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Device Deployment Models in K-12 Schools: Considerations for the Rollout of Artificial Intelligence

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Bibliographic Details
Title: Device Deployment Models in K-12 Schools: Considerations for the Rollout of Artificial Intelligence
Language: English
Authors: Kathryn M. Rich, Alise Crossland, Ky Cosand, American Institutes for Research (AIR)
Source: American Institutes for Research. 2025.
Availability: American Institutes for Research. 1400 Crystal Drive 10th Floor, Arlington, VA 22202. Tel: 202-403-5000; Fax: 202-403-5001; e-mail: inquiry@air.org; Web site: https://www.air.org/
Peer Reviewed: N
Page Count: 34
Publication Date: 2025
Document Type: Reports - Evaluative
Education Level: Elementary Secondary Education
Descriptors: Artificial Intelligence, Technology Uses in Education, Technology Integration, Elementary Secondary Education, Handheld Devices, Educational Change, Institutional Characteristics, Capacity Building, Faculty Development, Computer Assisted Instruction, Affordances, Models, Pandemics, COVID-19, Emergency Programs, Federal Aid, Grants, School Districts, Decision Making
Laws, Policies and Program Identifiers: Elementary and Secondary School Emergency Relief Fund
Abstract: The rapid expansion of digital learning in K-12 education, accelerated by the COVID-19 pandemic and the rise of artificial intelligence (AI), has brought renewed attention to device deployment models, which education systems use to facilitate student access to and use of digital devices. Device deployment models can play a key role in shaping instructional possibilities, especially in Grades 3-12, where digital tools and AI-powered platforms are increasingly central to teaching and learning. As AI becomes more embedded in educational practice, the structure and sustainability of device access will play a critical role in determining how effectively schools can leverage these technologies. This whitepaper examines issues and successes related to different device deployment models and their implications for teaching and learning, with particular attention to the proliferation of AI tools. It describes typical and emerging device deployment models and traces how device deployment models have changed over time, discussing the factors shaping those changes. Key issues related to teacher professional development and institutional capacity are spotlighted--two areas that are critical to successfully using technology in instruction, regardless of the device deployment model. The paper concludes with an examination of how early-adopter school systems are using AI for educational purposes and how the affordances and constraints of various device deployment models could shape AI's impact on teachers' and students' educational experiences.
Abstractor: ERIC
Entry Date: 2026
Accession Number: ED677969
Database: ERIC
Description
Abstract:The rapid expansion of digital learning in K-12 education, accelerated by the COVID-19 pandemic and the rise of artificial intelligence (AI), has brought renewed attention to device deployment models, which education systems use to facilitate student access to and use of digital devices. Device deployment models can play a key role in shaping instructional possibilities, especially in Grades 3-12, where digital tools and AI-powered platforms are increasingly central to teaching and learning. As AI becomes more embedded in educational practice, the structure and sustainability of device access will play a critical role in determining how effectively schools can leverage these technologies. This whitepaper examines issues and successes related to different device deployment models and their implications for teaching and learning, with particular attention to the proliferation of AI tools. It describes typical and emerging device deployment models and traces how device deployment models have changed over time, discussing the factors shaping those changes. Key issues related to teacher professional development and institutional capacity are spotlighted--two areas that are critical to successfully using technology in instruction, regardless of the device deployment model. The paper concludes with an examination of how early-adopter school systems are using AI for educational purposes and how the affordances and constraints of various device deployment models could shape AI's impact on teachers' and students' educational experiences.