Experimental Evidence on Artificial Intelligence in the Classroom

Bruno Ferman, Lycia Lima and Flavio Riva

MPRA Paper from University Library of Munich, Germany

Abstract: This paper investigates how technologies that use different combinations of artificial and human intelligence are incorporated into classroom instruction, and how they ultimately affect students' outcomes. We conducted a field experiment to study two technologies that allow teachers to outsource grading and feedback tasks on writing practices. The first technology is a fully automated evaluation system that provides instantaneous scores and feedback. The second one uses human graders as an additional resource to enhance grading and feedback quality in aspects in which the automated system arguably falls short. Both technologies significantly improved students' essay scores, and the additional inputs from human graders did not improve effectiveness. Furthermore, the technologies similarly helped teachers engage more frequently on nonroutine tasks that supported the individualization of pedagogy. Our results are informative about the potential of artificial intelligence to expand the set of tasks that can be automated, and on how advances in artificial intelligence may relocate human labor to tasks that remain out of reach of automation.

Keywords: artificial intelligence; technological change; automated writing evaluation; routine and nonroutine tasks (search for similar items in EconPapers)
JEL-codes: I21 I22 I25 (search for similar items in EconPapers)
Date: 2020-11-04
New Economics Papers: this item is included in nep-big, nep-exp and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:103934

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