A neuro-symbolic approach for automatic assessment in ordinary differential equations
DOI:
https://doi.org/10.66818/aiaie.v1i1.917Keywords:
Automated Assessment, Neuro-symbolic AI Strategies, Ordinary Differential Equations, Large Language Models, Computer Algebra SystemAbstract
This work presents a robust neuro-symbolic framework for the automated assessment of ordinary differential equations by integrating large language models with symbolic computation engines. The core innovation lies in using the natural language model as a semantic orchestrator capable of interpreting student logic, while a deterministic symbolic engine shields the process.
This hybrid approach addresses the risk of hallucinations by providing a rigorous framework for symbolic verification, thus increasing the overall accuracy of the results.
Our results suggest that this architecture has the potential to perform complex error carry-over analysis, aiding in the differentiation between conceptual failures and consistent algebraic derivations, within the scope of the evaluated cases.
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