Survey Explores Use of AI Within Multi-Tiered System of Supports

Blog Post | May 2026

An innovative journal article from the Children’s Learning Institute (CLI) examined how Artificial Intelligence (AI) can be integrated into the Multi-Tiered System of Supports (MTSS) framework. MTSS is designed to meet students’ diverse academic needs through evidence-based practices and a preventative, data-driven approach. Within this framework, educators rely on data to guide program development, refine instruction, deliver targeted interventions, and provide comprehensive supports. In their publication, the team described how AI is currently being used in PK–12 classrooms, and how it may be incorporated within and across the MTSS three-tier structure. They also addressed educators’ essential role in implementing AI tools, what educators know about and worry about regarding AI, and the ethical considerations of using AI in PK–12 settings.

MTSS AI Overview

Overview diagram displaying opportunities for Artificial Intelligence-enhanced Multi-Tiered System of Supports processes across the three-tier structure.

To supplement this work, the research team conducted a survey of 101 early childhood education professionals participating in one of CLI’s largest programs, Texas School Ready (TSR). The purpose of the survey was to better understand educators’ familiarity with the MTSS framework, whether they currently integrate AI into their classrooms, and how they feel about the use of AI in early education.

Survey Results

Title slide introducing the Respondent Demographics section of the CLI survey on AI and MTSS.

Title slide introducing perspectives on the Multi‑Tiered System of Supports (MTSS) Framework.

Question: Do respondents currently use AI tools in any part of their MTSS work? Pie chart showing AI use in MTSS work: 68% report no use, 21% are unsure, and 11% report using AI tools.

Title slide introducing perspectives on the use of artificial intelligence in early childhood education.

Question: Are AI tools used by respondents for classroom or lesson planning? Pie chart showing AI use for classroom or lesson planning: 22% yes and 78% no.

Question: How do respondents currently use AI in their educational practices? Bar chart showing current AI use: lesson planning 45%, content creation 19%, classroom management 17%, 30% report not using AI.

Question: What are the primary ways respondents hope to use AI in the future in their educational practices? Bar chart showing hoped‑for future AI use: lesson planning 66%, supporting diverse learners 51%, content creation 40%.

Question: How much do respondents agree with statements about AI use in their educational practices? Stacked bars show most respondents agree or strongly agree that AI supports lesson planning, engagement, assessment, and personalized learning.

Question: What factors would increase educator's willingness to use AI tools in education? Bar chart showing factors increasing AI use: better training 74%, improved outcomes 50%, stronger privacy and ease of use 46%.

Question: What supports would make educators feel more comfortable and confident using AI tools? Bar chart of supports needed: training or support 30%, other supports 11%; 46% did not respond.

Quote: "Evidence from empirical research that demonstrates AI enhances listening comprehension, vocabulary word knowledge, and oral language, and teaches students to apply skills not to click and guess, but to process information to develop knowledge."

Question: How familiar are respondents with their school's AI guidelines? Pie chart showing familiarity with school AI guidelines: not at all familiar 45%, somewhat familiar 27%, not very familiar 22%, very familiar 7%.

Question: What do respondents think is the future role of AI in education? Pie chart showing views on AI’s future role: essential and transformative 30%, concerned about impact 27%, helpful but not essential 24%, uncertain 20%.

Quote: "We need to make sure that districts clearly outline how AI is used and ensure that it does not remove too much thinking from the process. AI should make us smarter, not reliant on supports (e.g., Many people struggle to navigate without GPS—let's avoid that in education)"

The work of this publication has helped inform one of CLI’s most recent initiatives: the AERO AI project. Accelerated Early Reading Outcomes (AERO) is grounded in evidence-based practices and emphasizes foundational reading skills through instructional activities and routines that can be effectively administered by various tutors and teachers. Delivering AERO with fidelity is critical to ensuring positive outcomes; however, traditional fidelity-monitoring methods are resource-intensive, as they require trained observers to manually code recorded lessons. Project AERO AI is developing tools that analyze video recordings of AERO lessons, automatically detect adaptations from the lesson plan, flag segments for expert review of instructional quality, and generate tutor-friendly reports.