{"id":"a5186d58-0bf4-4fd0-ab0a-eb886ef90d2a","arxiv_id":"2412.05753","paper_version":1,"verdict":"REJECT","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"The study claims o1-preview surpasses human averages on several higher-order thinking tests, but post-hoc benchmark choices, missing statistics, and unaddressed training-data contamination make the claim unreliable.","lead":"This paper evaluates OpenAI's o1-preview model on seven recognized tests of higher-order thinking and compares its scores with published human averages. It reports that the model beats most human benchmarks, but the comparison methods are too weak to support that conclusion.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Paper's own Table 5 contradicts the 'five out of seven' claim: computational thinking overall z = -0.15, and the named five domains exclude logical/critical thinking where positive z-scores appear.","rationale":"The reader's weakest assumption correctly identifies benchmark contamination as a major validity threat, and contamination could also undermine the central claim. However, the single most load-bearing concern is more direct: the paper's own results contradict the headline 'five out of seven' assertion. Table 5 shows the model below human mean on overall computational thinking, and the Introduction's list excludes logical and critical thinking despite reported positive z-scores. This internal inconsistency means the central claim is not merely threatened by an external factor; it is already unsupported by the paper's reported data. The reader's rationale mentions this contradiction as issue (1), but the weakest_assumption field focuses on contamination. I agree with the reader's overall REJECT verdict, and this internal contradiction reinforces it. A concrete re-computation and domain count would settle whether the headline must be revised or rejected outright.","tokens_in":18102,"tokens_out":4858,"duration_ms":46026,"concrete_test":"Recalculate the overall computational thinking mean and SD from Table 5's five dimension rows using the 10 o1-preview trials (if raw data are available) and compare to the human aggregate. Also count the number of domains across Tables 3, 4, 6, 7, 8, 9, 10, and 11 where the o1-preview mean or z-score is favorable versus the paper's 'five out of seven' claim. If the recalculated overall CT mean is below the human mean, or if the count is six rather than five, the headline claim is refuted by the paper's own data.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central assertion in the Introduction (p.2) is that 'the o1-preview model outperforms human experts in five out of seven domains, including systematic thinking, computational thinking, data literacy, creative thinking, and scientific reasoning.' This claim is internally contradicted by the paper's own results. In Section 3.3, Table 5 reports the Computational Thinking Skills instrument: human overall mean = 3.92 (SD = 0.52) versus o1-preview mean = 3.84 (SD = 1.56), with z = -0.15, and z = -4.25 for Problem-Solving. Thus the model does not outperform humans in computational thinking overall, directly falsifying the 'five out of seven' statement. Additionally, the list of five domains excludes critical thinking and logical reasoning, yet the paper reports positive z-scores for those domains: EWCTET z = 1.60 (undergraduate) and z = 0.90 (postgraduate) in Section 3.1, and LogiQA accuracy 90% vs. 86% in Section 3.6. If those count as outperformance, the correct count would be six of seven, not five. This inconsistency suggests the selection of 'five' is post-hoc and unsupported. The load-bearing premise for the headline claim—that the evidence in the paper substantiates the specific count and domain list—fails on the paper's own reported numbers, independent of external issues like benchmark contamination or statistical inference.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"This manuscript evaluates OpenAI's o1-preview model on seven purported higher-order thinking domains — critical thinking, systematic thinking, computational thinking, data literacy, creative thinking, logical reasoning, and scientific reasoning — using established instruments (EWCTET, Village of Abeesee/LUV, CT Skills/ATTA, Merk/Chen data literacy tests, AUT/RAT, LogiQA, TOSLS). Human performance is taken from previously published studies, and the model is run for 10 trials per instrument. The authors report z-scores for model-versus-human comparisons and claim in the abstract and introduction that o1-preview outperforms human experts in five of seven domains. The paper concludes that AI can complement education in structured assessments but needs human oversight.","tokens_in":18310,"tokens_out":4191,"duration_ms":42853,"significance":"If the headline claim were supported, this would be a notable contribution to the debate on whether LLMs can match or exceed human performance on structured higher-order cognition assessments. The study's strengths include the use of multiple established instruments, transparent reporting of per-dimension means and z-scores in Tables 4–11, and an explicit acknowledgment of the model's weakness in