Pith. sign in

REVIEW 4 major objections 3 minor 48 references

ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities

T0 review · 4 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A new benchmark, ZPD-SCA, tests whether LLMs can match Chinese reading passages to students' cognitive stages. Zero-shot, several models score below random guessing; even with in-context examples, accuracy roughly doubles but systematic bia

desk verdict A promising but unvalidated Chinese reading-difficulty benchmark; the headline LLM results need label validation and statistical substance before they can be taken at face value. read the letter →

arxiv 2508.14377 v2 pith:6D26CESU submitted 2025-08-20 cs.CL cs.AIcs.CY

Wenhan Dong , Zhen Sun , Yuemeng Zhao , Zifan Peng , Jun Wu , Jingyi Zheng , Yule Liu , Xinlei He
show 4 more authors
Yu Wang Ruiming Wang Xinyi Huang Lei Mo
This is my paper · ORCID
classification cs.CLcs.AIcs.CY
keywords LLMevaluationreadingdifficultycognitivestagesZoneofProximalDevelopmentChinesecomprehensionbenchmarkzero-shotlearningin-context
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces ZPD-SCA, a benchmark of Chinese reading passages labeled by expert teachers according to the cognitive stage of the intended student reader, grounded in the Zone of Proximal Development. It asks whether large language models can autonomously judge whether a given text is appropriate for a particular age group's reading ability. In zero-shot testing, most LLMs perform poorly, with two models scoring below the random-guessing baseline. Providing a couple of in-context examples substantially improves accuracy, sometimes nearly doubling it, but the best models still show consistent directional errors. The paper argues that these results reveal only an emerging, unreliable ability in LLMs to perform cognitively aligned reading-difficulty assessment, and positions the benchmark as a tool for future improvements.

What carries the argument

The central object is the ZPD-SCA benchmark itself: a set of Chinese reading passages annotated with stage-level cognitive difficulty labels by 60 Special Grade teachers (the top 0.15% of in-service teachers nationwide). The evaluation task is to map each passage to the correct cognitive-development stage. The paper's mechanism is a controlled comparison between zero-shot prompting and in-context learning, followed by an analysis of directional bias (whether errors tend toward overestimation or underestimation of difficulty) and genre effects.

What would settle it

A direct empirical validation study would settle the central claim: take the same passages, give them to students at the specified cognitive stages, and measure actual comprehension accuracy. If teacher labels correlate poorly with student performance, or if inter-annotator agreement among a separate panel of Special Grade teachers is low, then the below-random and directional-bias findings are relative to an unvalidated standard and the evaluation's foundation collapses.

Watch

Extended reading notes

Core claim

The paper's central claim is that current LLMs lack reliable, zero-shot ability to assess Chinese reading comprehension difficulty in terms of students' cognitive stages. Using expert teacher annotations as ground truth, the benchmark shows that zero-shot accuracy can fall below random guessing (e.g., for Qwen-max and GLM). In-context examples help substantially, but even the strongest models exhibit systematic directional biases—consistently over- or under-estimating difficulty—and performance varies by genre. The paper concludes that while LLMs have some emerging sensitivity to reading difficulty, their judgment is not yet educationally reliable.

Load-bearing premise

The teacher-provided stage labels are treated as the authoritative ground truth for cognitive difficulty, but the benchmark does not show that these labels match students' real comprehension performance or that teachers agree with each other on the labels.

Editorial extensions

If this is right

  • If the central claim is correct, current LLMs cannot be trusted to automatically filter or recommend reading materials by student age or stage without calibration.
  • In-context learning appears to unlock part of the needed ability, suggesting that few-shot prompting—or better training data—could meaningfully improve educational alignment.
  • The systematic directional bias means that even top models are not merely noisy; they have consistent blind spots that could lead to systematically inappropriate recommendations.
  • The genre-dependent performance indicates that a single evaluation score hides important variation, so future benchmarks must stratify by text type.
  • ZPD-SCA provides a concrete yardstick for measuring progress in cognitively aligned educational AI, allowing future models to be compared against a fixed expert-labeled standard.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The benchmark labels are teacher judgments, not direct measurements of student comprehension; a plausible next step would be to validate the labels against actual student performance at each stage, which could strengthen or shift the reported baselines.
  • The directional bias the paper identifies may generalize beyond Chinese reading material, suggesting that LLMs have a generic tendency to compress or expand difficulty distinctions—an inference not tested in the paper but worth examining cross-linguistically.
  • The benchmark could be extended to a generation task: instead of only assessing difficulty, LLMs could be prompted to rewrite or select texts to target a specified cognitive stage, making the bias directly actionable.
  • The zero-shot failure may partly reflect format or label-mapping challenges rather than pure inability; a rigorous test would include prompt variations and calibration checks to separate task-format effects from true competence.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 3 minor

