The 85% Efficiency Gains: Using AI to Draft Amazing IEPs The special education infrastructure in the United States is currently navigating a period of transition, characterized by a structural imbalance between the rising number of students requiring services and a diminishing pool of qualified educators. As of the 2024-2025 school year, over 7.5
Re-engineering Special Education Workflows from Diagnostic Data to Contextual AI Top 3 Key Takeaways The primary ethical and operational crisis in special education is not the introduction of artificial intelligence; it is the systemic failure to provide teachers with precision diagnostic data at the foundational level of Individualized Education Program (IEP) development. While
Progress Monitoring That Actually Supports Instruction Top 3 Key Takeaways The shift from periodic benchmarks to continuous, granular diagnostics provides real-time instructional support. The "overlay" mechanism bridges the gap between baseline data and daily assessments to ensure substantive compliance. Prioritizing learning acceleration over traditional remediation significantly increases student mastery of grade-level standards.
How AI Helps Special Educators Keep Up During IEP-Heavy Months Top 3 Key Takeaways AI reduces IEP workload by automating data collection, progress monitoring, and reporting, giving special educators more time for instruction. Diagnostic platforms like Let’s Go Learn provide IEP-ready data instantly, aligning assessment results directly to goals and present levels. AI-powered
What Is Transition? A Clear Guide for Educators Top 3 Key Takeaways Transition is about outcomes, not just compliance: It prepares students with disabilities for life after school–employment, education, and independent living. Effective transition planning starts early and is data-driven: Strong assessments and individualized planning lead to better post-school success. Modern tools like
Using AI to Strengthen IEP Workflows for Special Education Teachers Top 3 Key Takeaways AI reduces IEP workload by automating data collection and analysis, progress monitoring, reporting, and draft writing. Good data needs to feed AI for it to accurately support teachers in developing accurate IEPs and goals for each student. AI-powered platforms
How to Write a Strong IEP Plan Using Real Data Top 3 Key Takeaways High-quality IEPs begin with accurate, student-specific data, not guesswork. LGL’s adaptive diagnostics provide precise present levels to anchor goals. Using the full range of DORA and ADAM sub-tests helps educators write goals that truly match student needs, especially when
Common IEP Mistakes Top 3 Key Takeaways IEP goals often fail when they are vague, not measurable, or not aligned to true present levels of performance (PLAAFPs). High-quality diagnostic data—such as the data produced by DORA and ADAM—helps educators write accurate, standards-aligned, skill-specific goals. Ongoing data monitoring ensures that instruction stays responsive and
How to Create an Effective Individualized Education Program (IEP) Top 3 Key Takeaways Effective IEPs begin with high-quality diagnostic data that clearly defines a student’s present levels of performance (PLAAFP). Tools like DORA and ADAM provide granular, skill-level insights that strengthen goal-setting. Goals must be measurable, standards-aligned, and instructionally meaningful, allowing teachers to
Before IDEA: The Fight for School Access and Why 50 Years of the Law Matters Currently, there is a lot of discussion surrounding the Department of Education, the challenges facing schools, and the state of special education across the country. Many people do not realize that the Individuals with Disabilities Education Act (IDEA)
