1. 顾曰国

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科学家，中国社会科学院创新工程首席研究员。兼任中国英汉语比较研究会语言智能教学专业委员会（ChinaCALL）主任，北京外国语大学网络教育学院荣誉院长。学术荣誉和兼职包括：CSSCI刊物《当代语言学》杂志主编，国际语用学协会常务理事，中国功能语言学协会常务理事，国际著名SSCI刊物《篇章学》、《语用学》（国际语用学协会会刊）咨询编审，英国学术院王宽城基金会院士，香港理工大学校外学术委员和学术顾问，教育部远程教育专家组成员。

【发言题目】

A Post-20 Year Look into the Future（北外网院未来20年展望）

【发言摘要】

The Institute of Beiwai Online Education (IBOE) was officially endorsed as a second batch of higher institutions piloting online education in China. 20 years have gone by. What have we learned from this piloting phase of practising online education? As the IBOE founding dean, I am privileged as well as obliged to reflect upon the eventful years to see if there have been achievements, if any, and regrets — personally quite a few. My optimistic personality motivates a focus this paper places on the IBOE future. What will IBOE look like in another 20 years, that is, when it reaches the year 2040? There will, to be sure, many unpredictables that lay on its way. The question I put to myself is: What will I be able to contribute to its future development, that is, if I can live that long? My present thinking is this: I’ll work on institutional knowledge ontology and management which, being the core of AI conceived by its founding pioneers, provides a key to its future success.

2. Bill Rivers

Bill Rivers博士，语言行业咨询公司WP Rivers & Associates负责人。曾担任美国国家语言联合委员会（JNCL）执行董事八年。在文化和语言促进经济发展和国家安全方面拥有30年以上的工作经验，发表成果涵盖第二和第三语言习得研究、水平测试、项目评估以及语言政策制定等领域。其他职务包括：ASTM技术委员会F43语言服务和产品前任主席和创始主席，ISO技术委员会232教育和学习服务美国技术顾问组主席，美国艺术与科学院美洲语言工作组成员以及美国语言行业协会荣誉会员。

【发言题目】

The Role of AI in Language Learning（人工智能在语言学习中的角色）

【发言摘要】

Can Artificial Intelligence Improve Language Learning? While language teaching has always been an early adopter of technology – witness the use of LP records in the 1950s, and the extensive adoption of language labs in universities – the revolution in personal device computing, coupled with significant advances in neural network modeling, creates a new set of affordances for language learning and teaching, namely, the potential application of Artificial Intelligence to language learning. At the same time, clear trends in the role of AI and education overall point to significant potential limitations for AI and language learning (Klein, 2019). While receptive skills - reading and listening - have seen tremendous advances in the use of computer assisted language applications, especially on mobile devices (Golonka et al., 2014), interactive skills lag far behind. However, AI has shown promise in the development of personalized tutors (Goren et al., 2016; Suvorov) and adaptive testing for language learning. This presentation discusses recent trends of AI in the context of the revolution of blended learning for languages.  查看更多>>

3. Agnes Kukulska-Hulme

Agnes Kukulska-Hulme教授，英国开放大学教育技术学院教授、博导，未来学习（Future Learning）项目负责人。专注于远程教育、移动学习和语言学习研究。曾任国际移动学习协会主席，现任ReCALL、System、RPTEL、International Journal of Mobile and Blended Learning等知名国际期刊编委。担任联合国教科文组织、英国文化教育协会、英联邦学习共同体（COL）、国际英语教育研究基金会（TIRF）以及剑桥大学出版社等机构教育政策和实践指南主要撰写人。多个国家访问学者，受邀在国际会议中作主旨发言或特邀报告百余次。

【发言题目】

Smart Mobile Assistance in Language Learning（智能移动技术辅助语言学习）

【发言摘要】

Learning a second or foreign language is a challenging endeavour which requires various degrees of support. Alongside existing resources and tools such as mobile apps, the emergence of smart technologies including chatbots and conversational agents has the potential to assist language learners wherever they may be. However, whilst a growing number of researchers and developers are working on such intelligent assistants across different disciplines, little is known about their application in language learning. The concept of ‘smart mobile assistance’ seeks to draw together the ways in which such intelligent tools on mobile, wearable and portable devices can provide instantaneous or ongoing support to language learners inside or outside the classroom. Smart assistants might answer questions, support study habits and extend opportunities for language learning and language practice. It is argued that they can support beneficial approaches including self-directed and reflective learning. The keynote will map out this emerging territory, consider some findings from recently analysed research literature, and highlight implications for educators wishing to embrace such developments in their teaching practice.