problem-solving. However, the paper's central assertion is internally contradicted by its own reported results, and several load-bearing methodological choices (human-benchmark selection, contamination, and differential scoring across arms) undermine the comparability of the AI-human comparisons. As it stands, the evidence does not support the claimed five-of-seven outperformance.","major_comments":[{"comment":"The claim that o1-preview 'outperforms human experts in five out of seven domains, including ... computational thinking' is contradicted by Table 5, which shows that for Computational Thinking Skills the model's overall z-score is -0.15 (mean 3.84 vs. human 3.92) and the Problem-Solving z-score is -4.25 (mean 1.00 vs. human 3.68). Thus computational thinking is not outperformed overall. Moreover, the five named domains omit critical thinking (EWCTET z = 1.60 and 0.90 in Table 3) and logical reasoning (LogiQA z = 0.62 in Table 10), both of which have positive z-scores; if those count, the correct count would be six of seven, not five. This suggests the 'five of seven' list is not supported by the paper's own data and must be corrected or explicitly justified.","section":"§1, Abstract, Table 5"},{"comment":"The paper never addresses the possibility that o1-preview's training data included the benchmark items. LogiQA, TOSLS, AUT, RAT, and EWCTET are all public, widely used evaluation instruments, and many are standard in LLM benchmarks. If the model has seen these items or their solutions, high scores may reflect memorization rather than reasoning. This is a load-bearing premise for the claim that o1-preview 'outperforms human experts' in higher-order thinking. The authors should either provide evidence of non-contamination, test on private held-out variants, or explicitly discuss this limitation and temper the conclusions accordingly.","section":"§2.2.6, §2.2.7, §3.6, §3.7"},{"comment":"The human AUT originality benchmark (1.74) was scored by trained expert raters, while o1-preview's AUT responses were scored using an automated AI-based tool (Organisciak et al., 2023). This changes the measurement procedure between the two arms of the comparison, so the z-score of 0.71 does not represent a like-for-like comparison. The authors should score both human and AI responses with the same rubric and same rater type (human or automated) to make the comparison valid.","section":"§3.5, Table 9"},{"comment":"The human benchmarks are selected post hoc in ways that inflate the model's apparent advantage. For EWCTET, Table 3 uses 'Undergraduate Students (Highest after Treatment)' of 13.8, while Table 1 includes other undergraduate results such as 11.51 (Hollis) and 6.6 (Davidson); no rationale is given for choosing the highest available mean. For TOSLS, Table 11 reports a z-score of 1.78 against the 0.85 student cohort, but the text claims the model 'surpass[es] ... even biology experts employed at universities,' and Gormally et al. report a biology-expert mean of 0.91, which is omitted from Table 11. The comparison should be made against a pre-specified or clearly justified benchmark, and all reported human cohorts should be included.","section":"§3.1, Tables 1 and 3; §3.7, Table 11"},{"comment":"The statistical analysis is not implemented as described. Section 2.4 states that 'a one-sample t-test was used' and that 'results were supplemented with confidence intervals and effect sizes,' but no t-statistics, confidence intervals, or effect sizes appear anywhere in Section 3. The z-scores are computed by comparing the model's 10-trial mean to the human mean scaled by the human SD, which ignores sampling error in the human mean and treats the model's performance as a fixed point. With seven domains and multiple dimensions, no multiple-comparison correction is applied. The authors should report the actual inferential statistics or revise the methods section to describe the descriptive z-score analysis that was performed.","section":"§2.4, §3"}],"minor_comments":[{"comment":"Typographical errors: 'TOSLS,, exceeding' has a double comma, and 'o1-preview models's' should be 'o1-preview model's.'","section":"Abstract"},{"comment":"The reported '0.99 ± 0.12' is difficult to interpret for a proportion that is bounded at 1.0. Please report the raw values of the five trials or use a scale on which the standard deviation is meaningful.","section":"§3.7"},{"comment":"In Table 10, the first row is labeled 'Model' but refers to human participants; relabel to 'Human' or 'Human participants' for clarity.","section":"§3.6"},{"comment":"The dimension name 'Implemented Challenges' in Table 4 differs from 'Implementation Challenges' in the text (Section 2.2.2); unify the naming.","section":"§3.2, Table 4"},{"comment":"The RAT human benchmark of 44.12% is not clearly derived from the preceding sentences, which report M = 27.38 and M = 23.80 for high- and low-proficiency bilinguals on a Chinese RAT. Clarify which human result is the benchmark and from which scale it comes.","section":"§3.5"},{"comment":"The Data availability statement says data are 'available within the article,' but the paper does not include prompts, raw model responses, or scoring scripts. Consider providing a repository with these materials for reproducibility.","section":"§6"}],"recommendation":"reject","confidential_remarks":"The paper's central claim is internally contradicted by its own Table 5, and the contamination and benchmark-selection issues are load-bearing for any 'outperforms humans' conclusion. These are not merely presentational; they require either new experiments (e.g., contamination tests, matched scoring) or a substantial reframing of the claims. Given the paper's likely public reach and its use of strong language ('outperforms human experts'), I recommend rejection rather than major revision, unless the authors can provide substantially revised claims and new analyses."