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REVIEW 3 major objections 6 minor 1 cited by

Benchmarking Large Language Models on Homework Assessment in Circuit Analysis

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read GPT-4o and Llama 3 70B can assess circuit-analysis homework correctly on more than 85% of attempts across five grading metrics, whereas GPT-3.5 Turbo succeeds only about half the time.

desk verdict Useful benchmark for LLM homework assessment in circuit analysis, but the headline accuracy numbers rest on a single unblinded rater and need independent verification. read the letter →

arxiv 2506.06390 v1 pith:RWEIVTNW submitted 2025-06-05 cs.CY cs.AI

Liangliang Chen , Zhihao Qin , Yiming Guo , Jacqueline Rohde , Ying Zhang This is my paper
classification cs.CYcs.AI
keywords largelanguagemodelscircuitanalysishomeworkassessmentengineeringeducationGPT-4oLlama370BbenchmarkingLaTeXconversion
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 asks whether large language models can be trusted to grade homework in an undergraduate circuit analysis course, a task that matters because unreliable automated feedback could mislead students. It introduces a dataset of 283 real student solutions to 119 problems, converted from handwritten and typed form into LaTeX, and evaluates GPT-3.5 Turbo, GPT-4o, and Llama 3 70B with a prompt template that supplies the official reference solution and asks for assessments on five metrics: completeness, method, final answer, arithmetic error, and units. The central finding is that GPT-4o and Llama 3 70B produce correct assessments at average rates of 88.55% and 85.08%, respectively, while GPT-3.5 Turbo reaches only 51.66%. The paper also documents where the models fail, including misjudging equivalent numerical forms, alternative methods, and unit conventions.

What carries the argument

The mechanism that carries the argument is a deliberately constrained assessment pipeline: homework problems from the course textbook (Svoboda & Dorf, 2013), reference solutions and student solutions converted to LaTeX with the Mathpix snipping tool, and a fixed prompt template that gives the model the official solution and the final answer, then asks for judgments on five named metrics — completeness, method, final answer, arithmetic error, and units. The reference solution acts as an external benchmark within the prompt, compensating for the models' inability to solve circuits from diagrams or perform reliable arithmetic on their own. The five metrics are the evaluation instrument: they define what 'correct assessment' means, and the reported percentages are the fraction of LLM responses judged correct by the human rater.

What would settle it

Have two or more independent graders, blinded to the model identity, re-classify a random sample of the LLM responses using the paper's own rubric; if inter-rater agreement (e.g., Cohen's kappa) falls below a pre-registered threshold, the reported correctness percentages are not stable enough to support the central claim. Alternatively, run the same prompt template on a new set of student solutions with known ground-truth grades; if GPT-4o's correct-response rate drops materially below 88.55%, the benchmark does not generalize beyond its original dataset.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that current state-of-the-art LLMs — specifically GPT-4o and Llama 3 70B — can provide largely reliable homework assessments for an undergraduate circuit analysis course when the student's solution and the official reference solution are both supplied in LaTeX form. The five-metric evaluation shows that GPT-4o is the most consistent overall, with its weakest performance (68.97% correct) on sinusoidal steady-state analysis, while Llama 3 70B excels at judging completeness and method but is markedly weaker on units for advanced topics. GPT-3.5 Turbo is insufficiently reliable for this use. The paper further claims that these models cannot yet interpret circuit diagrams, reliably read handwritten or typed solutions, or perform independent mathematical calculations, which is why the reference solution and LaTeX conversion are necessary components of the assessment pipeline.

Load-bearing premise

The paper treats the first author's classification of each LLM response as correct, partially correct, or incorrect as ground truth, even though that rater designed the dataset and prompt and graded the original homework, and no second rater or blinded check is reported.

Editorial extensions

If this is right

  • If these accuracy levels hold, GPT-4o and Llama 3 70B could be deployed as grading assistants for circuit analysis homework, flagging likely errors for a human instructor to review.
  • The five-metric prompt template can be adapted to other engineering courses that share the same structure of reference solutions and LaTeX-convertible student work.
  • The documented failure modes — treating equivalent fractions, decimals, units, and alternative methods as wrong — define a concrete checklist for prompt engineering or fine-tuning to improve reliability.
  • Because the dataset is not released (students did not consent), the benchmark results are tied to this specific course and time period; reproducing them requires building a comparable dataset.
  • The limitation analysis suggests that fully automated grading without a reference solution is not yet feasible, so near-term systems should keep a human in the loop.

