LLMs as Teaching Assistants for Mathematics Exam Grading: Reliability, and Practical Usability
Open-ended mathematics exams are valuable because they assess reasoning, proof construction, algorithmic thinking, and communication of intermediate steps. They are also difficult to grade at scale because instructors must apply partial-credit rubrics consistently while giving feedback that helps students repair misconceptions. This paper evaluates six contemporary large language model (LLM) configurations, Gemini 3.1 Pro Extended, Gemini 3.5 Flash, ChatGPT 5.5 Pro Extended, ChatGPT 5.5 Thinking, Claude Pro Opus 4.7, and Claude Sonnet 4.6, as grading assistants for an undergraduate discrete mathematics examination. The study compares two grading policies. The BASELINE policy uses a strict...
| Authors | Aastha Sapkota, M. G. Sarwar Murshed |
|---|---|
| Primary category | cs.CY |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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