Qayyem: A Real-time Platform for Scoring Proficiency of Arabic Essays (2026.acl-demo)
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| Challenge: | Existing Arabic writing technologies primarily use a single quality score for essays, but there is limited support for Arabic AES. |
| Approach: | They propose a Web-based platform that integrates Arabic AES workflows with a user-friendly interface. |
| Outcome: | The proposed system integrates with existing Arabic scoring systems and provides a user-friendly interface. |
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| Challenge: | Automated essay scoring (AES) is a key application of natural language processing . it is based on a holistic score that summarizes the essay's overall quality . |
| Approach: | aaron carroll: automated essay scoring is one of the most important applications in NLP . carroll says the task is still far from being solved, but it's still progressing steadily . he says it'll be interesting to see how researchers can improve performance numbers . |
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LAILA: A Large Trait-Based Dataset for Arabic Automated Essay Scoring (2026.eacl-long)
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May Bashendy, Walid Massoud, Sohaila Eltanbouly, Salam Albatarni, Marwan Sayed, Abrar Abir, Houda Bouamor, Tamer Elsayed
| Challenge: | Existing Arabic resources are small in scale and lack trait-specific annotations. |
| Approach: | They propose to use LAILA to build a large Arabic AES dataset with holistic and trait-specific annotations of seven writing proficiency traits. |
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IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation (2021.acl-demo)
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| Challenge: | Automated Essay Assessment (AEA) aims to judge students’ writing proficiency in an automatic way. |
| Approach: | They propose to use Chinese AEA system IFlyEssayAssess to evaluate essays written by native Chinese students from primary and junior schools. |
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Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking (2020.findings-emnlp)
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| Challenge: | Recent work on sentence prediction tasks uses shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss. |
| Approach: | They propose to use a pre-trained language model to learn text representations first and then to constrain the scores with regression loss or ranking loss. |
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Beyond the Score: Uncertainty-Calibrated LLMs for Automated Essay Assessment (2025.emnlp-main)
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| Challenge: | Automated Essay Scoring (AES) systems attain near–human agreement on some public benchmarks, but real-world adoption is limited. |
| Approach: | They propose a distribution-free wrapper that equips any classifier with set-valued outputs enjoying formal coverage guarantees. |
| Outcome: | The proposed model achieves coverage targets while keeping prediction sets compact. |
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input (N18-1)
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| Challenge: | Existing approaches to Automated Essay Scoring (AES) are not well-suited to capture adversarially crafted input of grammatical but incoherent sequences of sentences. |
| Approach: | They propose a neural model of local coherence that can effectively learn connectedness features between sentences. |
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Conundrums in Cross-Prompt Automated Essay Scoring: Making Sense of the State of the Art (2024.acl-long)
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| Challenge: | Automated essay scoring (AES) is a task of assigning a single score to an essay . authors abandon sophisticated neural architectures and develop a simple feature-based approach . |
| Approach: | a team of researchers develop a feature-based approach to cross-prompt automated essay scoring that adopts a simple neural architecture. |
| Outcome: | a new approach to cross-prompt automated essay scoring can achieve state-of-the-art results. |
Beyond the Gold Standard in Analytic Automated Essay Scoring (2025.acl-srw)
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| Challenge: | Automated Essay Scoring (AES) is a new approach to assessing writing practice . traditional holistic scoring methods are not reliable and lack formative feedback in the classroom. |
| Approach: | They propose to combine analytic and holistic AES to create a system that learns from individual raters instead of gold standard labels. |
| Outcome: | The proposed system learns from individual raters instead of gold standard labels. |
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models (2025.findings-acl)
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Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu
| Challenge: | Automated Essay Scoring (AES) systems face three major challenges: reliance on handcrafted features that limit generalizability, difficulty in capturing fine-grained traits like coherence and argumentation, and inability to handle multimodal contexts. |
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| Outcome: | The proposed system can evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering. |
Can Large Language Models Automatically Score Proficiency of Written Essays? (2024.lrec-main)
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| Challenge: | Automated essay scoring (AES) is one of the earliest research problems in natural language processing. |
| Approach: | They propose to use large language models to analyze and score written essays using four different prompts. |
| Outcome: | The proposed models show comparable performance on four different prompts and a slight advantage over the state-of-the-art models. |