The comparison of general tips for mathematical problem solving generated by generative AI with those generated by human teachers

Author:

Jia Jiyou1ORCID,Wang Tianrui2ORCID,Zhang Yuyue1ORCID,Wang Guangdi3ORCID

Affiliation:

1. Department of Educational Technology, Graduate School of Education, Peking University, Beijing, China

2. School of Humanities, University of Chinese Academy of Sciences, Beijing, China

3. Chinese Institute, Beijing International Studies University, Beijing, China

Funder

National Social Science Fund of China

Publisher

Informa UK Limited

Reference33 articles.

1. Besta M. Blach N. Kubicek A. Gerstenberger R. Gianinazzi L. Gajda J. and Hoefler T. (2023). Graph of thoughts: Solving elaborate problems with large language models. arXiv preprint arXiv:2308.09687.

2. Language models are few-shot learners;Brown T.;Advances in Neural Information Processing Systems,2020

3. Bubeck S. Chandrasekaran V. Eldan R. Gehrke J. Horvitz E. Kamar E. Lee P. Lee Y. T. Li Y. Lundberg S. Nori H. & Zhang Y. (2023). Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712.

4. Dao X. Q. & Le N. B. (2023). Investigating the effectiveness of ChatGPT in mathematical reasoning and problem solving: Evidence from the Vietnamese National high school graduation examination. arXiv preprint arXiv:2306.06331.

5. Frieder S. Pinchetti L. Griffiths R. R. Salvatori T. Lukasiewicz T. Petersen P. C. Chevalier A. & Berner J. (2023). Mathematical capabilities of chatgpt. arXiv preprint arXiv:2301.13867.

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