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📄 Research Article

Uncovering Students' Mental Models of Generative Artificial Intelligence

● high · 2026-07-14student-experience, ai-literacy, generative-ai, metacognition, higher-ed, human-ai-collaboration

This study investigates how students conceptualize generative AI (GenAI) and how those mental models shape their academic integration. A student's mental model of GenAI — their beliefs about what it can and cannot do — influences both perceived capability and choices about when to delegate tasks. The authors surface the range of student conceptions, from tool-as-calculator to collaborator, and show that inaccurate or shallow models correlate with Over Reliance and weaker learning outcomes.

The work connects to broader debates on AI Literacy and Student Experience with AI, arguing that mental-model accuracy is a prerequisite for productive Human AI Collaboration. It extends Metacognition research by treating AI understanding as a learnable metacognitive skill, and bears on Generative AI use in Higher Ed. Implications include designing interventions that explicitly calibrate students' models rather than assuming fluency.

Connected Concepts

  • Over Reliance
  • AI Literacy
  • Student Experience
  • Human AI Collaboration
  • Metacognition
  • Generative AI
  • Higher Ed

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    • Citation

    Amrita Ganguly, Sai Sharanya Garika, Aditya Johri (2026). Uncovering Students' Mental Models of Generative Artificial Intelligence. arXiv:2607.11692. arXiv preprint.