顾曰国教授，北京外国语大学人工智能与人类语言重点实验室首席专家。中国英汉语比较研究会语言智能教学专业委员会（ChinaCALL）主任委员，国际学术期刊Journal of China Computer-Assisted Language Learning（ChinaCALL会刊）主编，北京外国语大学网络教育学院荣誉院长。曾任中国社会科学院语言研究所研究员，中国社会科学院创新工程首席研究员，《当代语言学》杂志主编（1998-2015）。海外学术兼职包括诺丁汉大学特聘教授、香港理工大学校外学术顾问、西悉尼大学外聘教授、圣彼得堡彼得大帝理工大学访问讲习教授、悉尼大学杰出研究员、台湾科技大学讲习教授等。主要研究兴趣包括老年语言学、语料库语言学、语用学、话语分析、网络教育等。

Generative AI is seen as a set of toolkits capable of making arts, writing stories, composing music, answering questions, you name it – all alone without human help. These intelligent toolkits, becoming widespread only recently, have penetrated human lifeworld beyond imagination. This paper attempts to review critically the GenAI’s impacts, de facto as well potential, on human lifelong learning including such life phases as toddlers, schoolers, adolescents, adults and older people. Principles underlying the critique are formulated and testified.

Keywords: automated content, lifelong learning, natural/artificial intelligence contrasted

Mirjam Hauck教授是英国开放大学健康、教育与语言研究学院数字教育批判理论专业教授，她也是该校人工智能赋能教与学及评估领域学术负责人。 其学术研究聚焦于从社会公平与包容视角，构建协作式在线国际学习（COIL）/ 虚拟交流（VE）领域的理论体系，并将其界定为批判式协作在线国际学习 / 虚拟交流（CVE）。近期她致力于探索CVE在培养学生批判性AI素养技能方面的潜力，该研究同样基于公平、多样性、包容性与可及性原则。她担任欧洲计算机辅助语言学习协会（EUROCALL）主席，是《计算机辅助语言学习（CALL）》期刊副主编，EUROCALL官方期刊ReCALL与《语言学习与技术（LL&T）》编委。作为UNICollaboration.org与中国虚拟交流中心的创始成员，她积极推动国际学术协作实践。

The rapid integration of generative AI into language learning contexts demands more than pedagogical adaptation. It requires a fundamental rethinking of what it means to engage critically with digital tools in education. For language educators and researchers working across diverse linguistic contexts, this challenge carries particular weight: large language models trained predominantly on English-language, Western-epistemological data do not merely provide a learning resource, they carry embedded cultural assumptions about language, knowledge, and communication that risk marginalising learners whose languages and epistemologies lie outside that dominant paradigm.

I will argue that Critical CALL, with its commitment to equity, agency, and social justice, offers an important shared foundation for researchers and educators navigating these questions across different linguistic and institutional contexts. Drawing on the Open University’s Critical AI Literacy (CAIL) framework which is grounded in equity, diversity, inclusion and access principles, I will explore how Critical CALL's established theoretical traditions can inform justice-oriented AI literacy practices that are relevant in diverse local contexts.

Rather than treating AI as a neutral affordance, I will foreground questions of positionality and epistemic justice as productive resources for international collaboration and for equipping language educators and learners not merely to use AI, but to interrogate and reshape it together.

Pascual Pérez-Paredes是西班牙穆尔西亚大学应用语言学与语言学教授，曾任剑桥大学第二语言教育研究讲师。他的研究兴趣包括在应用语言学中使用语料库语言学方法、语料库与数字资源在语言教育中的应用以及语料库辅助话语分析。他是EUROCALL官方期刊ReCALL的联合主编。近期著作包括Corpus Linguistics for Language Learning Research《语料库语言学在语言学习研究中的应用》、Research Methods in Applied Linguistics 《应用语言学研究方法》系列著作，以及Data-driven Learning in and out of the Language Classroom《语言课堂内外的数据驱动学习》。

In language education, AI enables instant human-machine interaction, ubiquitous feedback and new forms of multimodal practice (Ma et al., 2026), yet it also raises challenges linked to reliability, bias, opacity, and overreliance. This keynote examines these tensions while positioning corpus linguistics within emerging AI-mediated ecologies. Drawing on my recent work on this topic (Pérez-Paredes, 2026), two scenarios are outlined: one in which corpus approaches remain complementary to AI, and another where they play a central role in validating and interrogating AI-generated language. The latter foregrounds corpora as an empirical reference for noticing, language data frequency, and register analysis (Pérez-Paredes, Mark & O'Keeffe,2025) supporting evidence-driven, human–AI workflows. In parallel, corpus-based pedagogy is reframed as a pathway into data literacy and Critical AI literacy (Pérez-Paredes, Curry & Ordoñana-Guillamón, 2025), providing language learners with the skills to question and verify AI output, aligning with emerging competencies in conversational AI literacy . The talk argues for an integrated model Pérez-Paredes, Curry & Aguado Jiménez, 2025) where corpora and AI co-evolve, sustaining principled language teaching while promoting transparency, critical engagement, and learner agency across diverse learning settings.

