Students increasingly use the assistance of large language models (LLMs) in their academic writing. While slight assistance (e.g., grammar and style correction, as well as feedback) is permitted under most institutional policies, it is usually forbidden to offload entire writing tasks to LLMs. Unfortunately, current approaches to LLM-generated text detection predominantly assume a binary distinction between human-written and LLM-generated text, ignoring the breadth of realistic human-AI collaboration practices and limiting the validity of detection systems for educational assessment. In this paper, we propose a contribution-aware evaluation framework for LLM-based detection systems in education. We introduce a scale of eight student contribution levels that model realistic writing scenarios ranging from fully human-written texts to LLM-assisted revisions to fully LLM-generated and adversarially humanized texts. Institutional policies regarding LLM use can then be translated to thresholds of acceptable LLM assistance on this scale. We further present Generative Essay Detection in Education (GEDE), a novel benchmark dataset comprising more than 900 human-written and over 12,500 generated essays across 886 tasks and all contribution levels. Using this benchmark, we conduct a systematic evaluation of four state-of-the-art zero-shot and supervised detection methods across policy boundaries, contribution levels, generative models, out-of-distribution data, and text length. We show that most detectors struggle to accurately classify texts at intermediate student contribution levels, in particular LLM-assisted revisions of human-written texts. Such errors pose a substantial risk of false accusations, indicating that current text detection systems are (still) unsuitable to reliably support the enforcement of institutional policies regarding LLM assistance in education.
Pedagogical Alignment for Vision-Language-Action Models: A Comprehensive Framework for Data, Architecture, and Evaluation in Education
Chosun University（全州大学）；Seoul National University（首尔国立大学）；Korea Institute for Curriculum and Evaluation（韩国课程评价院）；Nanyang Technological University（南洋理工大学）
A Framework for Deductive Semantic Content Analysis at Scale in Science Education Using Text Embeddings
Center for Interdisciplinary Education, University of Oslo（interdisciplinary Education 中心，奥斯陆大学）；Laboratory of Atomic and Solid State Physics, Cornell University（原子与固体物理实验室，康奈尔大学）；Department of Physics, California State University Fullerton（物理系，弗里蒙特加州州立大学）；Center for Computing in Science Education, University of Oslo（科学教育计算中心，奥斯陆大学）
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification
National Engineering Research Center of Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University（国家教育大数据工程研究中心，教育人工智能学院，中央财经大学）
Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity
Arab American University（阿拉伯美国大学）；Fu Foundation School of Engineering and Applied Science（傅 Foundation 工程与应用科学学院）；Columbia University（哥伦比亚大学）；Eastern Michigan University（东密歇根大学）；Texas A&M University（德克萨斯农工大学）
Rethinking Patient Education as Multi-turn Multi-modal Interaction
VA Bedford Health Care（VA贝德福德医疗中心）；UMass Amherst（马萨诸塞大学阿默斯特分校）；UMass Lowell（马萨诸塞大学洛厄尔分校）；Yale University（耶鲁大学）；National University of Singapore（新加坡国立大学）；Yale School of Medicine（耶鲁医学院）
A Platform for Generating Educational Activities to Teach English as a Second Language
Facultad de Ingeniería, Universidad de la República（工程学院，乌拉圭共和国大学）；Facultad de Información y Comunicación, Universidad de la República（信息与通信学院，乌拉圭共和国大学）；Indiana University of Pennsylvania（宾夕法尼亚大学）