Reading between the lines

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

  • One could test whether the performance gap between GPT-4o and Llama 3 70B on the unit metric reflects a systematic bias in Llama's instruction-following or a quirk of this particular prompt; a version of the prompt that explicitly demands unit checking might close the gap.
  • The paper's reliance on a single rater means the headline percentages should be read as upper-bound estimates; a multi-rater study with agreement statistics would likely show lower effective reliability.
  • The equity dimension is unexplored: if such graders are deployed, they might systematically penalize students who use unusual but correct notation or who make unit errors, which could disproportionately affect certain groups.
  • The same pipeline could be turned into a tutoring tool by reversing the direction: instead of grading, the model could generate targeted feedback on the specific metric that fails, which is a natural extension the authors mention as future work.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This paper benchmarks three LLMs (GPT-3.5 Turbo, GPT-4o, and Llama 3 70B) on homework assessment for an undergraduate circuit analysis course. The authors collected 283 real student solutions across 119 problems, converted official and student solutions to LaTeX, and prompted each model to assess solutions according to five metrics: completeness, method, final answer, arithmetic errors, and units. The LLM responses were manually classified as correct, partially correct, or incorrect by the first author, and the paper reports that GPT-4o and Llama 3 70B achieve average correct-response rates of 88.55% and 85.08% versus 51.66% for GPT-3.5 Turbo (Table 3). The paper additionally presents qualitative limitations of GPT-4o in circuit diagram recognition, handwritten/typed solution recognition, and mathematical calculation (Section 5 and Appendix B).

Significance. The paper addresses a timely and important problem: the reliable use of LLMs for formative homework assessment in engineering education. If the quantitative results are trustworthy, they provide a useful reference point and a reusable evaluation framework. The strengths are the realistic dataset of 283 genuine student solutions (even if not shareable), the LaTeX-conversion pipeline, the five-metric prompt template, and the detailed error taxonomy in Table 10 and Appendix B, which gives concrete insight into current LLM failure modes. The central weakness is the evaluation protocol: all classifications that produce the headline percentages were made by a single non-blinded rater, with no inter-rater reliability, statistical tests, or repeated runs. Because the paper's main claim is a quantitative ranking, this protocol concern is load-bearing; the qualitative error analysis, however, remains a solid contribution even if the exact percentages are treated as provisional.

major comments (3)
  1. [§4 and Appendix A] All individual metric classifications underlying Table 3 (283 solutions × 3 models × 5 metrics) were performed by the first author, who designed the dataset and prompt, served as the course TA, and inspected the responses without blinding to model identity (the folder structure in Fig. A1 preserves model names). No inter-rater reliability, independent verification, or blinding is reported. Because every percentage in Table 3 and all observations (i)–(iv) depend on these classifications, the exact rates and the claimed ranking are not yet verifiable. Please have at least one independent rater re-classify a stratified subsample, report agreement statistics (e.g., Cohen's kappa), and blind raters to which model produced each response.
  2. [§4, Table 3, observations (i)–(iv)] The word 'significantly' is used repeatedly (abstract, Section 4, conclusion), but the paper reports no statistical tests, confidence intervals, or multiple runs. LLM outputs are stochastic, and the paper itself notes GPT-4o's instability due to intrinsic randomness (Appendix B, GPT-4o-q 030601 cs 27 vs. cs 35). A single response per model-solution pair therefore leaves the exact gaps in Table 3 uncertain; for instance, GPT-4o and Llama 3 70B have the same reported final-answer average (84.10%) while their method averages differ (95.75% vs. 98.23%). Please run each model multiple times with reported temperature settings, compute confidence intervals, and apply appropriate significance tests or effect-size measures before making claims of significant outperformance.
  3. [§3.2 and §4] The classification rubric leaves substantial room for subjective judgment. 'Partially correct' is defined only as 'some sentences are accurate while others contain errors' (Section 4), and metrics such as completeness and method require interpretation. Appendix B contains many borderline examples, such as q 080401 cs 5 where 5.0027 V vs. 5 V is marked as an arithmetic error despite the prompt's rule that rounding errors should not be considered calculation errors, and q 100601 cs 24 where 14.69∠5.55° is judged incorrect against 14.67∠5.6°. A second rater could plausibly classify these differently. Please provide a more operational rubric, document how borderline cases are resolved, and quantify inter-rater agreement on the full or a subsampled set.
minor comments (6)
  1. [Table 9] In the GPT-3.5 Turbo row for Frequency Response, the Completeness value is listed as 42.66%, but 12/28 = 42.86%; please correct this typo.
  2. [§3.2, footnote 2] The sentence 'The student’s solution were graded by the first author' should read 'The student’s solutions were graded by the first author.'
  3. [Table 4 footnote] The footnote says 'LLM responses that can categorized in the corresponding classes'; it should be 'can be categorized.'
  4. [§1] The phrase 'we handcrafted the dataset by converting ... using the Mathpix snipping tool' is slightly contradictory; consider rewording to indicate that the conversion was performed with the tool and then manually proofread.
  5. [References] Several references contain malformed identifiers, e.g., 'https://doi.org/https://doi.org/10.48550/arXiv.2402.16733' (Yoo et al., 2024); please clean up the bibliography.
  6. [§5.2] The comparison between GPT-4o and Mathpix is based on a single example; consider softening 'the contents extracted by the Mathpix snipping tool contain no significant errors' to 'in this example, ...' to avoid overgeneralization.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found; the headline comparison is an empirical benchmark, and the single-rater concern is a validity issue rather than a by-construction reduction.