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The paper's headline claim—o1-preview outperforms humans in five of seven higher-order thinking domains—is not supported by the paper's own Table 5. The Computational Thinking Skills instrument shows the model below the human mean overall (z = -0.15) and far below on problem-solving (z = -4.25). That alone falsifies the \"five\" count as stated. Also, the named five exclude critical thinking and logical reasoning, where positive z-scores appear (EWCTET z = 1.60/0.90, LogiQA 90% vs 86%), so a correct count would be six or the criteria shifted post hoc.\n\nWhat is actually new: the specific battery of seven established instruments run on o1-preview, with ten trials per task. That combination is not in the cited literature, and the paper gives a useful snapshot of where a current model lands on these assessments. The systematic thinking results (LUV total z = 3.20, feedback loops z = 6.53) and data literacy results (Merk Use Data z = 2.19 pre/post; Chen Data Management z = 4.16) are large enough to survive some noise.\n\nThe soft spots are more than cosmetic. The human benchmarks are selected post hoc in several places—EWCTET uses the highest reported mean after treatment; TOSLS compares against the highest cohort. No significance testing is reported despite the stated t-test plan; z-scores below 2 are treated as evidence. The TOSLS item 2 was modified from visual to text, which is acknowledged. The AUT originality for the model was scored by an AI-based tool while the human benchmark used expert raters—same-family scoring bias. And the Korkmaz CT instrument is a self-report Likert scale; asking a language model to agree/disagree with \"I like\" items is not a valid comparison. Contamination is never discussed, despite all instruments being public and widely used in LLM eval.\n\nThe paper could be fixed: correct the count, report all benchmarks, add contamination checks, run a matched human sample, and provide prompts and outputs. As is, the central comparative claim fails.\n\nWho gets value: education researchers looking for a rough map of o1-preview on these instruments, and tool builders wanting benchmark numbers. Not a reliable source for \"AI exceeds humans\" claims.\n\nRecommendation: reject for the comparative claim, but send it to peer review—the battery is worth discussing, and the flaws are identifiable and fixable. If the authors revise with corrected claims and contamination checks, it could become a useful reference.","headline":"The paper's own Table 5 contradicts its five-of-seven claim, but the assembled battery and the large systematic-thinking effects make it worth a referee's time.","tokens_in":18924,"tokens_out":2300,"would_cite":false,"duration_ms":22079,"reading_group":"maybe","serious_thinker":"no","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that OpenAI's o1-preview model outperforms human comparison groups on five of seven established tests of higher-order thinking, with its main weakness showing up in problem-solving.","keywords":["OpenAI o1-preview","higher-order thinking","large language models","educational assessment","critical thinking","scientific reasoning","benchmark comparison","AI in education"],"falsifier":"Give o1-preview newly written parallel versions of the same seven assessments—same constructs and formats, but items that cannot be in its training data—and compare its scores on the originals; a large drop, or the model's ability to recite original items on demand, would show that the reported human-beating performance came from contamination rather than higher-order reasoning. A concrete spot check is the TOSLS Item 2 graph-selection question, which was converted from visual plots to text for the model: present the same item with novel graphs, and see whether the near-perfect score survives.","tokens_in":17840,"feed_emoji":"🧠","tokens_out":10125,"duration_ms":95020,"temperature":0.7,"pith_summary":"This paper asks whether OpenAI's o1-preview model can outperform human students and experts on established tests of higher-order thinking, and the introduction's answer is yes in five of seven domains: systematic thinking, computational thinking, data literacy, creative thinking, and scientific reasoning. The results tables actually show the model scoring above the human comparison groups on nearly every instrument, including the Ennis-Weir critical thinking essay test and