Product by OECD （Organization for Economic Co-operation and Development） 經济合作与发展组织

该报告向我们介绍了应用于教育领域的前沿技术，包括人工智能、机器人和区块链等，并重点讨论了这些智能技术如何为远程教育带来更丰富的教学模式和更系统的管理方法。

Smart technologies at the organisation and system levels

Smart technologies powered by AI and learning analytics also allow for the management of education organisations. They can be used for a variety of purposes; for example， to enhance an institution’s curriculum based on an analysis of students’ learning and study paths. While this is still a nascent trend， a whole-of-organisation adoption of learning analytics can transform educational institutions’ culture.

Early warning systems that identify students at risk of dropping out from high school are a good use of the administrative micro-data that are increasingly being collected by education systems and organisations. While identifying a good set of early warning indicators remains difficult， a few systems have shown a high level of accuracy and enriched thinking about the reasons students drop out. In order to avoid the risks of student profiling， open and transparent algorithms are important.

Game-based standardised assessments also build on smart technologies and smart data analysis techniques to expand assessment to skills that cannot be easily measured by traditional （paper-and-pencil or computer-based） tests. These include higher-order skills （e.g. creativity） or emotional and behavioural skills （e.g. collaboration， behavioural strategy）. Game-based tests may analyse eye-tracking data and audio recording， and process natural language and information such as time-on-task or use simulations.

Finally， as a “verification infrastructure”， blockchain technology opens new avenues for credentialing in education and training. Blockchain technology enables the validation of claims about an individual or institution， including their characteristics and qualifications， and to do this instantly and with a very high level of certainty. This helps eliminate diploma （and other records） fraud， facilitates the movement of learners and workers between institutions and geographies， and empowers individuals by giving them increased control over their own data. Many blockchain initiatives are underway across the world， which may transform how education and lifelong learning systems manage degrees and qualifications.

Policy pointers

There are good reasons to believe that smart technologies can contribute to the effectiveness， equity and cost-efficiency of education systems. At the same times， there are a few important aspects of smart technologies to keep in mind to reap those benefits：

·Smart technologies are human-AI hybrid systems. Involving end users in their design， giving control to humans for important decisions， and negotiating their usage with society in a transparent way is key to making them both useful and socially acceptable.

·Smart technologies support humans in many different ways without being perfect. Transparency about how accurate they are at measuring， diagnosing or acting is an important requirement. However， their limits should be compared to the limits of human beings performing similar tasks.

·More evidence about effective pedagogical uses of smart technologies in and outside of the classroom as well as their uses for system management purposes should be funded without focusing on the technology exclusively. Criteria for this evidence to be produced quickly could also be developed.

·The adoption of smart technologies relies on robust data protection and privacy regulation based on risk assessment but also ethical considerations where regulation does not exist. For example， there is mounting concern about the fairness of algorithms， which could be verified through “open algorithms” verified by third parties.

·Smart technologies have a cost， and cost-benefit analysis should guide their adoption， acknowledging that their benefits go beyond pecuniary ones. In many cases， the identification of data patterns allows for better policy design and interventions that are more likely to improve equity or effectiveness. Policy makers should also encourage the development of technologies that are affordable and sustainable thanks to open standards and interoperability.

譯文

组织和系统管理层面的智能技术

以人工智能和学习分析为动力的智能技术也可以用来管理教育组织机构。它们可用于多种用途，例如，根据对学生学习和学习路径的分析优化改进机构的课程设置。尽管这仍然是一个新生趋势，但在整个教育组织中采用学习分析可以改变教育机构的文化。

早期预警系统可以很好地利用教育系统和组织所收集的行政微观数据，从中识别出有高中辍学风险的学生。虽然确定一套完备的早期预警指标尚且存在困难，但一些系统已经显示出了高水平的准确性，并丰富了针对学生退学原因的思考。为了避免出现学生分析出错的风险，公开和透明的算法显得很重要。

基于游戏的标准化评估也建立在智能技术和智能数据分析技术的基础之上，将评估扩展到传统测试（纸笔或基于计算机）无法轻易衡量的技能。这些技能包括更高层次的技能（例如创造力）或情感以及行为技能（例如协作、行为策略）。基于游戏的测试可以分析眼球追踪数据和音频记录，并处理自然语言和信息，例如任务时间或使用模拟。

最后，作为一种“验证基础设施”，区块链技术为教育和培训领域的认证开辟了新途径。区块链技术使得对个人或机构声明（包括其特征和资质）的确认成为可能，不仅可以立即得出结论，而且具有高度确定性。这有助于消除文凭（和其他记录）造假，促进学习者和工作者在机构和地区之间的流动，并通过对自己数据的控制来提高个人的能力。目前，区块链项目正在世界各地开展，这可能会改变教育和终身学习系统管理学位和资格证书的方式。

政策指南

我们有充分的理由相信，智能技术可以促进教育系统的有效性、公平性和成本效益。同时，要获得这些好处，需要牢记智能技术的几个重要方面。

智能技术是人与人工智能的混合系统。让最终用户参与到系统设计中，将重要决策的控制权交给人类，并以公开透明的方式与社会保持协商，是使智能技术既发挥作用又为社会所接受的关键。

智能技术虽然能够以许多不同的方式支持人类，但并不完美。透彻地了解它们在测量、诊断或行动方面的准确程度是一项重要要求。然而，它们的极限应该与人类执行类似任务的极限进行比较。

更多关于智能技术在课堂内外的有效教学应用以及它们用于系统管理目的的证据应该得到资助，而不应该仅仅关注该技术。还可以制定出快速生成这些证据的标准。

智能技术的采用依赖基于风险评估的、健全的数据保护和隐私监管，但在没有监管的情况下也需要考虑道德因素。例如，算法的公平性正越来越受到人们关注，它可以通过第三方验证的“开放算法”进行验证。

智能技术是有成本的，成本效益分析应该指导它们的应用，承认它们提供的好处已经超越了金钱。在许多情况下，数据模式的确定有助于更好地设计政策和干预措施，从而更有可能提高公平性或有效性。除此之外，政策制定者还应鼓励开发那些由于开放标准和互操作性而可以负担得起并且可持续的技术。