Thinking Less, Trusting More: GenAI's Impacts on Students' Cognitive Habits

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.

Ähnliche Arbeiten