Summary. The paper introduces ZPD-SCA, a benchmark of Chinese reading-comprehension materials annotated by 60 Special Grade teachers for stage-level cognitive difficulty aligned to the Zone of Proximal Development. On this benchmark, the authors evaluate several LLMs in zero-shot and in-context-learning settings. They report that LLMs perform poorly zero-shot, with Qwen-max and GLM below random guessing, that in-context examples roughly double accuracy for the best models, that even the best models show directional biases relative to the teacher labels, and that performance varies across genres. The central claim is that current LLMs have only emerging and unreliable ability to align reading difficulty with students' cognitive stages.

Significance. If the benchmark is valid and the measurements are statistically reliable, this is a useful contribution to educational NLP and LLM evaluation. The use of highly credentialed teachers (top 0.15% of in-service teachers) is a strength, and the task is practically important for personalized learning. The finding that in-context examples improve performance is interesting and actionable. However, the evidentiary value of the study currently hinges on the unvalidated teacher rubric and on absent statistical detail; the headline 'below random guessing' claim cannot be interpreted without class counts and significance testing. The paper's contribution would be strengthened by external validation of the labels against student comprehension data or a clear statement that the claim is about agreement with expert judgment rather than cognitive difficulty itself.

major comments (4)
  1. [Abstract (dataset construction)] The benchmark labels are treated as ground truth for 'stage-level cognitive difficulty' and ZPD, but the abstract provides no inter-annotator agreement, no adjudication protocol, and no external validation against actual student comprehension data. Expert judgment of difficulty can diverge from empirical difficulty, and ZPD is defined by the gap between independent and assisted performance, not by teacher consensus alone. Since every accuracy and bias claim is relative to these labels, the central conclusion—that LLMs fail to align difficulty with cognitive stages—currently collapses into 'LLMs disagree with a specific teacher rubric.' Please report IAA (e.g., Fleiss' kappa or Krippendorff's alpha) and either validate the labels against student outcomes or explicitly reframe the claims as measuring agreement with expert annotation.
  2. [Abstract (experimental claims)] The statement that Qwen-max and GLM 'fall below the probability of random guessing' is uninterpretable without the number of classes and the experimental protocol. Random-guessing probability is 1/k, but the abstract does not state k, the number of items, the number of test repetitions, or any significance test. If k is small (e.g., 3 stages), 'below random' may be within noise; if k is large (e.g., 10 stages), even modest accuracy can be above random. The manuscript must report class distribution, per-model accuracy with confidence intervals or standard errors, and a permutation or bootstrap test for the below-random claim.
  3. [In-context learning setup] The improvement from in-context examples may be inflated if those examples are drawn from the same ZPD-SCA benchmark. In that case the model is matching in-distribution patterns rather than demonstrating generalized ability to assess reading difficulty. Please clarify the selection of in-context examples: are they from held-out items, are they excluded from the test set, and does the reported accuracy correspond to items not seen in the demonstrations? Without this control, the 'emerging abilities' conclusion is not fully supported.
  4. [Reproducibility] The abstract reports accuracy comparisons and bias claims but no prompt templates, model versions, API dates, sampling temperature, or number of runs. This is not merely a presentation issue: LLM outputs are sensitive to these details, and the below-random and bias findings could change with different prompting or sampling choices. The manuscript should include the full prompts, model release identifiers, and random-seed/run information in a reproducible appendix.
minor comments (3)
  1. [Genres] The abstract notes significant variation across genres but does not state the genre taxonomy or the per-genre sample sizes. Please provide a table with per-genre accuracy and confidence intervals, and check whether the 'below random' result is driven by a specific genre.
  2. [Terminology] The phrase 'stage-level cognitive difficulty' is used throughout, but the relationship between 'stage' and 'difficulty' is not defined formally. Clarify whether the annotation is a single ordinal scale or a multidimensional construct, and how it maps to ZPD.
  3. [Related work] The abstract claims 'a notable absence of comprehensive studies' on LLMs and reading difficulty alignment in Chinese education. The paper should cite and contrast with existing readability benchmarks (e.g., CLS, Chinese readability corpora) and any prior work on LLM-based difficulty assessment to support this novelty claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: benchmark labels are independent human expert judgments, not model outputs or fitted parameters.