Bill Rivers博士是WP Rivers & Associates首席顾问，也是全国语言服务可及性联盟（National Language Access Coalition）主席。他曾担任俄语/英语翻译和口译员、俄语教师、学术研究人员和管理者，以及营利和非营利组织高管，在国家层面上拥有超过35年的语言能力经验，尤其是在私营和学术领域的经济发展中具有丰富的文化和语言经验，并在第二语言和第三语言习得研究、熟练度评估、项目评估、标准以及语言政策开发与倡导方面发表多项学术成果。他的公司与多个语言公司协会签订了倡导支持协议。Bill Rivers博士还担任国际标准化组织第232技术委员会（教育与学习）主席。

Artificial Intelligence (AI) promises efficiency across innumerable use cases, perhaps none more salient than language. However, AI ant its antecedent technologies, statistical and neural machine translation, have struggled to penetrate the market for language learning curricula. Even with the widespread adoption of applications such as DuoLingo, Mango, and Mandarin Matrix – all of which incorporate some degree of AI for the generation of materials and the scoring of assessments – AI has not delivered on the promise of seamless, ubiquitous language learning. However, the fault lies not in the technology, but in the longstanding and collective failure to re-imagine how technology can improve language learning curricula and pedagogical methods. Dr. Bill Rivers will present a compelling vision of how to leverage AI and other technologies to augment what human teachers and curriculum developers do, emphasising the enormous potential of AI to offload repetitive learning tasks as well as basic interaction, freeing the teacher – who embodies the culture and language – to serve as a resource to guide students, unlocking their metacognition and curiosity.

Jeong-Bae Son教授, 博士，是一位作家、教育家和研究者。现任技术增强语言教学研究院(TELTRI)院长及亚太计算机辅助语言学习协会（APACALL）主席，也是澳大利亚南昆士兰大学荣誉教授。其主要研究领域为应用语言学和对外英语教学（TESOL），研究兴趣包括CALL、技术增强语言教学、AI赋能语言教学、数字素养、AI素养、学术英语写作及语言教师教育。他开发多款CALL应用程序，在该领域发表大量学术成果，并在全球范围内开展系列学术讲座与工作坊。他著有Teacher Development in Technology-Enhanced Language Teaching（《技术增强语言教学中的教师发展》，Palgrave Macmillan, 2018），Insights into digital literacy in language teaching（《语言教学中的数字素养洞察》，Castledown, 2024）等著作。更多学术详情请访问https://drjbson.com。

Artificial intelligence (AI) is changing our educational landscape. What do we know about AI? What is AI literacy? How can we develop AI literacy? What is AI-powered language teaching? What roles and competencies are required of language teachers in AI-powered language teaching? What AI tools are available for second/foreign language education? How can language teachers integrate AI tools effectively into their teaching? What research needs to be conducted on the use of AI tools for language learning and teaching? Where are we heading? These questions are being widely raised and discussed in the field of computer-assisted language learning (CALL). We will explore the questions and talk about challenges and opportunities in the era of AI. We will also look at some AI-powered language teaching activities based on Son’s (2024) AI literacy framework, which highlights using AI creatively, critically, effectively, efficiently, and ethically. The framework involves the development of understanding, knowledge, and skills for using AI applications and tools for specific purposes.

