Inclusive Planning with AI: Core + Scaffold for Every Learner

By Matías Molfino - Educational Psychologist & Learning Support Specialist

Introduction

If you’re a teacher or homeschooling parent planning for diverse learning needs, you’re not alone. Many educators want to design lessons that are meaningful for each student, and that adapt to individual needs, strengths, and interests. This is exactly where AI tools for special education can make a practical, classroom-level difference.

Inclusive teaching begins at the design stage, where we anticipate learner variability and proactively remove barriers before students enter the classroom. As UDL reminds us, the barriers are in the design, not in the student. This mindset guides everything - from setting clear learning goals to providing flexible options for engagement, representation, and expression. AI can support this process by helping us work more efficiently, especially when we’re juggling multiple needs at the same time.

In this article, I’ll explore practical ways to integrate AI into lesson planning so learning becomes both meaningful and personally relevant. By promoting student agency, activating prior knowledge, and designing with equity, flexibility, and universal support at the forefront, we align AI-assisted planning with the core pedagogical values of Finnish education.

1. Why Use AI for Inclusive Lesson Planning?

AI is not a decision-maker - it is a co-designer. 

When used well, it helps teachers: 

• Surface invisible barriers in the lesson design 

• Generate multiple access pathways aligned with UDL 

• Enrich the “Core” learning experience before scaffolds are added 

• Save time on drafting options for diverse learners 

However, AI does not understand the full context of a child - their sensory profile, emotional history, learning journey, or language background. Its suggestions must always be checked, adapted, and guided by you. That human insight is what makes you the most valuable part of any inclusive learning plan.

2. Start with the “Core”: The Foundation of an Inclusive Lesson

The Core is what all students will learn.

This is where clear, meaningful goals matter most.

When I identify a core goal - say “Explain the water cycle” - I ask myself:

• What barriers might students face?

• How can I represent the information in different ways?

• How can students show understanding in varied formats?

This is proactive design, just as Center for Applied Special Technology (CAST)’s UDL Guidelines recommend.

3. Then Add Scaffolds: Matching Support to the Learner

Scaffolds are the flexible supports around the Core.

Examples include:

• Sentence starters

• Step-by-step checklists

• Visuals for vocabulary

• Chunked reading

• Movement breaks

• Choice boards

AI tools for special education can help generate these scaffolds in seconds. For example, you can type: “Create three scaffolded options for explaining the water cycle for a student with dyslexia and ADHD.”

The goal is always meaningful learning, not simply making tasks easier.

4. Practical AI Prompts for SEN-Inclusive Planning

Every child processes the world differently. It is important to always anticipate sensory needs instead of reacting to them. AI can help you generate quick variations without having to redesign the entire lesson.

Here are prompts you can copy:

1. Movement-friendly variation

“Give me a kinesthetic version of this task for a student who needs movement breaks.”

2. Low-visual-load variation

“Reformat this as a low-clutter worksheet with only essential information.”

3. Noise-sensitive variation

“Suggest quiet alternatives for this group activity.”

4. ADHD-friendly sequencing

“Create a short checklist using simple language to guide task completion.”

These are especially useful when combining ai tools for special education and ai tools for STEM education, where experiments or hands-on work may require different sensory adjustments.

5. Avoid This When Using AI for Inclusion

AI often fails when: 

• Tasks are too complex, too open, or lack a clear Core 

• Learner profiles are vague (“student with dyslexia”) 

• Context is missing (grade level, sensory needs, language background) 

• Teachers ask for multiple accommodations without stating the purpose 

• AI is asked to “create everything at once” 

The fix? 

Break the planning process down. 

Feed information in a structured way. 

Treat AI as a collaborative planning partner - not a replacement for judgment.

How Elina Supports Inclusive Planning

The more I work with AI, the more I value tools that actually reduce my workload instead of adding new steps. What I like about Elina is how it brings planning, scaffolding, and documentation into one place.

When you create a lesson, Elina can:

• pull out your Core goal

• generate scaffold tiers

• add visual supports

• suggest sensory-friendly variations

It has never been about replacing your expertise. It simply frees up your time so you can focus on relationships, reflection, and the responsive teaching that truly matters.

Conclusion

Inclusive planning doesn’t have to be complicated. When we use UDL principles - clear goals, flexible options, and thoughtful scaffolds - we build lessons that welcome every learner. And when we bring in AI tools for special education, we make the process quicker and more sustainable.

If you’d like support generating plans and scaffolds that adapt to each learner, Elina can be a helpful place to start.

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