Evidence map›Paper›PMID 36590861›Full record

ArticleFrontiers in artificial intelligence2022

The Turing Teacher: Identifying core attributes for AI learning in K-12.

Alexander Pelaez, Amal Jacobson, Kara Trias, Elaine Winston

Open access · goldAbstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
2.8field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 13 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Alexander PelaezInformation Systems and Business Analytics, Hofstra University, Hempstead, NY, United States.
Amal JacobsonProgressive School of Long Island, Merrick, NY, United States.
Kara Trias5E Analytics LLC, Merrick, NY, United States.
Elaine WinstonInformation Systems and Business Analytics, Hofstra University, Hempstead, NY, United States.
Hofstra University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Artificial intelligence in the educational domain has many uses; however, using AI specifically to enhance education and teaching in a K-12 environment poses the most significant challenges to its use. Beyond usage and application, the quality of the education is made even more arduous due to the dynamics of teaching primary and secondary school children, whose needs far exceed mere fact recollection. Utilizing prior research using AI in education and online education in the K-12 space, we explore some of the hurdles that AI applications face in K-12 teaching and provide core attributes for a "Turing Teacher," i.e., an AI powered technology for learning, specifically targeting the K-12 space. Methods: Using a survey, which included qualitative responses during the implementation of online learning during the Covid Pandemic, we analyze the results using univariate and multivariate tests and analyzed the qualitative responses to create core attributes needed for AI powered teaching technology. Results: The results present the challenges faced by any technology in an education setting and show that AI technology must help overcome negative feelings about technology in education. Further, the core attributes identified in the research must be addressed from the three stakeholder perspectives of teachers, parents and students. Discussion: We present our findings and lay the groundwork for future research in the area of AI powered education. The Turing Teacher must be able to adapt and collaborate with real teachers and address the varying needs of students. In addition, we explore the use of AI technology as a means to close the digital divide in traditionally disadvantaged communities.

Indexed as

artificial intelligence (AI)educationefficacyexecutive functionK-12

Identifiers

PMID36590861
PMCPMC9794853
OpenAlexW4311495584

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.