Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies

被引:109
|
作者
Cope, Bill [1 ]
Kalantzis, Mary [1 ]
Searsmith, Duane [1 ]
机构
[1] Univ Illinois, Coll Educ, Champaign, IL 61820 USA
基金
美国国家科学基金会;
关键词
Artificial intelligence; e-learning; pedagogy; assessment; INSTRUCTION; SUPPORT;
D O I
10.1080/00131857.2020.1728732
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Over the past ten years, we have worked in a collaboration between educators and computer scientists at the University of Illinois to image futures for education in the context of what is loosely called "artificial intelligence." Unhappy with the first generation of digital learning environments, our agenda has been to design alternatives and research their implementation. Our starting point has been to ask, what is the nature of machine intelligence, and what are its limits and potentials in education? This paper offers some tentative answers, first conceptually, and then practically in an overview of the results of a number of experimental implementations documented in greater detail elsewhere. Our key finding is that artificial intelligence-in the context of the practices of electronic computing developing over the past three quarters of a century-will never in any sense "take over" the role of teacher, because how it works and what it does are so profoundly different from human intelligence. However, within the limits that we describe in this paper, it offers the potential to transform education in ways that-counterintuitively perhaps-make education more human, not less.
引用
收藏
页码:1229 / 1245
页数:17
相关论文
共 50 条
  • [21] Generative Artificial Intelligence in Education and Its Implications for Assessment
    Mao, Jin
    Chen, Baiyun
    Liu, Juhong Christie
    [J]. TECHTRENDS, 2024, 68 (01) : 58 - 66
  • [22] Generative Artificial Intelligence in Education and Its Implications for Assessment
    Jin Mao
    Baiyun Chen
    Juhong Christie Liu
    [J]. TechTrends, 2024, 68 : 58 - 66
  • [23] Artificial intelligence (AI) in blended learning
    Garner, Brian J.
    Tsui, Eric
    Lukose, Dickson
    [J]. KNOWLEDGE-BASED SYSTEMS, 2009, 22 (04) : 247 - 248
  • [24] AI-Enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography
    Puyol-Anton, Esther
    Ruijsink, Bram
    Sidhu, Baldeep S.
    Gould, Justin
    Porter, Bradley
    Elliott, Mark K.
    Mehta, Vishal
    Gu, Haotian
    Rinaldi, Christopher A.
    Cowie, Martin
    Chowienczyk, Phil
    Razavi, Reza
    King, Andrew P.
    [J]. SIMPLIFYING MEDICAL ULTRASOUND, ASMUS 2022, 2022, 13565 : 75 - 85
  • [25] Fast Learning for Dynamic Resource Allocation in AI-Enabled Radio Networks
    Qureshi, Muhammad Anjum
    Tekin, Cem
    [J]. IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2020, 6 (01) : 95 - 110
  • [26] AI-Enabled Autonomous Drones for Fast Climate Change Crisis Assessment
    Hernandez, Daniel
    Cano, Juan-Carlos
    Silla, Federico
    Calafate, Carlos T.
    Cecilia, Jose M.
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (10): : 7286 - 7297
  • [27] Conflicting roles for humans in learning health systems and AI-enabled healthcare
    Kasperbauer, T. J.
    [J]. JOURNAL OF EVALUATION IN CLINICAL PRACTICE, 2021, 27 (03) : 537 - 542
  • [28] A comprehensive overview of AI-enabled music classification and its influence in games
    Yang, Tiancheng
    Nazir, Shah
    [J]. SOFT COMPUTING, 2022, 26 (16) : 7679 - 7693
  • [29] A comprehensive overview of AI-enabled music classification and its influence in games
    Tiancheng Yang
    Shah Nazir
    [J]. Soft Computing, 2022, 26 : 7679 - 7693
  • [30] AI-Enabled Smart Healthcare Ecosystem Model and Its Empirical Research
    Du, Qianrui
    Cao, Changlin
    Liao, Qichen
    Ye, Qiongwei
    [J]. HCI IN BUSINESS, GOVERNMENT AND ORGANIZATIONS, PT II, HCIBGO 2023, 2023, 14039 : 130 - 139