录用日期: 2023-01-06
网络出版日期: 2023-03-01
基金资助
国家自然科学基金项目“面向图形化编程的项目式学习的自动化评价研究及应用”(61977058);上海市科技创新行动计划“人工智能”专项项目“教育数据治理与智能教育大脑关键技术研究与典型应用”(20511101600)
The Core Technology Engine of Digital Transformation in Education: Trustworthy Education Artificial Intelligence
Accepted date: 2023-01-06
Online published: 2023-03-01
教育数字化转型旨在通过数字技术实现教育教学流程再造和提质增效。一方面,以人工智能为代表的数字技术作为教育数字化转型的技术引擎,驱动教育数字化转型持续深入。另一方面,人工智能技术的“黑箱”本质引起了人机信任危机,存在违背教育中的公平、责任、透明、伦理等基本约束的风险,阻碍了教育数字化转型。本研究梳理了人工智能助力教育数字化转型过程中所引发和加剧的四大治理难题,剖析了可信教育人工智能的理论研究和发展现状,提出了可信教育人工智能的基本框架,总结了可信教育人工智能为教育数字化转型带来的机遇,提出了可信教育人工智能促进教育数字化转型的发展建议。研究建议出台可信教育人工智能的相关标准和法规,将技术可信度纳入教育数字化建设评价体系中,深入推进教育数据治理,提升教育从业者的数字素养。
江波 , 丁莹雯 , 魏雨昂 . 教育数字化转型的核心技术引擎:可信教育人工智能[J]. 华东师范大学学报(教育科学版), 2023 , 41(3) : 52 -61 . DOI: 10.16382/j.cnki.1000-5560.2023.03.006
The digital transformation of education aims to reengineer the education and teaching process and improve the quality and efficiency through digital technology. On the one hand, digital technology represented by artificial intelligence as the technical engine of digital transformation of education drives the continuous deepening of digital transformation of education. On the other hand, the “black box” problem of artificial intelligence technology has caused a crisis of human-machine trust, and there is a risk of violating basic constraints such as fairness, accountability, transparency, and ethics in education, hindering the digital transformation of education. This study sorts out the four major governance problems caused and aggravated by artificial intelligence to help the digital transformation of education, analyzes the theoretical research and development status of trustworthy artificial intelligence in education, puts forward the basic framework of trustworthy artificial intelligence in education, summarizes the opportunities brought by trustworthy artificial intelligence in education for the digital transformation of education, and puts forward the development suggestions of trustworthy education artificial intelligence to promote the digital transformation of education. The research recommends the introduction of relevant standards and regulations for trustworthy educational artificial intelligence, incorporating technical credibility into the evaluation system of education digitalization, further promoting educational data governance, and improving the digital literacy of education practitioners.
null | 曹建峰. (2022). 人工智能系统可解释性要求的法律规制. 月旦民商法杂志, (76), 28- 39. |
null | 陈凯泉, 张春雪, 吴玥玥, 刘璐. (2019). 教育人工智能(EAI)中的多模态学习分析、适应性反馈及人机协同. 远程教育杂志, (05), 24- 34. |
null | 但武刚, 李玉婷, 王海福. (2022). 高校教师数字素养框架构建与展望. 教育与教学研究, (09), 41- 53. |
null | 冯永刚, 赵丹丹. (2022). 人工智能教育的算法风险与善治. 国家教育行政学院学报, (07), 88- 95. |
null | 胡姣, 彭红超, 祝智庭. (2022). 教育数字化转型的现实困境与突破路径. 现代远程教育研究, (05), 72- 81. |
null | 黄荣怀. (2022). 加快教育数字化转型 推动学校高质量发展. 人民教育, (Z3), 28- 32. |
null | 金义富. (2017). 区块链+教育的需求分析与技术框架. 中国电化教育, (09), 62- 68. |
null | 荆敏菊. (2015). 中小学生电子产品使用状况及其对心理发展影响与对策的研究综述. 现代教育科学, (04), 77- 79. |
null | 刘晗, 李凯旋, 陈仪香. (2022). 人工智能系统可信性度量评估研究综述. 软件学报, (33), 1- 19. |
null | 刘三女牙. (2022). 人工智能+教育的融合发展之路. 国家教育行政学院学报, (10), 7- 10. |
null | 刘艳红. (2022). 人工智能的可解释性与AI的法律责任问题研究. 法制与社会发展, (01), 78- 91. |
null | 苗逢春. (2022). 教育人工智能伦理的解析与治理——《人工智能伦理问题建议书》的教育解读. 中国电化教育, (06), 22- 36. |
null | 牟智佳, 符雅茹. (2021). 多模态学习分析研究综述. 现代教育技术, (06), 23- 31. |
null | 上海市教育委员会. (2022). 上海宝山: 营造数智生态, 推进课堂转型| 基础教育综合改革典型案例. https://new.qq.com/rain/a/20221208A0A9C200 |
null | 孙波. (2022). 可解释的人工智能: 打开未来智能教育“黑箱”的钥匙. 中国教育信息化, (04), 3- 4. |
null | 孙启贵, 汪琛, 王加宇, 叶斌. (2021). 医疗人工智能发展的社会–技术分析与启示. 自然辩证法研究, (03), 48- 53. |
null | 托雷?霍尔, 曹梦莹, 明芷安, 袁莉. (2022). 可解释人工智能的教育视角: 基于伦理和素养的思考. 中国教育信息化, (04), 5- 13. |
null | 王一岩, 郑永和. (2022). 多模态数据融合: 破解智能教育关键问题的核心驱动力. 现代远程教育研究, (02), 93- 102. |
null | 魏亚丽, 张亮. (2022). 从“基于经验”到“数据驱动”: 大数据时代的教学新样态. 当代教育科学, (02), 50- 56. |
null | 许为. (2022). 八论以用户为中心的设计: 一个智能社会技术系统新框架及人因工程研究展望. 应用心理学, (05), 387- 401. |
null | 杨晓哲, 任友群. (2021). 教育人工智能的下一步——应用场景与推进策略. 中国电化教育, (01), 89- 95. |
