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Improvement Science

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Description

The article presents Improvement Science (Bryk et al., 2015) as a continuous-improvement framework for AIEd, stating that "Improvement Science is guided by the following six principles": making work problem-specific and user-centered, treating variation in performance as the core problem, seeing the system that produces current outcomes, embedding measures of key outcomes, anchoring improvement in disciplined inquiry through PDSA cycles, and accelerating improvement through networked communities. It argues these principles develop user ownership needed to adapt AIEd to local contexts and address FATE issues that arise in practice.

Accounts

  • Six principles of Improvement Science as a framework for pursuing FATE in AIEd: The article presents Improvement Science (Bryk et al., 2015) as a continuous-improvement framework for AIEd, stating that "Improvement Science is guided by the following six principles": making work problem-specific and user-centered, treating variation in performance as the core problem, seeing the system that produces current outcomes, embedding measures of key outcomes, anchoring improvement in disciplined inquiry through PDSA cycles, and accelerating improvement through networked communities. It argues these principles develop user ownership needed to adapt AIEd to local contexts and address FATE issues that arise in practice. (Bhimdiwala et al. (2022))

Claims

Key Sources

  • Bhimdiwala, A., Neri, R. C., & Gomez, L. M. (2022). Advancing the Design and Implementation of Artificial Intelligence in Education through Continuous Improvement. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-021-00278-8
Page metadata
Field Value
type research-method
id improvement-science
title Improvement Science
description The article presents Improvement Science (Bryk et al., 2015) as a continuous-improvement framework for AIEd, stating that "Improvement Science is guided by the following six principles": making work problem-specific and user-centered, treating variation in performance as the core problem, seeing the
status review
generated by process:settle-candidates on 2026-10-27
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