Reimagining MTSS, RTI, and Special Education Through Generative AI: Designing the Future "Magic Kingdom" of Learning

"Building adaptive, flexible, and innovative learning labs that spark imagination, curiosity, and passion for learning means embracing the transformative potential of artificial intelligence alongside human creativity. By harnessing AI to develop personalized educational games and gamified experiences, we create environments where abstract concepts become tangible challenges to overcome. These technology-enhanced spaces blur the boundary between play and learning, allowing students to manipulate variables, test hypotheses, and see immediate feedback through interactive, hands-on exploration. The marriage of thoughtful pedagogy with intelligent systems enables us to craft responsive learning ecosystems that adapt to each learner's pace, preferences, and potential—cultivating not just knowledge acquisition but genuine engagement and discovery. In these next-generation labs, students don't simply consume information; they collaborate with technology to create, iterate, and innovate, developing the adaptive thinking skills essential for navigating an increasingly complex world."

By Sean David Taylor, M.Ed.

Abstract
The integration of generative AI into education—through text, music, art, and interactive learning systems—marks a profound opportunity to revolutionize Multi-Tiered Systems of Support (MTSS), Response to Intervention (RTI), and special education. This article proposes a future in which Tier 2 and Tier 3 interventions are transformed into engaging, individualized, and magical learning environments through generative AI. Students and educators are empowered as co-creators of personalized content and adaptive tools tailored to students’ learning modalities. By leveraging the principles of Stanford Design Thinking and addressing the affective, behavioral, and cognitive needs of learners, AI-driven interventions may surpass Bloom's Two Sigma problem, creating a 3- or 4-sigma effect on student outcomes.

Introduction: The Problem with the Current System

Tier 2 and Tier 3 interventions in MTSS and RTI frameworks are critical lifelines for students who struggle. However, many of these interventions remain worksheet-driven, monotonous, and disconnected from student passions and modalities of engagement. They are also cut short for time or budget constraints. For students in special education or receiving targeted support, this disengagement compounds the barriers they already face. The opportunity to reimagine this experience through the lens of generative AI offers not only personalization but also joyful, transformative engagement.

The Promise of Generative AI

Generative AI—including large language models (LLMs), image generators, music composition tools, and interactive storytelling platforms—offers an unparalleled opportunity to tailor educational content to each learner. Applications of these tools in MTSS and RTI include

Personalized Learning Materials: Task cards, control cards, manipulatives, stories, and step-by-step explainer illustrations tailored to the student’s level, interest, and sensory modality.
Adaptive Universal Screeners: Assessments that dynamically adjust to a student's abilities
Dynamic Assessment: AI-generated formative assessments that adapt to student responses in real time.
Multimodal Interventions: Songs, chants, graphic novels, games, and interactive narratives that teach through the student’s preferred medium.
Student Agency: Students as prompt engineers—creating their own stories, songs, math games, or review tools.
Teacher Empowerment: Special educators can build apps or use AI interfaces to rapidly design custom interventions, replacing dry, pre-packaged programs with vibrant, student-centered learning journeys.

The Magic Kingdom of Learning: Making Interventions the Highlight of the Day

Imagine a Tier 3 reading intervention where students create a comic book about the phonics patterns they’re studying, score their own original soundtracks, and narrate dramatic read-alouds using character voices. Or a math intervention where learners explore a digital game board populated with monsters that can only be defeated through place value reasoning and fraction bar modeling. This is not fantasy—it is the accessible future of AI-driven special education.

When the most struggling students enter their interventions, they should feel like they’re entering Disneyland, not detention. Generative AI gives educators the tools to engineer this shift in experience.

Design Thinking and the AI Revolution in Intervention

Applying the Stanford Design Thinking process to MTSS/RTI reimagines the system around student empathy:

Empathize: Observe and listen deeply to understand students' frustrations, passions, and learning preferences.
Define: Identify root challenges, not just symptoms (e.g., disengagement due to modality mismatch).
Ideate: Brainstorm a wide range of possible AI-generated solutions—games, illustrated guides, interactive apps.
Prototype: Use tools like ChatGPT, DALL·E, or Scratch to quickly generate low-cost, high-impact learning artifacts.
Test and Iterate: Try the materials with the student, gather feedback, refine. Repeat.

This iterative, empathetic approach enables educators to become learning engineers—not just content deliverers.

Surpassing the Two Sigma Problem

Bloom’s 1984 finding—that one-on-one tutoring could produce two standard deviations of improvement—set an aspirational bar. AI, when used not as a replacement for the teacher but as an amplifier, holds the potential to push well beyond this.

3-Sigma Gains: When students receive interventions tailored to their emotional, cognitive, and sensory profile.
4-Sigma Gains: When students co-create their learning materials, engage through preferred modalities, and are emotionally invested in outcomes.

What Needs to Happen Now: Steps for Stakeholders

For Students

Learn basic prompt engineering skills to guide AI in generating content.
Practice metacognition: identify which learning styles and tools work best.
Engage with AI not as consumers but as co-creators of knowledge.

For Teachers

Train in generative AI literacy and prompt design.
Embrace a design mindset: become architects of learning experiences.
Build and share custom interventions using AI platforms.

For Administrators

Invest in professional development focused on AI and instructional design.
Encourage a culture of experimentation and design thinking.
Fund pilot projects that transform Tier 2 and 3 environments into hubs of innovation.

