LLMs as Teaching Assistants for Mathematics Exam Grading: Reliability, and Practical Usability
arXiv:2607.01247v1 Announce Type: cross Abstract: 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 g
The rapid advancement of LLM capabilities and pressure to automate administrative tasks in education make this a timely area of research and application.
This development could significantly alter the efficiency and nature of academic assessment, impacting educational institutions and the demand for human graders.
The labor-intensive process of grading complex assignments could become increasingly automated, shifting human roles towards oversight and higher-level instructional design.
- · Educational technology companies
- · LLM developers
- · Students (faster feedback)
- · Human teaching assistants
- · Traditional educational assessment providers
Widespread adoption of LLMs for academic grading, particularly in STEM fields.
A re-evaluation of educational pedagogy and assessment design to leverage LLM capabilities and mitigate potential biases.
Increased focus on 'AI-proof' assessment methods that require human creativity, critical thinking, and interaction, alongside a potential decline in emphasis on rote learning.
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Read at arXiv cs.AI