Uncovering Students' Mental Models of Generative Artificial Intelligence

2026-07-13

AI Summary

This study investigates undergraduate students’ mental models of generative artificial intelligence (GenAI), focusing on the interplay of declarative, procedural, and conditional knowledge. Through qualitative content analysis of concept maps produced by 64 students enrolled in a technology ethics course, the research systematically identifies five distinct types of mental models and evaluates their cognitive depth and structural complexity. Findings reveal that while students generally possess foundational declarative knowledge about GenAI, they demonstrate limited understanding of its underlying mechanisms and appropriate contextual applications. These insights offer a theoretical foundation for AI literacy education and inform recommendations for targeted curriculum design and ethical usage guidelines.

Abstract

In this paper we present a study of students' mental models of generative AI (GenAI). A student's mental model of GenAI influences not only how they perceive the technology's capabilities and limitations but also how they choose to integrate it into their academic work. Whether they view it as a collaborative partner, a shortcut to complete tasks, or something in between, depends on how they conceptualize its use. This study addresses the following questions: (I) What mental models do undergraduate students hold about GenAI? and (II) What aspects of conceptual knowledge - declarative, procedural, and conditional - are present in these mental models? Sixty-four concept maps were collected from students enrolled in a course on technology ethics. Students were asked to construct concept maps representing their understanding of GenAI use. The concept maps were analyzed using a structured codebook and the analysis revealed five categories of mental models: technical process based, educational tool based, transition model, consequence aware model, integrated model. Declarative knowledge was most dominant across maps, suggesting that students largely understood GenAI primarily at a surface level - knowing its names, tools, and applications but demonstrate limited procedural understanding of how it works and limited conditional knowledge about when and why it should or should not be used. By identifying students' mental models, we can improve students' AI literacy by designing curriculum and guidelines that improve cognition while ensuring responsible and ethical use.

Problem

• mental models
• generative AI
• AI literacy
• conceptual knowledge
• undergraduate students

Innovation

• mental models
• generative AI
• concept maps
• AI literacy
• declarative knowledge