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    人工智能驱动教育技术发展的中德视角 ——2019年中德双边研讨会综述

    A Sino-German Perspective on AI-driven Development of Educational Technology:A Review of Sino-German Bilateral Symposium

【作      者】:

刘三女牙, 柴唤友, 刘盛英杰, 黄山云, 胡天慧, 刘 智


【关 键 词 】:

中德科学中心; 人工智能+教育; 学习分析; 智能教育; 交互式人工智能


【栏      目】:

历史与国际比较


【中文摘要】:

由中德科学中心资助、华中师范大学教育大数据应用技术国家工程实验室承办的GZ1531国际学术研讨会于3月25日至29日在中国武汉华中师范大学召开。研讨会的主题是“人工智能驱动教育技术发展的中德视角”,契合了近年来教育领域和计算机领域的专家学者在“人工智能+教育”中的深度融合和创新发展方面所做的努力。与会嘉宾围绕“人工智能+教育”的深度认知(教育场景下人工智能的优劣势和可融入层次、机遇和挑战、理解视域)和应用案例(自适应学习系统的开发设计、基于新技术的“人工智能+教育”应用、“人工智能+教育”的数据采集与分析)作了精彩报告,并就中德双方未来的研究项目和学术合作(中德“人工智能+教育”白皮书的制定、自适应学习中的学习推荐和干预方法、基于多模态数据的学习分析与智能学习同伴研发)展开了深入研讨。对上述主题研究成果及进展的集中呈现和探讨,将有助于推动“人工智能+教育”的未来研究和应用实践。


【英文摘要】:

Sponsored by Sino-German Science Center and organized by National Engineering Laboratory for Educational Big Data in Central China Normal University (CCNU), the international academic seminar (Project number: GZ1531) was held in CCNU from March 25 to 29 in Wuhan, China. The theme of the seminar was "The Sino-German Perspective on AI-driven Educational Technology", which was in line with the efforts made by experts and scholars in the field of education and computers in recent years in the deep integration and innovative development of "AI plus education". Participants conducted in-depth studies on deep understanding and practical cases in this field, and discussed deeply on future research projects and academic cooperation between China and Germany. On the aspect of deep understanding, participants demonstrated the advantages and disadvantages, opportunities and challenges, and horizons of understanding of AI in the context of education. Regarding practical cases, it called attention to the development and design of adaptive learning system, application of new technology-based "AI plus education", data collection and analysis of "AI plus education". As for the potential cooperation between China and Germany, participants highlighted the establishment of the white paper of "AI plus education" between China and Germany, the learning recommendation and intervention methods in adaptive learning, the multi-modal data-based learning analytics, and the development of intelligent learning partners. The presentation and discussion of the above research results and progress will promote the future research and practical application of "AI plus education".

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