AI‐Generated Essays: Characteristics and Implications on Automated Scoring and Academic Integrity
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| Main Authors: | , , , , |
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| Format: | Artículo Open Access |
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Wiley
2026
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| _version_ | 1867016316363210752 |
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| author | Yang Zhong Jiangang Hao Michael Fauss Chen Li Yuan Wang |
| author_facet | Yang Zhong Jiangang Hao Michael Fauss Chen Li Yuan Wang Yang Zhong Jiangang Hao Michael Fauss Chen Li Yuan Wang |
| collection | Wiley Open Access |
| contents | AI‐Generated Essays: Characteristics and Implications on Automated Scoring and Academic Integrity Yang Zhong Jiangang Hao Michael Fauss Chen Li Yuan Wang Educational Measurement: Issues and Practice Abstract The rapid advancement of large language models (LLMs) has enabled the generation of coherent essays, making AI‐assisted writing increasingly common in educational and professional settings. Using large‐scale empirical data, we examine and benchmark the characteristics and quality of essays generated by popular LLMs and discuss their implications for two key components of writing assessments: automated scoring and academic integrity. Our findings highlight limitations in existing automated scoring systems when applied to essays generated or heavily influenced by AI, and identify areas for improvement, including the development of new features to capture deeper thinking and recalibrating feature weights. Despite growing concerns that the increasing variety of LLMs may undermine the feasibility of detecting AI‐generated essays, our results show that detectors trained on essays generated from one model can often identify texts from others with high accuracy, suggesting that effective detection could remain manageable in practice. 10.1111/emip.70013 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1111/emip.70013 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_emip_70013 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | AI‐Generated Essays: Characteristics and Implications on Automated Scoring and Academic Integrity Yang Zhong Jiangang Hao Michael Fauss Chen Li Yuan Wang Educational Measurement: Issues and Practice AI‐Generated Essays: Characteristics and Implications on Automated Scoring and Academic Integrity Yang Zhong Jiangang Hao Michael Fauss Chen Li Yuan Wang Educational Measurement: Issues and Practice Abstract The rapid advancement of large language models (LLMs) has enabled the generation of coherent essays, making AI‐assisted writing increasingly common in educational and professional settings. Using large‐scale empirical data, we examine and benchmark the characteristics and quality of essays generated by popular LLMs and discuss their implications for two key components of writing assessments: automated scoring and academic integrity. Our findings highlight limitations in existing automated scoring systems when applied to essays generated or heavily influenced by AI, and identify areas for improvement, including the development of new features to capture deeper thinking and recalibrating feature weights. Despite growing concerns that the increasing variety of LLMs may undermine the feasibility of detecting AI‐generated essays, our results show that detectors trained on essays generated from one model can often identify texts from others with high accuracy, suggesting that effective detection could remain manageable in practice. 10.1111/emip.70013 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | AI‐Generated Essays: Characteristics and Implications on Automated Scoring and Academic Integrity |
| topic | Educational Measurement: Issues and Practice |
| url | https://onlinelibrary.wiley.com/doi/10.1111/emip.70013 |