Session: Feedback Fatigue: Can AI Save Us from Ever More Marking? | 反馈疲劳：人工智能能拯救我们摆脱无休止的批改吗？
Theme: Technology Integration and Digital Learning | 技术整合与数字化学习
演讲者简介：
Ken Hyland 是东英吉利大学荣誉教授。他在写作与学术话语领域已发表330余篇论文及30部专著，谷歌学术引用量超10万次。其著作《教学与研究写作》（Teaching and Researching Writing）第五版将于2026年由Routledge出版。根据斯坦福/爱思唯尔基于Scopus数据库的分析，他连续三年（2022、2023、2024）被评为语言与语言学领域最具影响力的学者。其代表作精选集《The Essential Hyland》于2018年由Bloomsbury出版。他目前担任Routledge和Bloomsbury两个丛书系列的主编，兼任吉林大学客座教授及香港人文学院创院院士。此外，他曾共同创办《学术用途英语期刊》（Journal of English for Academic Purposes）并担任《应用语言学》联合主编。
Speaker Bio:
Ken Hyland is an Honorary Professor at the University of East Anglia. He has published over 330 articles and 30 books on writing and academic discourse with over 100,000 citations on Google Scholar. A fifth edition of his Teaching and Researching Writing will be published by Routledge in 2026. According to the Stanford/Elsevier analysis of the Scopus database, he is the most influential scholar in language and linguistics (2022, 2023, 2024). A collection of his work, The Essential Hyland, was published in 2018 by Bloomsbury. He is the Editor of two book series with Routledge and Bloomsbury, is a visiting professor at Jilin University in China and a Foundation Fellow of the Hong Kong Academy of the Humanities. He was founding co-editor of the Journal of English for Academic Purposes and co-editor of Applied Linguistics.
讲座内容：
研究显示，纠错性反馈对学生的写作能力及写作者自身均有益处，这也使教师面临更大压力，需提供更多、更个性化、更详尽的反馈。然而，随着班级规模扩大、工作量不断增加，教师普遍面临疲劳与职业倦怠。目前，数字资源已在支持二语写作与教学方面展现出显著价值，如自动翻译、错误修正、自动评分系统等功能。本次讲座将聚焦于这些技术如何赋能写作反馈。首先概述反馈对学生的价值，进而探讨自动写作评价（AWE）系统与生成式人工智能（GenAI）在反馈中的应用。这些技术能够针对学生需求，在多轮草稿中提供大量即时纠错反馈，从而有望提升学习者的动机与自主性，同时将教师从繁重的批改工作中解放出来。但这些承诺是否只是空谈，反而抬高了人们的期待？教师在其中应扮演什么角色？人工智能究竟能否真正帮助写作者进步，而非仅仅修饰文本？
Session Description:
With research showing the benefits of corrective feedback to both student writing and student writers, teachers have come under increasing pressure to provide more, more personalised, and more detailed responses to students. This often places heavy demands on teachers and with ever-larger class sizes and heavier workloads, teacher fatigue and burn out are common. New digital resources have already proven to be valuable in supporting L2 writing and teaching, offering automatic translation, error correction, automated scoring systems, and other benefits. In this paper I look at what they bring to feedback. Beginning with an overview of what feedback offers students, I explore the contribution of Automated Writing Evaluation (AWE) programmes and Generative Artificial Intelligence (GenAI) to feedback. The ability to provide instant corrective feedback across multiple drafts targeted to student needs and in greater quantities promises to increase learner motivation and autonomy while relieving teachers of hours of marking. But are these empty claims raising expectations? What is the role of teachers in all this and can AI really improve writers and not just texts?