Managing Anthropomorphism in Student–AI Interaction
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Rena Alasgarova, Jeyhun Rzayev

Managing Anthropomorphism in Student–AI Interaction

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Introduction

Managing anthropomorphism in student–ai interaction. Examines anthropomorphism in student-AI interactions and its impact on AI literacy in education. Offers strategies and policy recommendations for critical, reflective AI use.

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Abstract

This analytical paper examines the persistence of anthropomorphism in student interactions with artificial intelligence (AI) systems and its implications for AI literacy in education. Drawing on philosophical, psychological, and pedagogical perspectives, it explores how Theory of Mind (ToM), the human capacity to attribute beliefs, desires, and intentions to others, underpins the tendency to ascribe mental states to non-human agents. The discussion situates this tendency within the philosophy of mind, contrasting behaviorist, functionalist, and critical perspectives, and considers how these frameworks shape interpretations of AI as apparently understanding. A qualitative case study conducted in Azerbaijani schools provides situated evidence of how students, particularly younger ones, form emotional attachments to AI tools, often perceiving them as trustworthy confidants or personal advisers. These findings are compared with international studies, highlighting cross-cultural consistency in anthropomorphic framing. The paper evaluates both the risks and pedagogical opportunities of such framing, including its impact on epistemic vigilance and motivation. Practical strategies, such as role-switching and critical auditing exercises, are proposed to balance engagement with critical awareness. The analysis concludes with design and policy recommendations, advocating for explicit anthropomorphism awareness in AI literacy curricula and for design choices that avoid reinforcing false mental models. By integrating conceptual synthesis with practice-proximal observation, the paper offers educators a framework for cultivating informed, reflective, and critically engaged student use of AI technologies.



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