full rationale

The available manuscript excerpt (abstract) describes ZPD-SCA as a benchmark annotated by 60 Special Grade teachers, and then evaluates LLM zero-shot and in-context performance against those labels. This is a direct empirical evaluation, not a derivation chain: the ground-truth labels come from human experts and are not produced by, fitted to, or defined in terms of the LLMs being tested. There is no fitted parameter subsequently renamed as a prediction, no self-citation used as load-bearing evidence, and no equation in which the target result is an input by construction. The in-context example condition uses examples from the same benchmark, but this measures in-context learning and is not circular; at most it raises a benchmark-contamination or generalizability concern, which belongs under validity rather than circularity. Likewise, the absence of reported inter-annotator agreement or external validation of teacher labels against student performance is an evidentiary weakness, not a circularity of the kind defined by the seven patterns. No specific circular step can be quoted because no such step appears in the provided text.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No fitted numerical parameters appear in the abstract. The benchmark is a constructed evaluation artifact, not a postulated physical or theoretical entity. The key unprovided inputs are the validity of expert labels and the evaluation protocol, listed as domain assumptions.

assumptions (3)
  • domain assumption Special Grade teacher annotations are a valid gold standard for ZPD-aligned cognitive difficulty.
    The abstract claims the benchmark is annotated by 60 Special Grade teachers (top 0.15%), but no inter-annotator agreement or validation against student outcomes is mentioned.
  • domain assumption Chinese reading passages can be assigned to discrete stage-level cognitive difficulty categories.
    Stage-level labels are the core target variable; if difficulty is continuous or inconsistent across genres, accuracy measures over categorical labels may misrepresent model ability.
  • domain assumption Zero-shot and in-context prompting is a fair and stable probe of LLM assessment ability.
    The conclusions about 'emerging abilities' depend on prompt design, example selection, decoding parameters, and model version, none of which are described in the abstract.

how reviews work

0 comments
Cite this review

Pith. "Pith review of ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities." pith.science (2026). https://pith.science/paper/6D26CESU

@misc{pith2026250814377,
  author       = {Pith},
  title        = {Pith review of: ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6D26CESU}},
  note         = {Machine review of arXiv:2508.14377}
}
read the original abstract

Large language models (LLMs) have demonstrated potential in educational applications, yet their capacity to accurately assess the cognitive alignment of reading materials with students' developmental stages remains insufficiently explored. This gap is particularly critical given the foundational educational principle of the Zone of Proximal Development (ZPD), which emphasizes the need to match learning resources with Students' Cognitive Abilities (SCA). Despite the importance of this alignment, there is a notable absence of comprehensive studies investigating LLMs' ability to evaluate reading comprehension difficulty across different student age groups, especially in the context of Chinese language education. To fill this gap, we introduce ZPD-SCA, a novel benchmark specifically designed to assess stage-level Chinese reading comprehension difficulty. The benchmark is annotated by 60 Special Grade teachers, a group that represents the top 0.15% of all in-service teachers nationwide. Experimental results reveal that LLMs perform poorly in zero-shot learning scenarios, with Qwen-max and GLM even falling below the probability of random guessing. When provided with in-context examples, LLMs performance improves substantially, with some models achieving nearly double the accuracy of their zero-shot baselines. These results reveal that LLMs possess emerging abilities to assess reading difficulty, while also exposing limitations in their current training for educationally aligned judgment. Notably, even the best-performing models display systematic directional biases, suggesting difficulties in accurately aligning material difficulty with SCA. Furthermore, significant variations in model performance across different genres underscore the complexity of task. We envision that ZPD-SCA can provide a foundation for evaluating and improving LLMs in cognitively aligned educational applications.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

48 extracted references · 35 canonical work pages

  1. [1]

    Chatgpt promises and challenges in education: Computational and ethical perspectives

    Amr Adel, Ali Ahsan, and Claire Davison. Chatgpt promises and challenges in education: Computational and ethical perspectives. Education Sciences , 14(8):814, 2024

    work page 2024

  2. [2]

    Exceptions, instantiations, and overgeneralization: Insights into how language models process generics

