Evaluating Large Language Models on Solved and Unsolved Problems in Graph Theory: Implications for Computing Education
This research addresses the reliability of LLMs in supporting mathematically rigorous thinking for students in computing education, highlighting their utility for conceptual exploration but limitations in formal problem-solving.
The study evaluated a large language model's performance on a solved graph theory problem, where it produced correct definitions, recalled results without hallucination, and constructed a valid proof, and on an open problem, where it generated coherent interpretations but did not advance toward a solution. The findings indicate that LLMs can support exploration of established material but are limited in tasks requiring novel mathematical insight.
Large Language Models are increasingly used by students to explore advanced material in computer science, including graph theory. As these tools become integrated into undergraduate and graduate coursework, it is important to understand how reliably they support mathematically rigorous thinking. This study examines the performance of a LLM on two related graph theoretic problems: a solved problem concerning the gracefulness of line graphs and an open problem for which no solution is currently known. We use an eight stage evaluation protocol that reflects authentic mathematical inquiry, including interpretation, exploration, strategy formation, and proof construction. The model performed strongly on the solved problem, producing correct definitions, identifying relevant structures, recalling appropriate results without hallucination, and constructing a valid proof confirmed by a graph theory expert. For the open problem, the model generated coherent interpretations and plausible exploratory strategies but did not advance toward a solution. It did not fabricate results and instead acknowledged uncertainty, which is consistent with the explicit prompting instructions that directed the model to avoid inventing theorems or unsupported claims. These findings indicate that LLMs can support exploration of established material but remain limited in tasks requiring novel mathematical insight or critical structural reasoning. For computing education, this distinction highlights the importance of guiding students to use LLMs for conceptual exploration while relying on independent verification and rigorous argumentation for formal problem solving.