full rationale

The paper's central claim is an empirical benchmark result, not a derived prediction. Table 3 percentages come from manual classification of LLM responses by the first author, who also designed the prompt and dataset; this is a disclosed methodological limitation (Section 4 and Author Contributions) that threatens inter-rater reliability and blinding, but it does not make the result true by construction. The rubric is not defined as agreement with the rater, and the official reference solutions and final-answer strings are external inputs in the prompt. No fitted parameter is renamed as a prediction, and no equation is shown to equal its input by definition. The only self-citation (Rohde et al. 2024, Introduction) is contextual and not load-bearing. Therefore, under the hard rules requiring an explicit reduction for a circularity finding, no circular step can be identified; the concerns raised belong under correctness and validity risk rather than circularity.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No numerical free parameters are fitted; the benchmark measures existing LLMs with a fixed prompt. The main unstated assumptions are the correctness of the official solutions, the reliability of a single non-blinded rater, the sufficiency of the five hand-picked metrics, and the fidelity of the LaTeX conversion.

assumptions (5)
  • domain assumption The official reference solutions are correct and serve as an appropriate benchmark for homework assessment.
    Used in the prompt template as the ground truth for grading; stated in Section 3.2: 'The official solution is always correct and can serve as a benchmark for homework assessment.'
  • domain assumption The first author's classification of LLM responses into correct, partially correct, and incorrect is accurate and unbiased.
    All accuracy numbers in Tables 3-9 rest on this single human rater's judgment; no inter-rater reliability is reported (Section 4).
  • ad hoc to paper The five metrics (completeness, method, final answer, arithmetic error, units) adequately capture the important aspects of homework assessment in circuit analysis.
    The metrics are introduced by the authors specifically for this benchmark and are not derived from an external standard (Section 3.2).
  • domain assumption The Mathpix snipping tool with manual proofreading faithfully converts handwritten and typed solutions into LaTeX without introducing systematic errors that affect assessment.
    The dataset construction relies on this conversion; the paper reports that minor ambiguities remain (Appendix A, Section 5.2).
  • domain assumption The LLM API responses are representative despite unspecified sampling parameters (e.g., temperature) and a single run per solution.
    No sampling parameters or multiple runs are reported, yet the percentages are treated as stable estimates of model performance (Section 4).

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Cite this review

Pith. "Pith review of Benchmarking Large Language Models on Homework Assessment in Circuit Analysis." pith.science (2026). https://pith.science/paper/RWEIVTNW

@misc{pith2026250606390,
  author       = {Pith},
  title        = {Pith review of: Benchmarking Large Language Models on Homework Assessment in Circuit Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RWEIVTNW}},
  note         = {Machine review of arXiv:2506.06390}
}
read the original abstract

