AI evidence extraction

State of the art and practice in AI in education

W. Holmes, Ilkka Tuomi · European Journal of Education · 2022

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

5/10

Relevance

1/4

Quality (LMQS)

Evidence

758

Citations

130.40

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1111/ejed.12533

Methodology & findings

Study design

Literature review with typology development; systematic examination of existing AI systems in education and their pedagogic/educational assumptions; conceptual analysis and categorization of AIED approaches..

Main result

The paper identifies that "Recent developments in Artificial Intelligence (AI) have generated great expectations for the future impact of AI in education and learning (AIED). Often these expectations have been based on misunderstanding current technical possibilities, lack of knowledge about state‐of‐the‐art AI in education, and exceedingly narrow views on the functions of education in society." The authors develop a typology of AIED systems and describe different ways of using AI in education grounded in different interpretations of what AI and education is or could be.

Reports effect sizes.

Research paradigm

Interpretive/critical analysis of AI in education systems and their philosophical/pedagogical assumptions

Author conclusions

The authors conclude that "we provide a review of existing AI systems in education and their pedagogic and educational assumptions" and develop "a typology of AIED systems and describe different ways of using AI in education and learning, show how these are grounded in different interpretations of what AI and education is or could be, and discuss some potential roadblocks on the AIED highway."

Risk of bias

Selection bias in which AIED systems are reviewed (depends on authors' access and choice of literature sources); Potential publication bias favoring published systems over unpublished/failed implementations; Interpretive bias in categorizing systems within the developed typology

Open questions raised

The paper identifies gaps in understanding between current technical AI possibilities and expectations; lack of knowledge about state-of-the-art AIED; need for broader conceptualizations of education's functions in society beyond narrow technical implementations.
The paper identifies misunderstandings about current technical possibilities in AI, insufficient knowledge about state-of-the-art AI in education, and overly narrow conceptualizations of education's societal functions as gaps in the field.
The paper identifies a gap between expectations for AI in education and actual technical possibilities, suggesting a need for better understanding of state-of-the-art AI in education and more nuanced views on the functions of education in society.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 79%

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