AI-assisted academic writing in L2 medical education: Effects on undergraduate medical students' cognitive load and linguistic performance
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Abstract
This study examines the effects of artificial intelligence (AI) writing tools, specifically ChatGPT, on cognitive load and language skills among undergraduate medical students in Saudi Arabia. Utilizing a counterbalanced within-subject experimental design, 52 students produced two academic papers—one with AI support and one without. Cognitive load was assessed using the NASA task load index (NASA-TLX), while text quality was evaluated through grammatical accuracy, syntactic complexity, and lexical diversity. Results revealed a notable reduction in cognitive load with AI assistance (p < .001), particularly in mental demand and effort. Texts generated with AI showed significant improvements in grammatical accuracy (all p < .01) but experienced declines in syntactic complexity and lexical diversity (all p < .01). A negative correlation between cognitive load and linguistic accuracy was found in the non-AI condition (r = -.58, p < .001), which disappeared with AI assistance. Overall, while AI tools effectively decreased cognitive load and enhanced grammatical accuracy, they negatively impacted syntactic complexity and lexical diversity. The findings highlight the need for integrating AI writing tools with teaching methodologies that foster deeper language engagement and advanced writing skills.
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