LogiQA, with the clearest exception being the problem-solving subscale of the computational-thinking instrument, where the model scored 1.00 against a human mean of 3.68. The authors interpret the pattern as evidence that structured, well-defined assessments play to the model's strengths, while adaptive, ill-structured problem-solving remains a human strength. If the comparisons hold, AI tools could take on routine assessment and tutoring of higher-order skills, though the paper argues human oversight and better assessments are still needed.","feed_headline":"o1-preview beats human benchmarks on most higher-order thinking tests","feed_subtitle":"Five of seven structured assessments favor the model; humans keep the edge in ill-structured problem-solving.","key_machinery":"The machinery is a suite of published, normed assessment instruments, each administered to o1-preview as text prompts and scored with the same rubrics used for human test-takers: EWCTET, the Lake Urmia Vignette, the Computational Thinking Skills scale, the ATTA, Merk et al.'s and Chen et al.'s data literacy tests, the AUT and RAT, LogiQA, and TOSLS. The model's scores are then standardized against the human means and standard deviations reported in the source studies, with z-scores and one-sample t-tests used to express how far above or below the human distribution the model sits. The comparison only works if the instruments measure what they claim to measure, the human norms are representative, and the model encounters the items as novel prompts rather than as memorized training text.","core_discovery":"On the paper's own terms, the central discovery is that a general-purpose reasoning model, o1-preview, can be prompted with the items of standard cognitive assessments and score at or above the human means those instruments were designed to spread out. The model scored 24.33 on the Ennis-Weir critical thinking essay test versus 18.39 for postgraduates; 46.10 on the Lake Urmia Vignette total versus 20.08 for undergraduates; 8.60 on Merk et al.'s \"Use Data\" dimension versus a 4.17 post-test mean; near-perfect 0.99 on TOSLS versus 0.85 for the best student cohort; 90% accuracy on LogiQA versus 86% for humans; and a perfect 20/20 on the ATTA versus 14.63 for experts. The paper claims this as outperformance in five of seven domains, while flagging that the model's problem-solving score on the computational-thinking scale was far below humans and that visual TOSLS items had to be converted to text, which may have affected results.","pith_inferences":["Beyond the paper, the same logic implies that any LLM trained on public exam corpora will tend to saturate standardized reasoning tests, so the meaningful next comparison is on fresh, non-public items rather than on legacy benchmarks.","A test the authors did not run would be to administer paraphrased and answer-permuted versions of the same instruments; a large score drop on those versions would indicate the model is exploiting surface patterns rather than performing the reasoning the tests claim to measure.","The paper compares the model against human means drawn from different studies, cohorts, and years, so a direct head-to-head study with the same human sample and the same items would be needed to confirm the claimed five-of-seven superiority."],"forward_implications":["If the reported comparisons hold, o1-preview can already outperform typical university students on structured critical-thinking, data-literacy, and scientific-reasoning assessments, which suggests AI tools could take over routine scoring and tutoring of those skills.","The model's near-zero score on the computational-thinking problem-solving subscale is a direct counterexample to the idea that LLMs have generalized reasoning, so the paper implies that open-ended, ill-structured problem-solving should remain a human responsibility in AI-assisted classrooms.","Because the model saturates TOSLS and nearly saturates LogiQA, the paper's results imply that widely used tests of 'higher-order thinking' may need redesign if they are to keep measuring human cognitive development rather than machine pattern recognition.","The authors' recommendation that AI be used as a supplement, not a replacement, follows directly from the observed mix of very high scores on structured items and very low scores on problem-solving items."],"supporting_citations":[{"why":"Supplies the Ennis-Weir Critical Thinking Essay Test and its scoring rubric for the critical-thinking comparison.","marker":"[18]"},{"why":"Supplies the Lake Urmia Vignette instrument whose rubric counts variables, causal links, and feedback loops.","marker":"[30]"},{"why":"Supplies the Computational Thinking Skills scale with human benchmarks for creativity, algorithmic thinking, cooperation, critical thinking, and problem-solving.","marker":"[31]"},{"why":"Supplies the Algorithmic Thinking Test for Adults on which o1-preview scored a perfect 20 versus expert and novice human means.","marker":"[32]"},{"why":"Supplies Merk et al.'s data literacy test and the human pre-test and post-test means for the 'Use Data' and 'Transform Data' dimensions.","marker":"[37]"},{"why":"Supplies