Large language models (LLMs) have the potential to revolutionize various fields, including code development, robotics, finance, and education, due to their extensive prior knowledge and rapid advancements. This paper investigates how LLMs can be leveraged in engineering education. Specifically, we benchmark the capabilities of different LLMs, including GPT-3.5 Turbo, GPT-4o, and Llama 3 70B, in assessing homework for an undergraduate-level circuit analysis course. We have developed a novel dataset consisting of official reference solutions and real student solutions to problems from various topics in circuit analysis. To overcome the limitations of image recognition in current state-of-the-art LLMs, the solutions in the dataset are converted to LaTeX format. Using this dataset, a prompt template is designed to test five metrics of student solutions: completeness, method, final answer, arithmetic error, and units. The results show that GPT-4o and Llama 3 70B perform significantly better than GPT-3.5 Turbo across all five metrics, with GPT-4o and Llama 3 70B each having distinct advantages in different evaluation aspects. Additionally, we present insights into the limitations of current LLMs in several aspects of circuit analysis. Given the paramount importance of ensuring reliability in LLM-generated homework assessment to avoid misleading students, our results establish benchmarks and offer valuable insights for the development of a reliable, personalized tutor for circuit analysis -- a focus of our future work. Furthermore, the proposed evaluation methods can be generalized to a broader range of courses for engineering education in the future.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis

    cs.CY 2025-06 conditional novelty 4.0 of 10

    A GPT-4o-based smart tutor with problem-specific documents provided homework feedback in a circuit analysis course, and 90.9% of 66 student feedback responses were positive.

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Works this paper leans on

21 extracted references · 14 canonical work pages · cited by 1 Pith paper

  1. [1]

    oth- ers (2023)

    Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., . . . oth- ers (2023). Gpt-4 technical report. arXiv preprint arXiv:2303.08774 , 1–100, https://doi.org/10.48550/arXiv.2303.08774 2303.08774 Aldriye, H., Alkhalaf, A., Alkhalaf, M. (2019). Automated grading systems for pro- gramming assignments: A literature review. International Jou...

    arXiv doi:10.48550/arxiv.2303.08774 2023

  2. [5]

    track on datasets and benchmarks (pp. 1–46). New Orleans, USA. Guha, N., Nyarko, J., Ho, D., R´ e, C., Chilton, A., Chohlas-Wood, A., . . . others (2023). Legalbench: A collaboratively built benchmark for measuring legal reasoning in large language models. Proceedings of the 37th international conference on neural information processing system (neurips

    work page 2023

  3. [6]

    44123–44279)

    (pp. 44123–44279). New Orleans, USA. Guo, T., Nan, B., Liang, Z., Guo, Z., Chawla, N., Wiest, O., . . . others (2023). What can large language models do in chemistry? a comprehensive benchmark on eight tasks. Proceedings of the 37th conference on neural information processing systems (neurips

    work page 2023

  4. [7]

    59662–59688)

    track on datasets and benchmarks (pp. 59662–59688). New Orleans, USA. Hakhamaneshi, K., Nassar, M., Phielipp, M., Abbeel, P., Stojanovic, V. (2022). Pretraining graph neural networks for few-shot analog circuit modeling and design. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 42 (7), 2163–2173, https://doi.org/10.1109/TC...

    arXiv 2022

  5. [8]

    - volume 1 (pp. 93–105). Chicago, USA. Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., . . . others (2025). A sur- vey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems , 43 (2), 1–55, https://doi.org/10.1145/3703155 Jeon, J., & Lee, S. (2023). Large language ...

    arXiv 2025

  6. [10]

    763–770)

    (pp. 763–770). Kyoto, Japan. Liu, J., Zhou, P., Hua, Y., Chong, D., Tian, Z., Liu, A., . . . others (2023). Bench- marking large language models on cmexam-a comprehensive chinese medical exam dataset. 37th conference on neural information processing systems (neurips

    work page 2023

  7. [11]

    52430–52452)

    track on datasets and benchmarks (pp. 52430–52452). New Orleans, USA. Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys , 55 (9), 1–35, https://doi.org/10.1145/ 3560815 Liu, T., Ding, W., Wang, Z., Tang, J., Hu...

    work page 2023

  8. [13]

    (pp. 1–45). Vienna, Austria. Meißner, N., Speth, S., Kieslinger, J., Becker, S. (2024). Evalquiz–llm-based automated generation of self-assessment quizzes in software engineering education. Software engineering im unterricht der hochschulen 2024 (pp. 53–64). Bonn, Germany. Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., Zettlemoyer...

    work page 2024

Show all 21 references
  1. [15]

    (pp. 1–13). Lisbon, Portugal. 58 Ngoc, T.N., Tran, Q.N., Tang, A., Nguyen, B., Nguyen, T., Pham, T. (2023). Ai- assisted learning for electronic engineering courses in high education. arXiv preprint arXiv:2311.01048 , 1–13, https://doi.org/10.48550/arXiv.2311.01048 2311.01048 ...

