Evaluating the Critical Thinking of Large Language Models: Insights and Limitations

Abstract

Integrating artificial intelligence, particularly large language models, in academic writing has significantly enhanced productivity and content quality. However, effective academic writing fundamentally relies on critical thinking, which is essential for constructing persuasive arguments and identifying flaws. This study assessed the critical thinking abilities of GPT-3.5, GPT-4, LLaMA-4, and DeepSeek-R1 by using two primary tests—the multiple-choice Thinking Skills Assessment and the open-ended Ennis–Weir Critical Thinking Essay Test—and comparing their performance with 194 undergraduate students in China. The results showed that while LLaMA-4 and DeepSeek-R1 consistently outperformed students on both assessments, GPT-4 excelled only in the Thinking Skills Assessment and GPT-3.5 underperformed across both tests. These findings indicate that current large language models can excel not only in tasks targeting the specific cognitive skills of critical thinking but also in applying various skills in real-world contexts. However, detailed analysis revealed the tendency of large language models to miss key details, overlook multiple argumentative flaws, and show weakness in tasks demanding abstract reasoning, underscoring the need for human oversight and guidance when using artificial intelligence in academic writing. These results offer valuable insights for optimizing human–AI collaboration in academic writing.

Ähnliche Arbeiten