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AI-Enabled EdTech Adoption Cycle

Product or Programme · All products and programmes
Evidence · 1 claim (1 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

A four-phase adoption framework described by Ruiz et al. for guiding education systems through vision and planning, implementation, ongoing evaluation, and scaling and sustaining of AI-enabled edtech.

Components

  • AI-Enabled EdTech Adoption Cycle (vision and planning, implementation, ongoing evaluation, scaling and sustaining): Appendix 1 of the brief outlines a four-phase adoption cycle for AI-enabled edtech: vision and planning, implementation, ongoing evaluation, and scaling and sustaining, each with key challenges and recommendations. It states that "Planning for AI-enabled edtech requires districts to move beyond reactive purchasing decisions and establish strategic frameworks that align technology with clear learning objectives and pedagogical goals." AI-enabled tools require specialized evaluation criteria including algorithmic transparency, bias mitigation, and data privacy. (Ruiz et al. (2025))
  • Four-phase AI-enabled edtech adoption cycle for districts: The article organizes district adoption of AI-enabled edtech into a cycle of four phases: vision and planning, implementation, ongoing evaluation, and scaling and sustaining. Each phase pairs a goal statement with key challenges and recommendations, e.g. planning requires districts to "move beyond reactive purchasing decisions and establish strategic frameworks that align technology with clear learning objectives and pedagogical goals." The cycle frames adoption as continuous rather than a one-time purchase. (Ruiz et al. (2025))
  • Six-step edtech procurement process from needs analysis to sustain-and-scale: The guide structures district edtech procurement as six steps: Analyze & Inventory, Discover & Decide, Design & Plan, Mobilize & Implement, Evaluate & Reflect, and Sustain & Scale. Each step frames key questions and points to practical resources and templates. The guide says the steps "represent a comprehensive process that districts and schools can engage with to discover, pilot, and procure edtech tools to meet a given need," grounding the articulated need in data before exploring solutions and returning to Discover & Decide if a pilot shows a tool is not the right fit. (Digital Promise (2026))
  • EdTech Pilot Navigator with four pilot models (Resource E): Resource E is a quick-reference guide and planning template for choosing a pilot type before formal procurement. It describes four models: a Large-Scale Pilot Study getting more than 350 students to use the tool and measuring outcomes, useful for detecting differences in effects for special populations; a Small-Scale Pilot Study with a smaller group when conditions must be carefully controlled; Live Testing, where students practice using the tool while usage and feedback are observed; and Demonstration and Feedback for teachers when piloting with students is not allowed. Evidence-collection methods include embedded assessments, baseline/endline tests, propensity score matching with a control (Digital Promise (2026))
  • Faster metrics for AI edtech evaluation timing: the recommendation to find metrics that arrive before contract renewal decisions

Claims

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Key Sources

  • Ruiz, P., Peters Hinton, V., Prado, Y., Noakes, S., Liberman, B., Chillmon, C., Van Nostrand, P., Shell, A., Karim, S., Dallas, D., & Zachariah, I. (2025). Integrating AI-enabled edtech in PK16 education: Five recommendations for state and district leaders. Digital Promise. https://doi.org/10.51388/20.500.12265/273
  • Ruiz, P., and Noakes, S. (2025). AI-Enabled Edtech Adoption Cycle. Digital Promise. https://doi.org/10.51388/20.500.12265/272
  • Digital Promise. (2026). EdTech Quality Indicators Guide: Leveraging the Five EdTech Quality Indicators to save time and money, reduce risk, and improve student outcomes. https://digitalpromise.org
  • Digital Promise. (2026). EdTech Quality Indicators Guide: Leveraging the Five EdTech Quality Indicators to save time and money, reduce risk, and improve student outcomes. https://digitalpromise.dspacedirect.org/items/f1e0fd08-9047-42e7-9a2d-190ccb4d10dd
Page metadata
Field Value
type product
id ai-enabled-edtech-adoption-cycle
title AI-Enabled EdTech Adoption Cycle
description A four-phase adoption framework described by Ruiz et al.
status review
moved_from element/edtech-pilot-navigator-models
product_kind framework
generated by process:settle-candidates on 2026-10-09
trust unverified — no one has confirmed this page's content
sources 1 — digital-promise-2026

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