    2023 doi arXiv

  2. [16]

    27730–27744)

    (pp. 27730–27744). New Orleans, USA. Ozkaya, I. (2023). Application of large language models to software engineering tasks: Opportunities, risks, and implications. IEEE Software , 40 (3), 4–8, https:// doi.org/10.1109/MS.2023.3248401 Qadir, J. (2023). Engineering education in ...

    2023

  3. [17]

    (pp. 1–19). Portland, USA. Safranek, C.W., Sidamon-Eristoff, A.E., Gilson, A., Chartash, D. (2023). The role of large language models in medical education: applications and implications. JMIR Medical Education , 9 , e50945, https://doi.org/10.2196/50945 Sui, Y., Zhou, M., Zhou...

    2023 doi

  4. [18]

    645–654)

    (pp. 645–654). Merida, Mexico. Svoboda, J.A., & Dorf, R.C. (2013). Introduction to electric circuits (9th edition) . John Wiley & Sons. Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., . . . others (2023). Gemini: a family of highly capable multimodal models. ...

    2013 doi arXiv

  5. [19]

    38975– 38987)

    track on datasets and benchmarks (pp. 38975– 38987). New Orleans, USA. Wang, T., Zhou, N., Chen, Z. (2024). Enhancing computer programming education with llms: A study on effective prompt engineering for python code generation. arXiv preprint arXiv:2407.05437 , 1–18, https://d...

    2024 doi arXiv

  6. [20]

    (pp. 1–37). Vienna, Austria. Xie, W., Niu, J., Xue, C.J., Guan, N. (2024). Grade like a human: Rethinking auto- mated assessment with large language models. arXiv preprint arXiv:2405.19694 , 1–16, https://doi.org/10.48550/arXiv.2405.19694 2405.19694 Xu, Z., Jain, S., Kankanhal...

    2024 doi arXiv

  7. [21]

    576–584)

    (pp. 576–584). Toronto, Canada. Yoo, H., Han, J., Ahn, S.-Y., Oh, A. (2024). Dress: Dataset for rubric-based essay scoring on efl writing. arXiv preprint arXiv:2402.16733 , 1–13, https://doi.org/ https://doi.org/10.48550/arXiv.2402.16733 2402.16733 Zhu, Y., Zhu, C., Wu, T., Wa...

    2024 doi arXiv

  8. [2015]

    167–176)

    (pp. 167–176). Vancouver Canada. Lee, G.-G., Latif, E., Wu, X., Liu, N., Zhai, X. (2024). Applying large language models and chain-of-thought for automatic scoring. Computers and Education: Artificial Intelligence , 100213, https://doi.org/10.1016/j.caeai.2024.100213 57 Li, H....

    2024

  9. [2019]

    169–173)

    (pp. 169–173). Chicago, USA. Ma, Y.J., Liang, W., Wang, G., Huang, D.-A., Bastani, O., Jayaraman, D., . . . Anandkumar, A. (2024). Eureka: Human-level reward design via coding large language models. Proceedings of the 12th international conference on learning representations (iclr

    2024

  10. [2020]

    1877–1901)

    (pp. 1877–1901). Vancouver, Canada. Cai, H., Cai, X., Chang, J., Li, S., Yao, L., Wang, C., . . . others (2024). Sci- assess: Benchmarking llm proficiency in scientific literature analysis. arXiv preprint arXiv:2403.01976 , 1–22, https://doi.org/10.48550/arXiv.2403.01976 2403....

    2024 doi arXiv

  11. [2022]

    (pp. 1–19). Abu Dhabi, United Arab Emirates. Mustapha, K.B., Yap, E.H., Abakr, Y.A. (2024). Bard, chatgpt and 3dgpt: a sci- entometric analysis of generative ai tools and assessment of implications for mechanical engineering education. Interactive Technology and Smart Educatio...

    2024 doi

  12. [2023]

    15607–15631)

    – volume 1: Long papers (pp. 15607–15631). Toronto, Canada. Cribben, I., & Zeinali, Y. (2023). The benefits and limitations of chatgpt in business education and research: A focus on management science, operations management and data analytics. Operations Management and Data An...

    2023 doi

  13. [2024]

    track on datasets and benchmarks (pp. 1–22). Vancouver, Canada. Ebert, C., & Louridas, P. (2023). Generative ai for software practitioners. IEEE Software, 40 (4), 30–38, https://doi.org/10.1109/MS.2023.3265877 Fagbohun, O., Iduwe, N., Abdullahi, M., Ifaturoti, A., Nwanna, O. (...

    2023

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