arXiv:2603.14732v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are increasingly considered for automated assessment and feedback, understanding when LLM marking is valid is essential. We evaluate LLM-as-a-judge marking across three physics assessment formats - structured questions, written essays, and scientific plots - comparing GPT-5.2, Grok 4.1, Claude Opus 4.5, DeepSeek-V3.2, Gemini Pro 3, and committee aggregations against human markers under blind, solution-provided, false-solution, and anchored conditions. We distinguish absolute accuracy from rank-order agree
The proliferation of advanced LLMs and increasing interest in automated assessment tools means validating their efficacy is a pressing concern.
This research provides crucial insights into the reliability and limitations of LLMs for evaluative tasks, directly impacting education, hiring, and content moderation.
The understanding of LLM 'judge' capabilities shifts from general performance to task-specific validity, influencing deployment strategies and acceptable use cases.
- · AI developers focused on specialized evaluative tasks
- · Educational technology platforms
- · Institutions adopting automated assessment
- · Test and assessment organizations
- · Generic LLMs promoted for all evaluative tasks
- · Developers neglecting task-specific validation
- · Traditional human-only assessment providers without AI integration
Increased pressure for domain-specific LLM fine-tuning and specialized evaluation benchmarks.
Development of hybrid human-AI assessment systems where each excels in specific evaluation formats.
Potential for LLM-driven personalized learning paths and automated feedback loops, reshaping educational methodologies.
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