    Emily Allaway, Chandra Bhagavatula, Jena D Hwang, Kathleen McKeown, and Sarah-Jane Leslie. Exceptions, instantiations, and overgeneralization: Insights into how language models process generics. Computational Linguistics , 50(4):1211--1275, 2024

    work page 2024

  3. [3]

    Learn with me—let us boost personalized learning in k-12 math education! Education Sciences , 14(7):773, 2024

    Norbert Annu s and Tibor Kmet'. Learn with me—let us boost personalized learning in k-12 math education! Education Sciences , 14(7):773, 2024

    work page 2024

  4. [4]

    Introducing claude

    Anthropic . Introducing claude. Anthropic website, 2023. Next‑generation AI assistant, Claude and Claude Instant

    work page 2023

  5. [5]

    The potential of deep learning in improving k-12 students' writing skills: A systematic review

    Mazhar Bal and Emre \"O zt \"u rk. The potential of deep learning in improving k-12 students' writing skills: A systematic review. British Educational Research Journal , 2025

    work page 2025

  6. [6]

    The belebele benchmark: a parallel reading comprehension dataset in 122 language variants

    Lucas Bandarkar, Davis Liang, Benjamin Muller, Mikel Artetxe, Satya Narayan Shukla, Donald Husa, Naman Goyal, Abhinandan Krishnan, Luke Zettlemoyer, and Madian Khabsa. The belebele benchmark: a parallel reading comprehension dataset in 122 language variants. In Lun - Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd Annual Meetin...

    work page 2024

  7. [7]

    Rank-then-score: Enhancing large language models for automated essay scoring

    Yida Cai, Kun Liang, Sanwoo Lee, Qinghan Wang, and Yunfang Wu. Rank-then-score: Enhancing large language models for automated essay scoring. CoRR , abs/2504.05736, 2025

    arXiv 2025

  8. [8]

    Prediction as a basis for skilled reading: Insights from modern language models

    Benedetta Cevoli, Chris Watkins, and Kathleen Rastle. Prediction as a basis for skilled reading: Insights from modern language models. Royal Society open science , 9(6):211837, 2022

    work page 2022

Show all 48 references
  1. [9]

    Adapting educational practices for generation z: integrating metacognitive strategies and artificial intelligence

    Sarah Chardonnens. Adapting educational practices for generation z: integrating metacognitive strategies and artificial intelligence. In Frontiers in Education , volume 10, page 1504726. Frontiers, 2025

    2025

  2. [10]

    Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pond \' e de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Sco...

    2021 arXiv

  3. [11]

    Predicting reading difficulty with statistical language models

    Kevyn Collins-Thompson and Jamie Callan. Predicting reading difficulty with statistical language models. Journal of the american society for information science and technology , 56(13):1448--1462, 2005

    2005

  4. [12]

    Assessing text readability using cognitively based indices

    Scott A Crossley, Jerry Greenfield, and Danielle S McNamara. Assessing text readability using cognitively based indices. Tesol Quarterly , 42(3):475--493, 2008

    2008

  5. [13]

    Moving beyond classic readability formulas: New methods and new models

    Scott A Crossley, Stephen Skalicky, and Mihai Dascalu. Moving beyond classic readability formulas: New methods and new models. Journal of Research in Reading , 42(3-4):541--561, 2019

    2019

  6. [14]

    Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J

    DeepSeek - AI, Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei...

    2024 arXiv

  7. [15]

    Evaluation hallucination in multi-round incomplete information lateral-driven reasoning tasks

    Wenhan Dong, Tianyi Hu, Jingyi Zheng, Zhen Sun, Yuemeng Zhao, Yule Liu, Xinlei He, and Xinyi Huang. Evaluation hallucination in multi-round incomplete information lateral-driven reasoning tasks. CoRR , abs/2505.23843, 2025

    2025 arXiv

  8. [16]

    Humanizing llms: A survey of psychological measurements with tools, datasets, and human-agent applications

    Wenhan Dong, Yuemeng Zhao, Zhen Sun, Yule Liu, Zifan Peng, Jingyi Zheng, Zongmin Zhang, Ziyi Zhang, Jun Wu, Ruiming Wang, Shengmin Xu, Xinyi Huang, and Xinlei He. Humanizing llms: A survey of psychological measurements with tools, datasets, and human-agent applications. CoRR ,...

