Beyond Cognitive Offloading: AI Reliance and Perceived Higher-Order Thinking Skills among UNIMAS Undergraduates

Abstract

The increasing use of artificial intelligence (AI) tools in higher education has prompted widespread concern that student reliance on these technologies may erode higher-order thinking skills (HOTS). However, much of the existing evidence draws on Western contexts and relies on deficit-oriented assumptions about AI use. This mixed-methods study investigates the relationship between reliance on AI and perceived higher-order thinking skills among undergraduate students at Universiti Malaysia Sarawak (UNIMAS), with a comparative focus on STEM and non-STEM disciplines. Quantitative data were collected from 100 undergraduates using a structured questionnaire, while qualitative data were collected from six purposively selected participants through semi-structured interviews. Contrary to the dominant cognitive offloading narrative, a strong positive correlation was found between AI reliance and self-reported engagement in higher-order thinking (ρ = .577, p < .001), with no significant disciplinary differences. Qualitative findings revealed that students approached AI with verification strategies, critical awareness, and reflective judgement rather than passive dependence. These results suggest that the relationship between AI use and cognitive engagement is more nuanced than prevailing literature implies, and that contextual factors such as evaluative habits and pedagogical culture may be more influential than disciplinary background in shaping how students think with AI.