Improve LLM-based Automatic Essay Scoring with Linguistic Features
Zhaoyi Joey Hou, Alejandro Ciuba, Xiang Lorraine Li
备注：
To be published in the workshop Innovation and Responsibility in AI-Supported Education (iRaise) at the 2025 Conference on Artificial Intelligence (AAAI)
Automatic Essay Scoring (AES) assigns scores to student essays, reducing the grading workload for instructors. Developing a scoring system capable of handling essays across diverse prompts is challenging due to the flexibility and diverse nature of the writing task. Existing methods typically fall into two categories: supervised feature-based approaches and large language model (LLM)-based methods. Supervised feature-based approaches often achieve higher performance but require resource-intensive training. In contrast, LLM-based methods are computationally efficient during inference but tend to suffer from lower performance. This paper combines these approaches by incorporating linguistic features into LLM-based scoring. Experimental results show that this hybrid method outperforms baseline models for both in-domain and out-of-domain writing prompts.
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Future Brain Education Technology Co., Ltd.（北京未来脑教育科技有限公司）；The Hong Kong University of Science and Technology（香港科技大学）；Tsinghua University（清华大学）；Guangxi Zhuang Autonomous Region Big Data Research Institute（广西壮族自治区大数据研究院）
From Texts to Scores: Tracing the Emergence of Essay Quality Representations in Large Language Models
NLP2 CT Lab, Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系NLP2 CT实验室）；Institute of International Language Services Studies, Macau Millennium College（澳门 millennium 学院国际语言服务研究学院）；School of Data Science and Information Engineering, Guizhou Minzu University（贵州民族大学数据科学与信息工程学院）
Human-AI Collaborative Essay Scoring: A Dual-Process Framework with LLMs
Tsinghua University（清华大学）；City University of Hong Kong（香港城市大学）；University of Maryland（马里兰大学）；Beijing Xicheng Educational Research Institute（北京市西城区教育科学研究院）
Unleashing Large Language Models' Proficiency in Zero-shot Essay Scoring
National Key Laboratory for Multimedia Information Processing, Peking University（北京大学多媒体信息处理国家重点实验室）；School of Computer Science, Peking University（北京大学计算机学院）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）
PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback
Department of Educational Psychology, East China Normal University（华东师范大学教育心理学系）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（华东师范大学上海智能教育研究院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
Composable Cross-prompt Essay Scoring by Merging Models
National Key Laboratory for Multimedia Information Processing（国家多媒体信息处理重点实验室）；Peking University School of Computer Science（北京大学计算机学院）；Peking University（北京大学）
WrAFT: a Modularized Automated Writing Evaluation System for Argumentative Essays
Department of Informatics, King’s College London（伦敦国王学院信息学系）；Waseda University（早稻田大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；University of Sheffield（谢菲尔德大学）；Hosei University（法政大学）
From Prompting to Preference Optimization: A Comparative Study of LLM-based Automated Essay Scoring
Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam（信息科技学院，科学大学，胡志明市，越南）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学，胡志明市，越南）
Chinese Essay Rhetoric Recognition Using LoRA, In-context Learning and Model Ensemble
The Open University of China（中国开放大学）；Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education（教育部数字学习技术整合与应用工程研究中心）
Essay submissions to standardized tests like the ACT occasionally include references to bullying, self-harm, violence, and other forms of disturbing content. Graders must take great care to identify cases like these and decide whether to alert authorities on behalf of students who may be in danger. There is a growing need for robust computer systems to support human decision-makers by automatically flagging potential instances of disturbing content. This paper describes MTLHealth, a disturbing content detection pipeline built around recent advances from computational linguistics, particularly …
