Negli ultimi tempi si parla sempre più spesso di metaverso, realtà aumentata e immersiva e delle opportunità didattiche correlate. AR e VR sono parte integrante del metaverso e, negli ultimi mesi, ricercatori ed esperti nel settore dell’Higher Education stanno sperimentando e analizzando il suo utilizzo nei contesti e processi di apprendimento, cercando di comprendere se esso possa risultare utile al fine di fornire nuovi mezzi per una didattica più inclusiva e motivante per i discenti. Ma è davvero possibile apprendere attraverso il metaverso e rendere questo ambiente virtuale e immersivo inclusivo?

The integration of artificial intelligence (AI) technologies with traditional research methodologies presents significant opportunities for enhancing educational and social interventions while maintaining scientific rigor and community engagement. However, current approaches often lack systematic frameworks for ensuring community ownership, ethical implementation, and sustainable social impact. This study introduces and validates the Community-Based AI Development (CBAID) framework through comprehensive analysis of five diverse AI projects implemented during the Accadia Winter School initiative, focusing on methodological innovation, replicability, and social impact. We employed a multiple case study design analyzing five AI projects: G.A.M.E.S.-I.N. (health promotion), AI4Citizens (digital governance), LLM-Didattica (educational technology), DACSE (health communication), and AI-Enhanced Cybersecurity Training. Data collection included project documentation, stakeholder interviews (n=47), focus groups (n=8), surveys, and observational records. Cross-case analysis identified common patterns and framework validation evidence. All five projects demonstrated successful CBAID framework implementation with significant positive outcomes. Community engagement indicators showed high satisfaction (4.3/5.0) and meaningful participation in decision-making. Individual outcomes included enhanced knowledge, skills, and self-efficacy across domains. The framework showed strong transferability across diverse contexts with systematic adaptation guidance.

Emotional recognition relies on the integration of multiple affective cues. In everyday contexts, however, facial expressions, vocal prosody, and semantic content may convey incongruent emotional information, generating emotional conflict and increasing cognitive demands. The present study examined how multimodal emotional conflict affects emotion recognition during video viewing, focusing on short videos in which a single actor simultaneously conveyed incongruent emotional cues across facial, vocal, and semantic channels. Forty-seven undergraduate students completed a gaze-based response task in which, after each short video, they provided a single judgment of the overall emotion conveyed by the stimulus. The videos depicted either congruent or incongruent combinations of semantic content, facial expressions, and vocal prosody across six basic emotions and a neutral condition. Data were analyzed using repeated-measures ANOVAs and generalized linear mixed-effects models. Accuracy was consistently higher for congruent than incongruent stimuli across all domains, indicating a robust emotional interference effect. Critically, the magnitude of this effect differed by domain. Semantic content showed the largest performance reduction under incongruence, followed by facial expression and vocal prosody. Mixed-effects models confirmed these effects while accounting for participant- and item-level variability and revealed a significant Congruency × Domain interaction. In a gaze-based response task requiring a single overall emotion judgment, emotional conflict disrupted recognition in a domain-specific manner, with semantic information being particularly vulnerable to multimodal interference.