Impacts of Histories and Models on LLM Grading: A Study in Advanced Software Engineering Courses
Graduate-level research reading report assessment creates a substantial labor burden for educators. While large language models (LLMs) hold great potential for automating academic grading, their reliability for this specialized task remains understudied, particularly regarding grading consistency, the lack of which represents a primary obstacle to educational fairness. This paper proposes a human-aligned LLM-assisted grading workflow and presents a case study based on 180 student submissions from a graduate advanced software engineering course. We evaluate two mainstream LLMs, Grok and GPT, in terms of grading consistency and alignment with human scores. We find LLMs exhibit distinct leve...
| Authors | Qilin Zhou, Zhuo Wang, Yue Li et al. |
|---|---|
| Primary category | cs.SE |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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