Exploring the interplay between AI and human logic in mathematical problem-solving

Weizheng Gao et al.

Online Journal of Applied Knowledge Management2024https://doi.org/10.36965/ojakm.2024.12(1)73-93article
AJG 1
Weight
0.61

What the paper says

This paper investigates the dynamic interplay between Artificial Intelligence (AI) and human logic in the domain of mathematical problem-solving. By critically examining a series of case studies, we compare the efficacy of AI-generated solutions, particularly those offered by ChatGPT, against traditional human problem-solving methods. The study employs various mathematical challenges, ranging from abstract logical puzzles to applied numerical problems, to evaluate AI's problem-solving approach and alignment with human cognitive processes. Our analysis highlights instances where AI's computational strategies complement or diverge from human reasoning, shedding light on AI's potential and limitations in deciphering mathematical problems. Furthermore, we explore the implications of integrating AI tools in educational contexts, specifically their role in enhancing students' mathematical problem-solving skills. The paper aims to contribute to the ongoing discourse on the optimal utilization of AI in education, proposing a balanced approach that leverages AI's computational power while fostering the depth and creativity of human logic. Through this comparative study, we advocate for a collaborative model where AI and human reasoning merge to enrich the educational landscape, particularly in the teaching and learning of mathematics.

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https://doi.org/https://doi.org/10.36965/ojakm.2024.12(1)73-93

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@article{weizheng2024,
  title        = {{Exploring the interplay between AI and human logic in mathematical problem-solving}},
  author       = {Weizheng Gao et al.},
  journal      = {Online Journal of Applied Knowledge Management},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.36965/ojakm.2024.12(1)73-93},
}

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Evidence weight

0.61

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.71 × 0.4 = 0.29
M · momentum0.65 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.