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

Context: Many students now use generative AI (genAI) in their coursework, yet its effects ontheir intellectual development remain poorly understood. While prior work has investigatedstudents’ cognitive offloading during episodic interactions, it remains unclear whether usinggenAI routinely is tied to more fundamental shifts in students’ thinking habits.Objectives: To explore this possibility, we investigate (RQ1-How): how students’ trust in androutine use of genAI affect their cognitive engagement—specifically, reflection, the need forunderstanding, and critical thinking in STEM coursework. Further, we investigate (RQ2-Who):which students are particularly vulnerable to these cognitive disengagement effects.Methods: We drew on dual-process theory, cognitive offloading, and the automation biasliterature to develop a statistical model explaining how and to what extent students’ trust-driven routine use of genAI affected their cognitive engagement habits in STEM coursework, and how these effects differed across students’ diverse 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 cognitiveengagement. Unexpectedly, students with higher technophilic motivations, risk tolerance, andcomputer self-efficacy—traits often celebrated in STEM—were more prone to these effects.Interestingly, students’ prior experience with genAI or academia did not protect them fromcognitively disengaging.Conclusion: Our findings suggest a potential cognitive debt cycle in which routine genAIuse progressively weakens students’ intellectual habits, potentially driving over-reliance andescalating usage.This poses critical challenges for curricula and genAI system design, requiring interventions that actively support cognitive engagement.