null | 于聪, 刘飞. (2022). 人工智能教育应用的伦理风险及其对策研究. 机器人产业, (02), 32- 37. |
null | 余欣, 朝乐门, 孟刚. (2022). 人在回路型AI训练的基本流程与交互模型研究. 情报资料工作, (05), 34- 41. |
null | 翟云, 蒋敏娟, 王伟玲. (2021). 中国数字化转型的理论阐释与运行机制. 电子政务, (06), 67- 84. |
null | 张坤颖, 张家年. (2017). 人工智能教育应用与研究中的新区、误区、盲区与禁区. 远程教育杂志, (05), 54- 63. |
null | 张双志, 张龙鹏. (2020). 教育治理结构创新: 区块链赋能视角. 中国电化教育, (07), 64- 72. |
null | 中国通信院, 京东探索研究院. (2021). 可信人工智能白皮书. http://www.caict.ac.cn/kxyj/qwfb/bps/202107/t20210708_380126.htm |
null | 朱嘉文, 顾小清. (2022). 打通“数据孤岛” 实现数据互联互通. 教育传播与技术, (04), 3- 8. |
null | 祝智庭, 胡姣. (2022a). 教育数字化转型的实践逻辑与发展机遇. 电化教育研究, (01), 5- 15. |
null | 祝智庭, 胡姣. (2022b). 教育数字化转型: 面向未来的教育“转基因”工程. 开放教育研究, (05), 12- 19. |
null | 祝智庭, 彭红超, 雷云鹤. (2018). 智能教育: 智慧教育的实践路径. 开放教育研究, (04), 13- 24+42. |
null | Blikstein, P., & Worsley, M. B. (2016). Multimodal Learning Analytics and Education Data Mining: Using computational technologies to measure complex learning tasks. Journal of learning Analytics, 3, 220- 238. |
null | Brence, F. , & Mauhart, J. (2019). Digital Enablement: Turning Your Transformation Journey into a Successful Journey. https://www2.deloitte.com/content/dam/Deloitte/at/Documents/human-capital/at-digital-enablement-turning-your-transformation-into-a-successful-journey.pdf |
null | Chango, W., Lara, J. A., Cerezo, R., & Romero, C. (2022). A review on data fusion in multimodal learning analytics and educational data mining. WIREs Data Mining and Knowledge Discovery, 12 (4), e1458. |
null | Conati, C., Barral, O., Putnam, V., & Rieger, L. (2021). Toward personalized XAI: A case study in intelligent tutoring systems. Artificial Intelligence, 298, 103503. |
null | Duggan, T., & Corporation, T. (2020). AI in Education: Change at the Speed of Learning. https://iite.unesco.org/wp-content/uploads/2021/05/Steven_Duggan_AI-in-Education_2020-2.pdf. |
null | European Commission, & Directorate-General for Communications Networks, C. and T. (2019). Ethics guidelines for trustworthy AI. Publications Office. |
null | European Commission. (2021). Laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain union legislative acts. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206 |
null | Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Research policy, 31 (8-9), 1257- 1274. |
null | Halevy, A., Norvig, P., & Pereira, F. (2009). The Unreasonable Effectiveness of Data. IEEE Intelligent Systems, 24 (2), 8- 12. |
null | Hanna, N. (2018). A role for the state in the digital age. Journal of Innovation and Entrepreneurship, 7 (1), 5. |
null | IEEE Computer Society. (2022). 2022 Technology Predictions. https://ieeecs-media.computer.org/media/tech-news/tech-predictions-report-2022.pdf |
null | Kizilcec, R. F., & Lee, H. (2020). Algorithmic Fairness in Education. CoRR, abs/2007.05443. |
null | Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A. K., & Tang, J. (2022). Trustworthy AI: A Computational Perspective. ACM Trans. Intell. Syst. Technol. |
null | Stanton, B. , & Jensen, T. (2021). Trust and Artificial Intelligence. https://nvlpubs.nist.gov/nistpubs/ir/2021/NIST.IR.8332-draft.pdf |
null | Trump. (2020). Executive Order on Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government. https://trumpwhitehouse.archives.gov/presidential-actions/executive-order-promoting-use-trustworthy-artificial-intelligence-federal-government/ |
null | UNESCO. (2021). 人工智能伦理问题建议书. https://unesdoc.unesco.org/ark:/48223/pf0000381137_chi |
null | UNESCO. (2022). United Nations Transforming Education Summit——Thematic Action Track 4 on ‘Digital learning and transformation’. https://transformingeducationsummit.sdg4education2030.org/track/digital |
null | Vaggalis, N. (2019). Ethics Guidelines For Trustworthy AI. https://www.i-programmer.info/programming/artificial-intelligence/12702-ethics-guidelin |
null | Wang, X., He, J., Jin, Z., Yang, M., Wang, Y., & Qu, H. (2022). M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis. IEEE Transactions on Visualization and Computer Graphics, 28 (1), 802- 812. |
/
〈 |
|
〉 |