4. 苗逢春

苗逢春博士，联合国教科文组织总部教育信息化与人工智能教育应用部门主任，负责的项目领域涵盖教育信息化政策制定、人工智能与教育、教师和学生的数字技能培养、开放教育资源（OER）、移动学习以及未来电子学校等。苗博士还负责联合国教科文组织的教育信息化奖。苗博士的主要工作成就包括：发起并连续举办九届联合国教科文组织“移动学习周”（Mobile Learning Week）；制定并通过了关于通过教育信息化实现可持续发展目标4（SDG 4）（教育2030议程）的《青岛宣言》；制定并通过有关人工智能与教育的第一个国际间共识《北京共识》；直接培训指导60多个国家教育部制定国家教育信息化政策和开放教育资源政策。在加入联合国教科文组织之前，苗博士曾担任教育部全国中小学计算机教育研究中心（北京部）主任、中国教育学会中小学信息技术教育专业委员会常务副理事长、秘书长。

【发言题目】

Artificial Intelligence and Education: What Teachers Need to Know（人工智能与教育：教师需要知道什么）

【发言摘要】

I. A framework on interplays between AI and education

A. How to ensure ethical, inclusive & equitable use of AI in education?

B. How can education prepare humans to live and work with AI?

C. How can AI be leveraged to enhance or reinvent education?

II. A matrix to analyse emerging issues relating to AI and education based on three key layers
Digital World (Data + Algorithm + Computing); Interface (Apps + Devices); Education, Teaching, and Learning (Use Cases)

III. An outline of AI in education use cases
For each of the following use cases, examples are provided followed by a fundamental question relating to the topic.
1) the use of big data, AI tools to advance inclusive and equitable access to education or digital opportunities
Can Al help level the playing field of accessing to learning resources and digital opportunities?
2) the use of big data, data collection and processing technology to upgrade the EMIS
How can data across platforms and social media be drawn to inform policy planning and decision making in education?
3) the use of AI to recognize learning patterns and enable personalized high-quality learning
Can algorithms precisely diagnose personal learning problems and provide precise solutions?
4) the use of AI for subject-specific or interdisciplinary learning
Can algorithms precisely diagnose personal learning problems and provide precise solutions?
5) the use of AI for innovative or new forms of learning
Will the AI-driven future of learning be an evolution inside-out of education systems?
6) the use of AI for development of creativity and/or critical thinking
How far can AI go to stimulate humans’ creativity in art areas and in solving real problems?
7) human-machine dual teacher models to empower teachers
Teachers are irreplaceable in developing humans’ personal capital. How will teachers’ competencies be redefined and developed for the AI era?
8) AI-powered lifelong learning companions
Can AI help recognize with less bias learning outcomes obtained from varied lifelong and life-wide learning settings?  查看更多>>

5. Carolyn Rosé

Carolyn Rosé博士，美国卡内基梅隆大学语言技术与人机交互专业教授，前国际学习科学协会（International Society of the Learning Sciences）主席，教育领域顶级SSCI期刊《计算机支持的协作学习（ijCSCL）》联合主编。主要研究聚焦于更好地理解会话的社会和语用本质，并利用这种理解来构建计算系统，提高人与人以及人与计算机之间的对话效力。Rosé博士的研究团队具有突出的跨学科特色，已发表同行评审的学术成果超过250项，在语言技术、学习科学、认知科学、教育技术及人机交互五个研究领域均处于国际领先地位。

【发言题目】

Supporting Learning During Collaborative Work: From Text Based Chat to a Smart Office Space（协作学习支持演进——从文本会话到智能办公空间的协作）

【发言摘要】

Building on over a decade of AI-enabled collaborative learning experiences in the classroom and online, this talk report on classroom studies in large online software courses with substantial teamwork components. Project courses are believed to be valuable experiences for students to engage in reflection on concepts while applying them in practice. In our classroom work, we have adapted an industry standard team practice referred to as Mob Programming into a paradigm called Online Mob Programming (OMP) for the purpose of encouraging teams to reflect on concepts and share work in the midst of their project experience. At the core of this work are process mining technologies that enable real time monitoring and just-in-time support for learning during productive work. This work builds on a foundation in text-based interaction, and transitions into multi-modal support for collaborative learning in face-to-face collaboration.