AI in Education: Change at the Speed of Learning（Ⅱ）譯題 《教育中的人工智能：學習速度的變化》（二）

Product by UNESCO IITE （The UNESCO Institute for Information Technologies in Education）聯合國教科文組織教育信息技術研究所

該報告為UNESCO IITE新推出的“教育的數字化轉型”（Digital Transformation of Education）系列出版物的第一期。據悉，“教育的數字化轉型”系列將包括政策簡報、分析報告和反思論文，以探討由于技術的使用及其對教育和人類生活其他領域的影響，教育領域正在發生和正在出現的根本性變化。

1.The Changing Role of Technology in Education

Students and teachers are facing a world of constant， ever-accelerating change. The disparity between the developed and developing world in terms of access to the internet and smart devices was immediately recognised as a ‘Digital Divide; and as technology entered the classroom in schools where the socio-economic conditions allowed for such investments， fears were expressed that disadvantaged students of all nationalities would be left behind.

Conversely， many people held that access to technology and low-cost mobile devices could help close the educational gap caused by failures of the formal education system， and connect those who had left school or never had the opportunity to attend a classroom to rich educational experiences which could address foundational issues such as the global challenge of illiteracy.

In the event， the introduction of devices into the classroom did not bring about a commensurate improvement in exam grades — though it did help to enable new pedagogical modalities such as the flipped classroom — and illiteracy rates remained virtually unchanged. The role of the teacher has been proven paramount， along with the need for adequate professional development supports which incorporate the building of digital skills. Far from technology replacing teachers， the role of the teacher is now recognised as the most important factor in unlocking the potential of technology to advance teaching and learning.

In concert with the emergence of these new technologies was a focus upon what were termed ‘21st century skills. Knowledge-acquisition was relegated in importance， and the ability to work in teams， to collaborate and communicate effectively and to develop analytical and digital skills to better prepare students for the world they would face upon leaving school assumed a primacy in curricular and pedagogical design. Learning how to learn and the importance of lifelong learning were regarded as intrinsically linked to the acquisition of digital skills and experience in the use of basic technologies.

2.Advancements in Technology

2.1 Data， Analytics and Personalized Learning

The promise of technology lays in its ability to support personalised learning pathways： but this could not be achieved through the use of hardware or software alone. To determine whether students were building 21st century skills it would be necessary to measure a variety of data， drawn from disparate sources. The implementation of Student Information Systems and Learning Management Systems provided new digital data sets， but the majority of data pertinent to student progress and performance was still in paper form， and often resided solely in the heads of teachers. Before Artificial Intelligence or Predictive Analytics could be employed to assist in the creation of personalized learning pathways， progress was necessary in how data was gathered， stored， and made accessible.

2.2 Artificial Intelligence

Artificial Intelligence begins with data. The ability to ingest data from multiple data sources， interrogate that data and derive insights — using tools such as predictive analytics and machine learning — is what makes AI such an exciting advancement in education technology. In an educational context， perhaps the best way to view Artificial Intelligence is to view it as Augmented Intelligence. The application of AI provides all stakeholders with additional information and with insights which can better inform decisions.

2.3 Cloud Computing

Perhaps the single most important development in the evolution of Artificial Intelligence has been the shift from what is termed ‘on-premises computing? to Cloud Computing， where users access computing resources through the internet， with a virtually unlimited source of computing power that can increase in line with their needs. These resources include tools and applications like data storage， servers， databases， networking， and software. As long as an electronic device has access to the internet， it has access to the data and the software programs to run it.

2.4 Machine Learning

Machine Learning is an application of Artificial Intelligence which enables computers to act without being explicitly programmed， and to learn and improve from experience without human intervention or assistance. It is used to answer questions that cannot be answered by manual research， and is based on the idea that systems can learn to identify patterns and make decisions and can become more proficient over time， learning from previous computations to produce reliable， repeatable decisions and results.

譯文

1.技術在教育中不斷變化的作用

學生和教師正面臨著一個不斷加速變化的世界。發達國家和發展中國家在互聯網和智能設備接入方面的差距被視為“數字鴻溝”;而社會經濟條件允許進行此類投資的學校畢竟有限，隨著技術進入課堂，人們擔心其他弱勢學校的學生會被拋在后面。

相反，許多人認為使用技術和低成本的移動設備可以幫助縮小因正規教育系統失衡而造成的教育差距，讓那些已經離開學?；驈奈从袡C會進入課堂的人獲得豐富的教育體驗，這可以解決一些基本問題，譬如降低全球文盲率這一艱巨任務。

到頭來，將設備引入課堂并沒有引起成績的相應提高——盡管它確實有助于啟用新的教學模式，例如翻轉課堂——而且文盲率幾乎保持不變。事實證明，課堂上教師的作用至關重要，同時教師需要足夠的專業發展支持，其中包括數字技能的培養。技術遠不能取代教師，教師的角色現在被認為是挖掘技術促進教學潛力的最重要因素。

隨著新技術的出現，人們開始關注所謂的“21世紀技能”。知識獲取的重要性降低，團隊合作、有效合作和溝通以及發展分析和數字技能在課程和教學設計中占據首要地位，為學生在離校后更好地面對世界做好準備。學會學習和終身學習的重要性，被視為與獲得使用基本技術中的數字技能和經驗有著內在聯系。

2.前沿技術

2.1數據分析和個性化學習

技術的前景在于它能支持個性化的學習途徑，但這不能僅僅通過使用硬件或軟件來實現。為了確定學生是否在培養21世紀技能，有必要測量從不同渠道獲得的各種數據。學生信息系統和學習管理系統的實施提供了新的數字數據集，但與學生進步和表現有關的大多數據仍然是紙面形式，而且往往完全由教師負責。在使用人工智能或預測分析來幫助創建個性化學習路徑之前，必須在數據的收集、存儲和訪問方面取得進展。

2.2人工智能

人工智能始于數據。利用預測分析和機器學習等工具從多個數據源獲取數據、查詢數據并得出見解的能力，是人工智能在教育技術方面取得如此令人興奮進步的原因。在教育背景下，也許看待人工智能最好的方法就是將其視為增強智能。人工智能的應用為所有利益相關者提供了額外的信息和見解，這些信息和見解可以更好地為決策提供信息。

2.3云計算

可能人工智能發展最重要的方向，就是從所謂的本地計算向云計算轉變，用戶通過互聯網訪問計算資源，擁有可以根據需要增加的幾乎無限的計算能力。這些資源包括諸如數據存儲、服務器、數據庫、網絡和軟件等工具和應用程序。只要電子設備能夠訪問互聯網，它就可以通過訪問數據和軟件程序來運行。

2.4機器學習

機器學習是人工智能的一種應用，它使計算機能夠在沒有明確編程的情況下自行操作，并在沒有人為干預或幫助的情況下從經驗中學習、改進。它用于回答人工研究無法回答的問題，并且基于這樣一種理念——系統可以學習識別模式和做出決策，而且能從以前的計算中學習，并隨著時間的推移變得更加精通，從而產生可靠的、可重復的決策和結果。