Education leaders should engage in this work with a collaborative, cross-functional team in the district office that works hand-in-hand with school leaders and educators. The team should begin their exploration of edtech, including AI-enabled tools, by analyzing formative and summative assessment data to define their instructional needs. These defined instructional needs should then be used to determine which research-based approaches demonstrate the most impact for meaningfully closing learning gaps and provide clarity on where technology has the potential be most impactful. Concurrently, education leaders should inventory the edtech products used in any classroom across the school or district to identify whether these tools should be used more broadly to address instructional gaps, as well as to minimize redundancies and increase savings. These efforts will enable districts to approach exploration of edtech tools with intentionality and the ability to determine whether a new product is required to successfully meet their needs.
Strategies for Analyze & Inventory
Create a collaborative team with representatives across departments. Consider including representation from instructional, operational, legal, and IT teams, as well as school leaders, classroom educators, families, and learners. For example, Denver Public Schools’ Cross-Departmental Collaborative Team participating in the evaluation process were divided into four core teams: EdTech, DoTS (Department of Technology Services), Purchasing, and Legal, and 14 content teams: EdTech (content), Humanities, Math, Assessment, Multiple Language Education (MLE), Social Emotional Learning (SEL), Arts & PE, Exceptional Student Services (ESS), College and Career Success (CCS), Career and Technical Education (CTE), Library Services, Science, Early Childhood Education (ECE), and Gifted and Talented (GT).
Use formative and summative assessment data, like state assessment scores, to identify priority instructional needs gaps. Collaborate with school leaders and educators to co-interpret data through a root cause analysis that helps identify the core competencies students struggle with most.
Leveraging research, identify evidence-based strategies to address the identified instructional gaps, including articulating where technology could play a valuable role.
Begin the review process with the products currently in active contracts with the district and schools. Start with usage to identify products being paid for through a contract with zero use across any learners. Then, move into reviewing products with evidence of use.
Administer a survey to all staff to compile the edtech tools used for instructional purposes across the school or district. Define instances of instructional use of technology to ensure staff include use of tools that may not be explicitly “edtech,” such as Google Docs, to create a robust list of products. Enable staff to easily add more tools to keep the list dynamic and all-encompassing.
Engage in focus groups with teachers to review the list of products and learn which tools are most valuable to teachers and why, and which are the most difficult or frustrating to use and why.
Design a dynamic website that houses the edtech inventory, including naming the uses of each tool as described by the staff who use them. Enable the website to allow staff to directly leave comments on the value or challenges with the tools, including comparisons to other tools.
Categorize products with similar use cases to determine how many different tools are used for the same purposes. Work closely with school leaders and educators to understand why they use one of the products over others in a collaborative effort to design next steps in product consolidation.
This operational rubric outlines how a district systematically scores vendor applications across technical compliance, privacy protections, and instructional design quality. It allows multi-stakeholder evaluation panels to apply uniform weighted metrics during a software procurement review.
This diagnostic reference list offers targeted prompts for school evaluation teams to review when selecting instructional software. It prompts evaluators to examine data security rules, vendor support availability, and professional development needs before completing a purchase.
Created by Digital Promise, this template guides district leadership teams to explicitly define the educational challenge they intend to solve before selecting a tool. It establishes explicit, measurable learning objectives and criteria to judge pilot program success.
This diagnostic step-by-step framework aids district leaders in structuring community stakeholders around a formal, data-driven needs assessment protocol. It ensures procurement choices are aligned to real instructional gaps rather than purchasing software based on market trends.
This resource focuses on classroom-level strategies to move educational software beyond passive consumption into active, collaborative student workflows. It details methods for educators to systematically match specific digital applications with targeted learning goals. There is a supplemental questionnaire on pages 275-282.
This kit provides structured survey instruments designed to capture direct, feedback from educators, staff, and families regarding technology usage behaviors. The collected dataset informs systemic planning regarding infrastructure support and remote learning deployment strategies.
This guide provides administrative parameters to ensure edtech acquisition addresses historical accessibility gaps and diverse student learning preferences. It moves beyond regulatory checklist verification to evaluate cultural inclusivity and structural product equity.
This National Center for Education Statistics (NCES) resource provides a guide on how school systems can assess their technology integration requirements. It focuses on identifying gaps by gathering data on the availability, type, and current operational use of technology in schools.
The Technology Integration Matrix (TIM) outlines a structured framework to evaluate and target how effectively digital tools are being applied within classrooms. It breaks down evaluation criteria across five distinct levels of technology adoption cross-referenced with five active learning environments.
This visual reference tool explains how districts can implement a standardized grading matrix to evaluate software platforms before full deployment. It scores tools on pedagogy, user experience, and safety to ensure low-quality apps are filtered out early.
This case study documents how a district cross-departmental team built a formalized evaluation framework and an approved product repository. The process ensures student data privacy, maximizes cost-savings through cohort purchasing, and assesses product instructional value.
This framework resource details methods for districts to map out existing hardware and software footprints across their operations to identify system redundances. It fosters intentional communication bridges between distinct curriculum, finance, and IT administration teams.
This case study focuses on Orcutt Union School District’s model of leveraging national partnerships and multi-district networks to share best practices and pilot new tools. It outlines how aligning local infrastructure with nationwide insights accelerates district-wide AI readiness.
This case study focuses on Indian Prairie School District’s model of forming a Generative AI Task Force to combine curriculum, technology, and administrative teams. It outlines methods for establishing multi-disciplinary governance to systematically evaluate, procure, and inform ethical AI policies.
This case study details how the Indian Prairie School District created continuous evaluation mechanisms, student playbooks, and family guides to monitor deployed AI tools. It provides an operational framework for maintaining community transparency and data accountability as AI scales.