马清教授是香港教育大学人文学院副院长（主管研究及 研究生事务）、语言学及现代语言研究学系教授，研究核心聚焦语言技术领域，涵盖三个紧密相关的领域：二语词汇习得；技术增强语言学习（CALL 和 MALL）；基于语料库的语言教学法（CBLP）。她是CBLP领域的开拓者，荣获包括“2025 年美国国际英语教师协会研究卓越奖”在内的多个国际奖项。她还担任五本国际期刊副主编，包括《计算机辅助语言学习（CALL）》、《语言学习与技术（LL&T）》、《国际计算机辅助语言学习与教学（IJCALLT》）、《语言智能教学（JCCALL）》以及《技术增强学习研究与实践（RPTEL）》。

As AI and corpus technologies reshape language education, this keynote synthesizes evidence from three cross-tier collaboration research studies among pre-service and in-service teachers to outline a practical agenda for professional development. Study 1 (N > 150) involved teachers from more than 28 countries/regions participating in online teacher professional development (TPD) via a Community of Inquiry (CoI) approach. Social network and CoI analyses showed strong social/cognitive presence and teacher knowledge building; design artifacts and interviews linked teacher interaction patterns to observable TPACK growth. Case Study 2 examined a pre‑/in‑service teacher pair in the Chinese context designing corpus‑supported argumentative writing for 73 students. Reciprocal learning emerged: the in‑service teacher mentored pedagogy and content, while the pre‑service teacher contributed corpus‑technology fluency, with growth within each partner’s zone of proximal development. Case Study 3 followed two pre‑/in‑service pairs in Indonesia co‑designing AI‑ and corpus‑enhanced reading lessons. TPACK development was dialogic and situated; teachers applied pedagogical restraint with AI to protect critical thinking, while corpus tools reliably supported vocabulary and comprehension, with positive student perceptions and modest gains. Together, these studies yield scalable TPD principles: structured cross‑tier teams, adoption of iterative design‑teach‑reflect cycles, alignment of technological tools with student learning aims, and foregrounding free and equitable learning opportunities for language teachers.

许悦婷是华南师范大学外文学院教授，博士（香港大学），博士生导师，广东省青年珠江学者（英语教育学科），她是斯坦福大学评选的“全球前2%高被引科学家”（2022-2025）之一，连续六年（2020-2025）上榜爱思唯尔中国高被引学者，中国知网前1%高被引学者（2024&2025年），担任中国英汉语比较研究会外语教师教育与发展专业委员会常务理事。她的研究兴趣包括教师评估素养、在线教育、教师身份认同与情感以及家长参与教育等领域。近年来在国内外教育学及语言学权威期刊发表论文70余篇。获评华南师范大学第七届研究生“我最喜爱的导师”。近三年受邀做外语教师培训超80场，深受广大参训教师好评。

The teaching profession is evolving against the backgrounds of overlapping global challenges and breathtaking technological shifts. When changes in education accelerates, teacher education cannot just tweak at the margin. As teacher educators, we need to rethink teaching itself, redesign what we teach and how we assess, and ultimately reflect on what preparing qualified teachers really means today and tomorrow. To address this issue, this talk introduces what we have done so far in the teacher education program of South China Normal University (SCNU) to nurture a profession to be agile enough to meet future challenges: understanding critical thinking as an essential skill and disposition for pre-service teachers, developing and adapting teacher education courses, innovating teacher educator’s pedagogical practices, and transforming assessment tasks. While these attempts are still tentative, this talk concludes with implications for teacher education in the era of AI, and provides food for thought regarding how to prepare teachers to be resilient enough to navigate uncertainty, confident enough to embrace the unknown, and capable enough to empower their students to do the same.

Yiannis Papargyris 博士是教育管理专业人士，在英语高等教育、教育资格证书开发和语言评估领域拥有超过 20 年的经验。在 PeopleCert，他担任测试开发及质量控制总监，负责开发朗思国际语言测评产品组合并参与相关研究和调研项目。Yiannis拥有伯明翰大学博士学位，并在学校任教多年。他也获得特许教育评估师资格。

The rapid development of artificial intelligence (AI) is reshaping language education, with significant implications for language assessment. AI-powered tools can now generate texts, provide feedback, and simulate communicative interaction, challenging traditional assumptions about what exactly language tests measure and how they should be designed.

This presentation explores the implications of AI for language assessment across three key areas: test design, validity, and assessment practice. First, it examines how the availability of AI tools dictate reconsideration of task design, authenticity, and test integrity. Second, it discusses the impact of AI on validity arguments, including questions about construct representation, test fairness, and the interpretation of scores. Finally, the presentation considers how AI can also enhance assessment practice through automated scoring, item development, and data-informed validation processes, making extensive reference to LanguageCert’s internal development and validation processes.

Drawing on current developments in digital assessment and practical examples from large-scale language testing, the talk highlights both opportunities and risks. It argues that educational assessment professionals should engage critically with AI to ensure that language tests remain valid, meaningful, and relevant in an increasingly AI-mediated communicative environment.