' mathematical performance and biases. To this end, we introduce Math Educati…