Chen et al.'s data literacy assessment with human benchmarks for data management, visualization, and basic analysis.","marker":"[38]"},{"why":"Supplies Guilford's Alternate Uses Task, the divergent-thinking measure used for the creative originality score.","marker":"[41]"},{"why":"Supplies the Remote Associates Test, the convergent-thinking measure on which o1-preview scored 70 percent versus the human benchmark.","marker":"[47]"},{"why":"Supplies the LogiQA dataset and its human accuracy benchmark for the logical reasoning comparison.","marker":"[50]"},{"why":"Supplies the Test of Scientific Literacy Skills with student and expert human score benchmarks for the scientific reasoning comparison.","marker":"[55]"}],"fun_headline_variants":["o1-preview tops humans on 5 of 7 thinking tests","OpenAI o1 beats human benchmarks on most cognitive tasks","AI reasoning model outperforms humans in critical thinking tests","o1-preview scores above human means on 5 of 7 assessments","Model exceeds human performance on most higher-order thinking domains"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that o1-preview never memorized the publicly available test questions during training, so its high scores reflect reasoning rather than recall of answers.","fun_headline_variants_meta":{"raw":{"variants":["o1-preview tops humans on 5 of 7 thinking tests","OpenAI o1 beats human benchmarks on most cognitive tasks","AI reasoning model outperforms humans in critical thinking tests","o1-preview scores above human means on 5 of 7 assessments","Model exceeds human performance on most higher-order thinking domains"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000931,"raw_usage":{"total_tokens":4092,"prompt_tokens":1158,"completion_tokens":2934,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":774,"completion_tokens_details":{"reasoning_tokens":2848}},"tokens_in":774,"tokens_out":2934,"duration_ms":19965,"temperature":1.0,"reasoning_tokens":2848,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T20:23:40.629737+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Give o1-preview newly written parallel versions of the same seven assessments—same constructs and formats, but items that cannot be in its training data—and compare its scores on the originals; a large drop, or the model's ability to recite original items on demand, would show that the reported human-beating performance came from contamination rather than higher-order reasoning. A concrete spot check is the TOSLS Item 2 graph-selection question, which was converted from visual plots to text for the model: present the same item with novel graphs, and see whether the near-perfect score survives.","supporting_citations":[{"cited_title":"Cornell Critical Thinking Tests Level X & Level Z Manual","cited_arxiv_id":null,"evidence_quote":"Supplies the Ennis-Weir Critical Thinking Essay Test and its scoring rubric for the critical-thinking comparison."},{"cited_title":"The Lake Urmia vignette: a tool to assess understanding of complexity in socio-environmental systems","cited_arxiv_id":null,"evidence_quote":"Supplies the Lake Urmia Vignette instrument whose rubric counts variables, causal links, and feedback loops."},{"cited_title":"A validity and reliability study of the computational thinking scales (CTS)","cited_arxiv_id":null,"evidence_quote":"Supplies the Computational Thinking Skills scale with human benchmarks for creativity, algorithmic thinking, cooperation, critical thinking, and problem-solving."},{"cited_title":"Assessing computational thinking: Development and validation of the algorithmic thinking test for adults","cited_arxiv_id":null,"evidence_quote":"Supplies the Algorithmic Thinking Test for Adults on which o1-preview scored a perfect 20 versus expert and novice human means."},{"cited_title":"Fostering aspects of pre-service teachers’ data literacy: Results of a randomized controlled trial","cited_arxiv_id":null,"evidence_quote":"Supplies Merk et al.'s data literacy test and the human pre-test and post-test means for the 'Use Data' and 'Transform Data' dimensions."},{"cited_title":"Validating a novel digital performance-based assessment of data literacy: Psychometric and eye-tracking analyses","cited_arxiv_id":null,"evidence_quote":"Supplies Chen et al.'s data literacy assessment with human benchmarks for data management, visualization, and basic analysis."},{"cited_title":"The nature of human intelligence","cited_arxiv_id":null,"evidence_quote":"Supplies Guilford's Alternate Uses Task, the divergent-thinking measure used for the creative originality score."},{"cited_title":"The associative basis of the creative process","cited_arxiv_id":null,"evidence_quote":"Supplies the Remote Associates Test, the convergent-thinking measure on which o1-preview scored 70 percent versus the human benchmark."},{"cited_title":"Developing a test of scientific literacy skills (TOSLS): Measuring undergraduates’ evaluation of scientific information and arguments","cited_arxiv_id":null,"evidence_quote":"Supplies the Test of Scientific Literacy Skills with student and expert human score benchmarks for the scientific reasoning comparison."}],"review_version":1}