Assessing the Reliability and Validity of Large Language Models for Automated Assessment of Student Essays in Higher Education

• URL: http://arxiv.org/abs/2508.02442v1
• Date: Mon, 04 Aug 2025 14:02:12 GMT
• Title: Assessing the Reliability and Validity of Large Language Models for Automated Assessment of Student Essays in Higher Education
• Authors: Andrea Gaggioli, Giuseppe Casaburi, Leonardo Ercolani, Francesco Collova', Pietro Torre, Fabrizio Davide,
• Abstract summary: Five advanced Large Language Models (LLMs), Claude 3.5, DeepSeek v2, Gemini 2.5, GPT-4, and Mistral 24B, were investigated for automated essay scoring in a higher education context.<n>A total of 67 Italian-language student essays were evaluated using a four-criterion rubric.<n>Human-LLM agreement was consistently low and non-significant, and within-model reliability across replications was similarly weak.
• Score: 0.30158609733245967
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This study investigates the reliability and validity of five advanced Large Language Models (LLMs), Claude 3.5, DeepSeek v2, Gemini 2.5, GPT-4, and Mistral 24B, for automated essay scoring in a real world higher education context. A total of 67 Italian-language student essays, written as part of a university psychology course, were evaluated using a four-criterion rubric (Pertinence, Coherence, Originality, Feasibility). Each model scored all essays across three prompt replications to assess intra-model stability. Human-LLM agreement was consistently low and non-significant (Quadratic Weighted Kappa), and within-model reliability across replications was similarly weak (median Kendall's W < 0.30). Systematic scoring divergences emerged, including a tendency to inflate Coherence and inconsistent handling of context-dependent dimensions. Inter-model agreement analysis revealed moderate convergence for Coherence and Originality, but negligible concordance for Pertinence and Feasibility. Although limited in scope, these findings suggest that current LLMs may struggle to replicate human judgment in tasks requiring disciplinary insight and contextual sensitivity. Human oversight remains critical when evaluating open-ended academic work, particularly in interpretive domains.

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