From AI Use to Critical Thinking Among Medical Students: A Moderated Mediation Perspective on Cognitive Load and Self-Regulated Learning

Synthesis: Arshad et al. (2026) examined how AI-based educational technology influences critical thinking among 480 undergraduate medical students in Pakistan, using a cross-sectional design and Hayes' PROCESS Model 14. They found that AI use was positively associated with critical thinking and self-regulated learning, while cognitive load negatively related to both. Cognitive load partially mediated the AI-use→critical-thinking link, and self-regulated learning significantly moderated that indirect effect — the negative impact of cognitive load on critical thinking weakened at higher levels of self-regulation. The study argues that AI's effectiveness for higher-order thinking depends not only on the cognitive support AI provides but on learners' capacity to regulate their engagement.

Key Findings

Study Design & Method

The authors used a cross-sectional survey design with 480 undergraduate medical students who had prior experience using AI tools. Data were collected with standardized measures assessing AI usage, cognitive load, self-regulated learning, and critical thinking. Hayes' PROCESS Model 14 was used to test mediation and moderated mediation effects. The analytic approach allowed the authors to model cognitive load as a mediator of AI use on critical thinking and self-regulated learning as a moderator of the mediated (indirect) path — specifically, whether the load→thinking pathway is conditional on students' regulatory capacity.

Implications for AI in Education

The findings caution against treating AI as uniformly beneficial for higher-order cognition. They suggest that the same AI tool can either scaffold or undermine Critical Thinking depending on how much mental work it offloads and whether students can strategically regulate their engagement. For educators, this argues for designing AI-supported learning that deliberately preserves opportunities for independent reasoning — prompting verification, reflection, and judgment rather than answer-replacement — and for building students' Self Regulated Learning capacity as a protective factor against the Cognitive Offloading and shallow-processing risks of generative AI. The authors position the work as a corrective to the research literature's emphasis on efficiency, academic performance, and engagement at the expense of the cognitive processes underlying higher-level thinking, and connect it to academic-integrity and dependency concerns in medical education.

Limitations

The cross-sectional, non-experimental design limits causal inference despite theory-consistent directional modeling. Data are self-report measures collected at a single time point, which raises common-method-bias concerns; the moderated mediation effects are estimated from this single snapshot. The sample is drawn from medical students in one national context (Pakistan), bounding generalizability to other disciplines, institutions, and educational systems. AI usage, cognitive load, and self-regulated learning are all operationalized via standardized self-report instruments rather than objective behavioral measures.

Connected Concepts

Connected Articles

Citation

Arshad, A., Lone, A., Arickswamy, L., Hassan, K., Alnaim, A. A., & AlFarhan, M. F. (2026). From AI use to critical thinking among medical students: A moderated mediation perspective on cognitive load and self-regulated learning.