Why Johnny Can't Think: GenAI's Impacts on Cognitive Engagement

2026-01-30

AI Summary

This study investigates whether routine use of generative artificial intelligence (GenAI) among STEM students leads to diminished cognitive engagement, particularly in reflection, comprehension, and critical thinking. Grounded in dual-process theory, cognitive offloading, and automation bias, the research employs partial least squares structural equation modeling (PLS-SEM) to analyze survey data from 299 students across five North American universities. Findings reveal that students who trust and frequently use GenAI exhibit significantly lower cognitive engagement. Notably, traits traditionally advantageous in STEM—such as high technology affinity, risk tolerance, and computer self-efficacy—paradoxically exacerbate cognitive disengagement, with neither prior GenAI exposure nor academic experience offering protective effects. The study introduces the novel concept of a “cognitive debt cycle,” highlighting how proclivity toward technology may inadvertently entail cognitive risks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Context: Many students now use generative AI in their coursework, yet its effects on intellectual development remain poorly understood. While prior work has investigated students'cognitive offloading during episodic interactions, it remains unclear whether using genAI routinely is tied to more fundamental shifts in students'thinking habits. Objective: We investigate (RQ1-How): how students'trust in and routine use of genAI affect their cognitive engagement -- specifically, reflection, need for understanding, and critical thinking in STEM coursework. Further, we investigate (RQ2-Who): which students are particularly vulnerable to these cognitive disengagement effects. Method: We drew on dual-process theory, cognitive offloading, and automation bias literature to develop a statistical model explaining how and to what extent students'trust-driven routine use of genAI affected their cognitive engagement habits in coursework, and how these effects differed across students'cognitive styles. We empirically evaluated this model using Partial Least Squares Structural Equation Modeling on survey data from 299 STEM students across five North American universities. Results: Students who trusted and routinely used genAI reported significantly lower cognitive engagement. Unexpectedly, students with higher technophilic motivations, risk tolerance, and computer self-efficacy -- traits often celebrated in STEM -- were more prone to these effects. Interestingly, prior experience with genAI or academia did not protect them from cognitively disengaging. Implications: Our findings suggest a potential cognitive debt cycle in which routine genAI use progressively weakens students'intellectual habits, potentially driving over-reliance and escalating usage. This poses critical challenges for curricula and genAI system design, requiring interventions that actively support cognitive engagement.

Problem

• generative AI
• cognitive engagement
• STEM education
• cognitive offloading
• automation bias

Innovation

• cognitive engagement
• generative AI
• cognitive offloading
• automation bias
• PLS-SEM