AI and Learning with AI: University Students’ Metaphorical Conceptualizations

Abstract

The introduction of artificial intelligence (AI) in people’s lives, including in educational settings, is happening rapidly and on a massive scale. However, AI represents a complicated and abstract concept for laypeople and is, in its entirety, still quite unfamiliar to many, including students in higher education. Metaphors may facilitate the comprehension of novel or abstract concepts in terms of something already known, and help investigate implicit beliefs that have the potential to influence an individual’s actions. This study explored undergraduate students’ (n = 124) perceptions of AI and of learning with AI by analyzing metaphors collected following an established metaphor elicitation paradigm. Students’ attitudes towards AI, AI content knowledge, and usage of AI tools were assessed. The qualitative analysis of metaphors of AI yielded nine categories (e.g., brain, human, machinery, unknown), while the analysis of metaphors for learning with AI yielded seven categories (e.g., self-regulation, educator, shared learning). Overall, the anthropomorphization of AI for both foci was observed. Many conceptualized learning with AI as learning with trustworthy support, and foregrounded the perceived facilitation of learning on the basis of AI. This study highlights the importance of fostering accurate conceptualizations of AI and its role in learning, while addressing misconceptions and overly simplistic representations. Promoting a nuanced understanding of AI is essential to ensuring its effective use as a tool that enhances, rather than impedes, learning processes.