2026-04-13T01:03:16Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4455622026-02-02T07:02:53Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Monsalves Cabello, Diego author Riquelme Csori, Fabian Rolando author Cornide Reyes, Héctor author 2026-01 In the current digital era, analyzing the dynamics of interaction in groups presents challenges in fields such as education, the business sector, and healthcare. The lack of integrated tools that monitor and evaluate discursive and social interactions in real-time makes it difficult to understand the flow of collaboration, the formation of effective teams, or the monitoring of social cognitive processes. In this article, we present a framework designed to analyze speech interactions in group activities by combining Social Network Analysis (SNA) and Large Language Models (LLM). Naira enables the real-time capture, processing, and analysis of speech interaction data, providing tools to evaluate discursive effectiveness and collaborative dynamics. The framework’s components are detailed in its different stages, and application cases are explored in educational, business, and healthcare contexts. A proof of concept in an educational environment proves the versatility and potential of the proposal to improve the understanding and optimization of group processes. Integrating SNA and LLM offers a comprehensive perspective combining validated and interpretable techniques to analyze attribute and relational variables with advanced and current artificial intelligence techniques. The framework’s main innovation lies in its ability to fuse the quantitative structural analysis of SNA with the semantic and qualitative content analysis of LLMs, offering a novel perspective that overcomes the limitations of each technique in isolation. This work was partially funded by Escuela de Ingeniería Informática, Universidad de Valparaíso, Chile, through the REXE grant No. 4054/2022. Diego Monsalves was partially funded by the National Doctoral Scholarship ANID Chile, exempt resolution 4402/2023. H. Cornide-Reyes has been supported by DIUDA (Dirección de Investigación de la Universidad de Atacama) Regular Project of the Universidad de Atacama, No. 22386. Peer Reviewed 9 - Indústria, Innovació i Infraestructura 9.4 - Per a 2030, modernitzar les infraestructures i reconvertir les indústries perquè siguin sostenibles, usant els recursos amb més eficàcia i promovent l’adopció de tecnologies i processos industrials nets i racionals ambientalment, i aconseguint que tots els països adoptin mesures d’acord amb les capacitats respectives 9.5 - Augmentar la investigació científica i millorar la capacitat tecnològica dels sectors industrials de tots els països, en particular els països en desenvolupament, entre d’altres maneres fomentant la innovació i augmentant substancialment, d’aquí al 2030, el nombre de persones que treballen en el camp de la investigació i el desenvolupament per cada milió d’habitants, així com la despesa en investigació i desenvolupament dels sectors públic i privat Postprint (author's final draft) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural Multimodal learning analytics Social networks analytics Data analytics Large language model Artificial intelligence A real-time speech interaction analytics framework for group activities using SNA and LLM techniques