This study investigates how Italian university students metaphorically represent Generative Artificial Intelligence (GAI) and whether such representations show potential associations with gender, age, field of study and prior GAI knowledge. Data were collected through a cross-sectional online survey of 296 students. The analysis followed a hybrid Framework Method, combining inductive development of a category system, deductive application with single-label assignment, and double coding with assessment of intercoder reliability. Bivariate patterns were examined using chi-square tests and evaluated under Benjamini–Hochberg procedure for false discovery rate control (q = 0.05). Results revealed a compact repertoire of metaphor families. Tool/assistant framings predominated, followed by partner/coach and agent/autonomy framings; risk/control and ethics/governance framings were less frequent yet salient. Students’ metaphors highlight available representational resources and pragmatic expectations about GAI, offering insights for Artificial Intelligence (AI) literacy activities focused on understanding, agency, and control. Limitations include explicit elicitation, single-label coding, and the use of a convenience sample. Future research should test multi-label coding schemes and analyse conversational interaction.
Sansone et al. (Fri,) studied this question.
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