    2025 arXiv

  9. [17]

    The role of C hat GPT in chinese reading education for chinese as a heritage language (chl) learners

    Hanjie Ge. The role of C hat GPT in chinese reading education for chinese as a heritage language (chl) learners. In Proceedings of the 7th International Conference on Big Data and Education (ICBDE) , 2024

    2024

  10. [18]

    From virtual assistant to writing mentor: Exploring the impact of a chatgpt-based writing instruction protocol on efl teachers’ self-efficacy and learners’ writing skill

    Mohammad Ghafouri, Jaleh Hassaskhah, and Amir Mahdavi-Zafarghandi. From virtual assistant to writing mentor: Exploring the impact of a chatgpt-based writing instruction protocol on efl teachers’ self-efficacy and learners’ writing skill. Language Teaching Research , page 13621...

    2024

  11. [19]

    The flores-101 evaluation benchmark for low-resource and multilingual machine translation

    Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzm \'a n, and Angela Fan. The flores-101 evaluation benchmark for low-resource and multilingual machine translation. Transactions of the Ass...

    2022

  12. [20]

    Nastase, Zaid Zada, and Ping Li

    Chanyuan Gu, Samuel A. Nastase, Zaid Zada, and Ping Li. Reading comprehension in l1 and l2 readers: neurocomputational mechanisms revealed through large language models. NPJ Science of Learning , 10(1):46, 2025

    2025

  13. [21]

    Measuring massive multitask language understanding

    Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. CoRR , abs/2009.03300, 2020

    2009 arXiv

  14. [22]

    Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen - Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

    Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen - Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022 ....

    2022

  15. [23]

    McNamara

    Linh Huynh and Danielle S. McNamara. GenAI -powered text personalization: Natural language processing validation of adaptation capabilities. Applied Sciences , 15(12):6791, 2025

    2025

  16. [24]

    A study on the utilization of openai chatgpt as a second language learning tool

    Sunyoung Kim, Joobo Shim, Jaechang Shim, et al. A study on the utilization of openai chatgpt as a second language learning tool. Journal of Multimedia Information System , 10(1):79--88, 2023

    2023

  17. [25]

    Towards general text embeddings with multi-stage contrastive learning, 2023

    Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang. Towards general text embeddings with multi-stage contrastive learning, 2023

    2023

  18. [26]

    Language models outperform cloze predictability in a cognitive model of reading

    Adrielli Tina Lopes Rego, Joshua Snell, and Martijn Meeter. Language models outperform cloze predictability in a cognitive model of reading. PLOS Computational Biology , 20(9):e1012117, 2024

    2024

  19. [27]

    Lui and Sarah M

    Angela M. Lui and Sarah M. Bonner. Preservice and inservice teachers' knowledge, beliefs, and instructional planning in primary school mathematics. Teaching and Teacher Education , 56:1--13, 2016

    2016

  20. [28]

    Chinese curriculum standards for compulsory education, 2022

    Ministry of Education of the People’s Republic of China . Chinese curriculum standards for compulsory education, 2022

    2022

  21. [29]

    Mathematics curriculum standards for compulsory education, 2022

    Ministry of Education of the People’s Republic of China . Mathematics curriculum standards for compulsory education, 2022

    2022

  22. [30]

    Exploring the feasibility and efficacy of chatgpt3 for personalized feedback in teaching

    Irum Naz and Rodney Robertson. Exploring the feasibility and efficacy of chatgpt3 for personalized feedback in teaching. Electronic Journal of e-Learning , 22(2):98--111, 2024

    2024

  23. [31]

    Efl teachers’ perspectives toward the use of chatgpt in writing classes: A case study at van lang university

    Hang Nguyen Thi Thu. Efl teachers’ perspectives toward the use of chatgpt in writing classes: A case study at van lang university. Nguyen, TTH (2023). EFL Teachers’ Perspectives toward the Use of ChatGPT in Writing Classes: A Case Study at Van Lang University. International Jo...

