TY - JOUR
T1 - Artificial intelligence for education
T2 - Knowledge and its assessment in AI-enabled learning ecologies
AU - Cope, Bill
AU - Kalantzis, Mary
AU - Searsmith, Duane
N1 - Over the past ten years, we have been developing software and evaluating these propositions about the potentials of artificial intelligence in educational settings though a series of research and development projects supported by the Institute of Educational Sciences, the Bill and Melinda Gates Foundation, and the National Science Foundation. The result has been to create, test and research the effectiveness of CGScholar (Common Ground Scholar), a suite of web social knowledge applications. In 2019, CGScholar had nearly 200,000 user accounts. Parts of the software suite are open to anyone for others to sign up and use at no charge; other parts have a modest licensing fee based on self-sustainability principles and managed by Common Ground Research Networks, a not-for-profit public benefit corporation based in the Research Park at the University of Illinois. Among others, use cases for CGScholar range from: literacy in schools between grades 4 to 12; to higher education, including education, engineering, medicine, and veterinary medicine courses; to global social learning interventions by the Red Cross and the World Health Organization.
PY - 2021
Y1 - 2021
N2 - Over the past ten years, we have worked in a collaboration between educators and computer scientists at the University of Illinois to imagine 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.
AB - Over the past ten years, we have worked in a collaboration between educators and computer scientists at the University of Illinois to imagine 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.
KW - Artificial intelligence
KW - assessment
KW - e-learning
KW - pedagogy
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U2 - 10.1080/00131857.2020.1728732
DO - 10.1080/00131857.2020.1728732
M3 - Article
AN - SCOPUS:85079717342
SN - 0013-1857
VL - 53
SP - 1229
EP - 1245
JO - Educational Philosophy and Theory
JF - Educational Philosophy and Theory
IS - 12
ER -