Exposía is the first public dataset linking academic writing and feedback in higher education, comprising student research project proposals, peer and instructor comments, and free-text reviews collected from a Computer Science course. It includes human assessment scores based on a fine‑grained, pedagogically‑grounded schema for both writing and feedback. The dataset is used to benchmark large language models on automated scoring of proposals and student reviews, revealing that different LLMs excel at each task and that closed‑source models outperform open‑weight ones, while a multi‑aspect prompting strategy proves most effective for classroom deployment.
By Dennis Zyska, Alla Rozovskaya, Ilia Kuznetsov, Iryna Gurevych
arXiv:2606. 12422v1 Announce Type: cross Abstract: The integration of large language models (LLMs) into educational assessment represents a transformative shift in classroom grading practices.
By Zewei Tian, Alex Liu, Lief Esbenshade, Michael Xiao, Zachary Zhang, Yulia L\'apicus, Thomas Han, Kevin He, Min Sun
arXiv:2606. 16626v1 Announce Type: cross Abstract: Based on a questionnaire of 100 higher-education students, predominantly from engineering-related fields, and a critical review of recent literature, this chapter examines how students use and perceive Large Language Models (LLMs) in engineering education.
By Olya Kudina
arXiv:2504.02323v5 Announce Type: replace
Abstract: Large language models (LLMs) have created new opportunities to assist teachers and support student learning. While researchers have explored variou...
By Clayton Cohn, Ashwin T S, Naveeduddin Mohammed, Gautam Biswas
arXiv:2507. 03162v2 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) has transformed various domains, particularly computer science (CS) education.
By Adrian Marius Dumitran, Theodor-Pierre Moroianu, Mihnea-Vicentiu Buca
arXiv:2609.00344v1 Announce Type: new
Abstract: Translation students need to learn both how to use translation technologies and how to judge the choices those technologies make available. This articl...
By Gokhan Dogru, Adri\`a Mart\'in Mor
arXiv:2605. 05598v2 Announce Type: replace Abstract: The proliferation of large language models (LLMs) in educational settings has paradoxically undermined the cognitive processes they purport to support.
By Ran Bi, Shiyao Wei, Yuanyiyi Zhou
arXiv:2606. 06025v1 Announce Type: cross Abstract: Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback.
By Xinpeng Qiu, Wang Yihu, Zhifeng Liu, Xiaochen Wang, Jimin Wang
arXiv:2601. 11541v2 Announce Type: replace-cross Abstract: To address the scalability of feedback in computer science while mitigating the privacy and cost limitations of commercial Large Language Models (LLMs), this study evaluates a locally hosted Small Language Model (SLM).
By Suqing Liu, Runlong Ye, Christopher Eaton, Bogdan Simion, Michael Liut
arXiv:2509. 15035v2 Announce Type: replace Abstract: This study investigates the use of generative AI to support formative assessment through machine generated reviews of peer reviews in graduate online courses in a public university in the United States.
By Gabriela C. Zapata, Bill Cope, Mary Kalantzis, Duane Searsmith
arXiv:2607. 00274v1 Announce Type: cross Abstract: Effective writing feedback is among the strongest drivers of student learning, yet producing it at scale is labor-intensive.
By Shayan Peyghambari Oskoui, Norah Almousa, Zhaoyi Joey Hou, Carolina Gustafson, Gayle Rogers, Raquel Coelho, Diane Litman, Xiang Lorraine Li
arXiv:2607. 11314v1 Announce Type: cross Abstract: The promise of AI literacy ``for all'' confronts a structural challenge embedded in how nations organise secondary computer science education.
By Adrian-Marius Dumitran, Iulia-Maria Popescu