    2023

  24. [32]

    GPT-4 technical report

    OpenAI. GPT-4 technical report. CoRR , abs/2303.08774, 2023

    2023 arXiv

  25. [33]

    A systematic review of artificial intelligence in mathematics education: The emergence of 4ir

    Oluwaseyi Aina Gbolade Opesemowo and Habeeb Omoponle Adewuyi. A systematic review of artificial intelligence in mathematics education: The emergence of 4ir. Eurasia Journal of Mathematics, Science and Technology Education , 20(7):em2478, 2024

    2024

  26. [34]

    Lopes Rego, Joshua Snell, and Martijn Meeter

    Adrielli T. Lopes Rego, Joshua Snell, and Martijn Meeter. Language models outperform cloze predictability in a cognitive model of reading. PLoS Computational Biology , 20(9):e1012117, 2024

    2024

  27. [35]

    High-performance automated abstract screening with large language model ensembles

    Rohan Sanghera, Arun James Thirunavukarasu, Marc El Khoury, Jessica O’Logbon, Yuqing Chen, Archie Watt, Mustafa Mahmood, Hamid Butt, George Nishimura, and Andrew AS Soltan. High-performance automated abstract screening with large language model ensembles. Journal of the Americ...

    2025

  28. [36]

    Christina Schneider and Pamela Gowan

    M. Christina Schneider and Pamela Gowan. Investigating teachers’ skills in interpreting evidence of student learning. Applied Measurement in Education , 26(3):191--204, 2013

    2013

  29. [37]

    Personalized learning through ai: Pedagogical approaches and critical insights

    Klarisa I Vorobyeva, Svetlana Belous, Natalia V Savchenko, Lyudmila M Smirnova, Svetlana A Nikitina, and Sergei P Zhdanov. Personalized learning through ai: Pedagogical approaches and critical insights. Contemporary Educational Technology , 17(2), 2025

    2025

  30. [38]

    Mind in society: Development of higher psychological processes

    Lev Semenovich Vygotsky and Michael Cole. Mind in society: Development of higher psychological processes . Harvard university press, 1978

    1978

  31. [39]

    Yu, and Qingsong Wen

    Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S. Yu, and Qingsong Wen. Large language models for education: A survey and outlook. CoRR , abs/2403.18105, 2024

    2024 arXiv

  32. [40]

    Mmlu-pro: A more robust and challenging multi-task language understanding benchmark

    Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, et al. Mmlu-pro: A more robust and challenging multi-task language understanding benchmark. Advances in Neural Information Processing Systems , 37:...

    2024

  33. [41]

    Human-ai collaborative essay scoring: A dual-process framework with llms

    Changrong Xiao, Wenxing Ma, Qingping Song, Sean Xin Xu, Kunpeng Zhang, Yufang Wang, and Qi Fu. Human-ai collaborative essay scoring: A dual-process framework with llms. In Proceedings of the 15th International Learning Analytics and Knowledge Conference, LAK 2025, Dublin, Irel...

    2025

  34. [42]

    Evaluating reading comprehension exercises generated by LLM s: A showcase of C hat GPT in education applications

    Changrong Xiao, Sean Xin Xu, Kunpeng Zhang, Yufang Wang, and Lei Xia. Evaluating reading comprehension exercises generated by LLM s: A showcase of C hat GPT in education applications. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applica...

    2023

  35. [43]

    Exploring the landscape of generative ai (chatgpt)-powered writing instruction in english as a foreign language education: A scoping review

    Feiwen Xiao, Siyu Zhu, and Wen Xin. Exploring the landscape of generative ai (chatgpt)-powered writing instruction in english as a foreign language education: A scoping review. ECNU Review of Education , page 20965311241310881, 2025

    2025

  36. [44]

    Hanyi Xu, Wensheng Gan, Zhenlian Qi, Jiayang Wu, and Philip S. Yu. Large language models for education: A survey. CoRR , abs/2405.13001, 2024

    2024 arXiv

  37. [45]

    Scaling evidence-based instructional design expertise through large language models

    Gautam Yadav. Scaling evidence-based instructional design expertise through large language models. CoRR , abs/2306.01006, 2023

    2023 arXiv

  38. [46]

    Qwen2.5 technical report

    An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Me...

    2024 arXiv

  39. [47]

    Eduplanner: Llm-based multi-agent systems for customized and intelligent instructional design

    Xueqiao Zhang, Chao Zhang, Jianwen Sun, Jun Xiao, Yi Yang, and Yawei Luo. Eduplanner: Llm-based multi-agent systems for customized and intelligent instructional design. CoRR , abs/2504.05370, 2025

    2025 arXiv

  40. [48]

    Introduction

    Zhipu AI . Introduction. Zhipu AI Open Platform website (BigModel.cn), 2024. Introduction of Zhipu AI Open Platform and the launch of GLM‑4 (January 16 2024)

    2024

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.