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Artificial Intelligence Paradigms and the Future of Learning: What a Partial Review of Half a Century of AI Conceptualization Suggests

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Computers in Education 7 - Modulus 2

Tagged Division

Computers in Education

Page Count

21

DOI

10.18260/1-2--36700

Permanent URL

https://peer.asee.org/36700

Download Count

63

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Paper Authors

biography

Joseph Maloba Makokha Stanford University

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Joseph Makokha was born, raised and educated in Kenya. He obtained a BSEE degree from the University of Nairobi before moving to the United States, where he earned two masters degrees in education before starting his doctoral studies in mechanical engineering at Stanford University focussing on design. He researches human collaboration with artificial intelligence (AI), with the goal of understanding how to design AI that augments humans on thinking tasks.

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Abstract

A close examination of Human-Artificial Intelligence (H-AI) research and application shows that there is a wide gap between expectations and implementation outcomes arising from several factors, most importantly the assumptions and guiding principles that creators have about users on one hand, and those of users on the other. In this paper, a review of literature - publications and intellectual property - from the first (1970s) and last (2010s) decades of the past half century was conducted to explore and highlight the driving factors on artificial intelligence (AI) research and application, the resulting implementation, and corresponding response from the AI community. Initial results reveal four general paradigms guiding research and development of AI - replacing the entire human with AI; replacing some part of the human with AI; augmenting the human with AI; and finally keeping AI out of the loop. Each of these paradigms lead to vastly different conceptualization of the human interaction with AI, affecting how we learn from, with, and by AI; what we trust AI to perform; perceptions of AI among different groups; and how well we view AI’s performance relative to our assumptions and expectations. Findings from this review will help practitioners in the human-AI field in recognizing and aligning their paradigms on AI with their goals, leading to better outcomes and fulfilled expectations across the spectrum from researchers to users of AI.

Makokha, J. M. (2021, July), Artificial Intelligence Paradigms and the Future of Learning: What a Partial Review of Half a Century of AI Conceptualization Suggests Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--36700

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