TY - GEN
T1 - Enduring Questions, Innovative Technologies
T2 - Computing Conference, 2021
AU - Papa, Rosemary
AU - Jackson, Karen Moran
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - This paper aims to tie literature in AI to enduring questions in education about teaching and learning and discern ethical considerations that define those ties. The challenge was to answer the question: how do we merge our learning and leadership theories to technologies and the algorithmic biases that may maintain today's social injustices into our future? The paper first reviews the literature to identify the dialogue on AI by computer scientists in relation to enduring questions in education, learning theories, and ethics. Then we summarize data in the form of vignettes written by experts from the humanities, computer science, and social sciences. Some of the vignettes focused on how educational and technological systems are products of the social system and the ethical implications of such connections. Other writings centered data-driven approaches to incorporating AI technologies in classrooms, with concerns around uneven implementation and differential access. The paper concludes that to dialogue with educators AIED will need to move away from discussions of efficiency as measured by educational assessments and incorporate humanistic and social learning theories that embrace the complexities of human relationships. Developers should seek to work directly with educational leaders to establish optimal teaching strategies for the ethical ‘good’ of the learner, while attending to social justice parameters. Equally critical is the need to create ethical parameters between the AI and the student.
AB - This paper aims to tie literature in AI to enduring questions in education about teaching and learning and discern ethical considerations that define those ties. The challenge was to answer the question: how do we merge our learning and leadership theories to technologies and the algorithmic biases that may maintain today's social injustices into our future? The paper first reviews the literature to identify the dialogue on AI by computer scientists in relation to enduring questions in education, learning theories, and ethics. Then we summarize data in the form of vignettes written by experts from the humanities, computer science, and social sciences. Some of the vignettes focused on how educational and technological systems are products of the social system and the ethical implications of such connections. Other writings centered data-driven approaches to incorporating AI technologies in classrooms, with concerns around uneven implementation and differential access. The paper concludes that to dialogue with educators AIED will need to move away from discussions of efficiency as measured by educational assessments and incorporate humanistic and social learning theories that embrace the complexities of human relationships. Developers should seek to work directly with educational leaders to establish optimal teaching strategies for the ethical ‘good’ of the learner, while attending to social justice parameters. Equally critical is the need to create ethical parameters between the AI and the student.
KW - AIED
KW - Algorithmic bias
KW - Artificial intelligence
KW - Educational technologies
KW - Ethics
KW - Learning theories
UR - http://www.scopus.com/inward/record.url?scp=85112682555&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112682555&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-80126-7_51
DO - 10.1007/978-3-030-80126-7_51
M3 - Conference contribution
AN - SCOPUS:85112682555
SN - 9783030801250
T3 - Lecture Notes in Networks and Systems
SP - 725
EP - 742
BT - Intelligent Computing - Proceedings of the 2021 Computing Conference
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 15 July 2021 through 16 July 2021
ER -