An LLM-based hybrid approach for enhanced automated essay scoring
Abstract Automated Essay Scoring systems have traditionally relied on shallow lexical data, such as word frequency and sentence length, to assess essays. However, these approaches neglect crucial factors like text structure and semantics, resulting in limited evaluations of coherence and quality. To...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87862-3 |
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| author | John Atkinson Diego Palma |
| author_facet | John Atkinson Diego Palma |
| author_sort | John Atkinson |
| collection | DOAJ |
| description | Abstract Automated Essay Scoring systems have traditionally relied on shallow lexical data, such as word frequency and sentence length, to assess essays. However, these approaches neglect crucial factors like text structure and semantics, resulting in limited evaluations of coherence and quality. To address these limitations, we propose a hybrid approach to AES that combines multiple features from different linguistic levels. By leveraging the complementary nature of these features, our model captures the intricate relationships underlying coherent texts. Through extensive experimentation using standard essay datasets, we demonstrate that our large language model based hybrid model surpasses state-of-the-art methods based on shallow features and pure neural networks. This research represents a significant advancement towards the development of an accurate and effective tool for assessing student writing. |
| format | Article |
| id | doaj-art-71710f5260594af4bb6bc15fed19ddb5 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-71710f5260594af4bb6bc15fed19ddb52025-08-20T03:14:06ZengNature PortfolioScientific Reports2045-23222025-04-011511910.1038/s41598-025-87862-3An LLM-based hybrid approach for enhanced automated essay scoringJohn Atkinson0Diego Palma1AI EmpoweredAI EmpoweredAbstract Automated Essay Scoring systems have traditionally relied on shallow lexical data, such as word frequency and sentence length, to assess essays. However, these approaches neglect crucial factors like text structure and semantics, resulting in limited evaluations of coherence and quality. To address these limitations, we propose a hybrid approach to AES that combines multiple features from different linguistic levels. By leveraging the complementary nature of these features, our model captures the intricate relationships underlying coherent texts. Through extensive experimentation using standard essay datasets, we demonstrate that our large language model based hybrid model surpasses state-of-the-art methods based on shallow features and pure neural networks. This research represents a significant advancement towards the development of an accurate and effective tool for assessing student writing.https://doi.org/10.1038/s41598-025-87862-3Automated essay scoringLarge language modelsGPTNatural-language processingNeural context embeddingsTransformer |
| spellingShingle | John Atkinson Diego Palma An LLM-based hybrid approach for enhanced automated essay scoring Scientific Reports Automated essay scoring Large language models GPT Natural-language processing Neural context embeddings Transformer |
| title | An LLM-based hybrid approach for enhanced automated essay scoring |
| title_full | An LLM-based hybrid approach for enhanced automated essay scoring |
| title_fullStr | An LLM-based hybrid approach for enhanced automated essay scoring |
| title_full_unstemmed | An LLM-based hybrid approach for enhanced automated essay scoring |
| title_short | An LLM-based hybrid approach for enhanced automated essay scoring |
| title_sort | llm based hybrid approach for enhanced automated essay scoring |
| topic | Automated essay scoring Large language models GPT Natural-language processing Neural context embeddings Transformer |
| url | https://doi.org/10.1038/s41598-025-87862-3 |
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