Interpretare l’Intelligenza Artificiale Generativa attraverso le metafore: prospettive degli studenti universitari italiani
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Lo studio indaga come gli studenti universitari italiani percepiscono sul piano metaforico l’Intelligenza Artificiale (IA) Generativa e se le loro rappresentazioni sono associate al genere, all’età, all’area di studi e a eventuali competenze pregresse sul tema. I dati sono stati raccolti online tramite un questionario a carattere cross-sectional che ha coinvolto 296 studenti. L’analisi ha seguito il metodo Framework in versione ibrida, combinando sviluppo induttivo di un sistema di categorie, applicazione deduttiva con codifica a etichetta singola e doppia codifica con valutazione dell’affidabilità tra codificatori. Le associazioni bivariate sono state esaminate con test chi-quadrato e valutate tramite controllo del tasso di false scoperte con procedura Benjamini–Hochberg (q = 0.05). I risultati hanno evidenziato un repertorio compatto di famiglie metaforiche: predominano le cornici strumento/assistente, seguite da quelle partner/coach e agente/autonomia; le cornici rischio/controllo ed etica/governance, meno frequenti, risultano comunque rilevanti. Le metafore scelte dagli studenti rivelano risorse rappresentazionali e aspettative pragmatiche nei confronti dell’IA Generativa, offrendo spunti utili per attività di alfabetizzazione centrate sulla comprensione critica, l’agentività e il controllo. Tra i limiti si segnalano l’elicitazione esplicita, la codifica a etichetta singola e l’uso di un campione di convenienza. Ricerche future potrebbero testare schemi di codifica multi-etichetta e analizzare le interazioni conversazionali.
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