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Outcome-oriented equity framework distinguishing access equity from impact equity

Research Method · All research methods
Evidence · 2 claims (2 for) · 2 studies (2 theoretical), q1–q2 · 0 of 2 report an effect size · 2 claims rest on one study

Description

The paper distinguishes access equity, whether students and institutions can obtain devices, broadband, software, and support, from impact equity, whether AI-enabled learning produces comparable gains across subgroups. It argues that 'Access without learning gains can reproduce inequality in a more technologically sophisticated form, while learning gains observed only in average outcomes can obscure subgroup harms.' Evaluation should use disaggregated evidence on learning gains, transfer, persistence, and confidence calibration.

Accounts

  • Outcome-oriented equity framework distinguishing access equity from impact equity: The paper distinguishes access equity, whether students and institutions can obtain devices, broadband, software, and support, from impact equity, whether AI-enabled learning produces comparable gains across subgroups. It argues that 'Access without learning gains can reproduce inequality in a more technologically sophisticated form, while learning gains observed only in average outcomes can obscure subgroup harms.' Evaluation should use disaggregated evidence on learning gains, transfer, persistence, and confidence calibration. (Dongming Mei et al. (2026))

Claims

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Key Sources

  • Dongming Mei, Katherine Moore, and Ben Sayler. (2026). Preparing Students for AI-Powered Materials Discovery: A Workflow-Aligned Framework for AI Literacy, Equity, and Scientific Judgment. https://arxiv.org/abs/2605.09624
Page metadata
Field Value
type research-method
id outcome-oriented-equity-framework-distinguishing-access-equity-from-impact-equity
title Outcome-oriented equity framework distinguishing access equity from impact equity
description The paper distinguishes access equity, whether students and institutions can obtain devices, broadband, software, and support, from impact equity, whether AI-enabled learning produces comparable gains across subgroups.
status review
generated by process:settle-candidates on 2026-10-10
trust unverified — no one has confirmed this page's content

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