Evaluating the Critical Thinking of Large Language Models: Insights and Limitations
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".