For Policy Makers

Update MTSS and RTI frameworks to support AI integration.
Protect privacy and ethical use, while encouraging open-source solutions.
Prioritize funding for equitable access to AI tools in special education.

Tier 3 (top): Intensive, individualized interventions for 1-5% of students
Tier 2 (middle): Targeted small-group instruction for 5-16% of students
Tier 1 (bottom): Universal instruction for 80-90% of students

Reconceptualizing Tier 1 Instruction: Balancing Structure and Flexibility in Modern Learning Environments

The tension between explicit direct instruction and student-centered approaches presents an opportunity to reimagine Tier 1 educational frameworks. Traditional "chalk and talk" methodologies, while providing systematic structure, often lack the adaptability needed to foster independent learning skills. Meanwhile, Montessori-inspired environments emphasize self-direction but incorporate surprisingly systematic competency progressions.

Key Considerations for Modern Tier 1 Instructional Design

Systematic Progression with Flexible Pacing

Effective Tier 1 instruction should maintain clear competency sequences while accommodating diverse learning tempos. Rather than adhering to rigid whole-class pacing, instructional design should:

Establish well-defined skill progressions with explicit mastery criteria
Allow students to demonstrate competence through multiple modalities
Create structured pathways that permit acceleration for rapid learners
Provide extended engagement opportunities for those requiring additional time

Shifting Instructional Delivery Models

The traditional teacher-centered approach requires transformation toward a more dynamic instructional model where:

Direct instruction occurs strategically in varied groupings rather than exclusively whole-class
Teacher demonstrations transition to facilitation of peer-to-peer knowledge transmission
Students develop metacognitive awareness through guided reflection on learning processes
Instruction becomes more responsive to emergent student interests while maintaining curricular integrity

Environmental Design for Differentiated Learning

Physical and temporal learning spaces must be reconceptualized to:

Create designated areas for different learning modalities (direct instruction, collaborative work, independent practice)
Establish clear protocols for accessing tiered support within the classroom
Implement flexible scheduling that accommodates varied completion timeframes
Provide structured choice within carefully curated learning activities

Integration of Assessment with Instruction

A modernized Tier 1 approach necessitates assessment practices that:

Emphasize competency demonstration rather than time-bound completion
Utilize formative assessment continuously to inform instructional decisions
Implement strategic progress monitoring to identify both acceleration and intervention needs
Foster student agency in tracking and evaluating learning progression

By integrating the systematic nature of explicit instruction with the responsive flexibility of student-centered approaches, Tier 1 instruction can evolve to accommodate diverse learning needs while maintaining curricular coherence and instructional rigor.

🌟 10 Facts About Hands-On, Multimodal Learning That Transforms Tier 2 and Tier 3 Interventions

Multimodal approaches enhance retention by up to 75%. While traditional worksheet-based interventions often yield temporary results, engaging multiple sensory pathways creates neural connections that support lasting competency. These comprehensive memory pathways prevent the rapid skill deterioration commonly observed within weeks of traditional interventions (Mayer, 2009).
Peer-to-peer teaching solidifies conceptual understanding. When struggling learners articulate concepts to others, they develop metacognitive awareness and deeper comprehension. This "learning through teaching" methodology transforms passive intervention recipients into active knowledge constructors, promoting skill permanence rather than transient memorization.
Movement-based interventions facilitate cognitive integration. Physical engagement activates multiple brain regions simultaneously, creating robust memory networks resistant to decay. Unlike computer applications that engage limited cognitive pathways, kinesthetic interventions produce measurable improvements in long-term retention and application (Ratey, 2008).
Multisensory instruction addresses foundational skill gaps. Traditional drill-based interventions often mask rather than resolve underlying misunderstandings. Hands-on manipulatives and concrete representations reveal conceptual misconceptions that worksheet completion might conceal, allowing for genuine rather than superficial mastery.
Social-emotional engagement enhances intervention efficacy. Interactive, collaborative interventions trigger dopamine and oxytocin release, creating positive associations with challenging material. This emotional connection transforms intervention from remedial punishment to rewarding experience, sustaining motivation through difficult learning progressions.
Music and rhythmic patterns encode academic content durably. Unlike digital drill programs where information rapidly fades post-completion, rhythmic and musical encoding creates persistent memory pathways. These intervention modalities establish long-term retention patterns resistant to the rapid forgetting curve observed with traditional approaches.
Concrete-representational-abstract progression builds transferable competencies. Manipulative-based interventions systematically bridge concrete understanding to abstract application, unlike worksheet interventions that prematurely demand abstract processing. This intentional progression sequence ensures genuine conceptual mastery rather than procedure memorization.
Game-based interventions increase practice frequency without diminishing engagement. The repetition necessary for mastery often becomes tedious in traditional interventions, leading to compliance without deep processing. Game structures maintain high engagement through distributed practice, facilitating the volume of repetition required for competency development.
Storytelling and narrative frameworks enhance conceptual retention. Decontextualized worksheet tasks fail to create meaningful memory anchors, whereas narrative-embedded interventions connect skills to emotionally resonant contexts. This narrative scaffolding prevents the rapid skill deterioration commonly observed following traditional interventions.
Cross-modal interventions develop compensatory strategies for diverse learners. Rather than repeatedly applying ineffective modalities, multimodal interventions develop alternative processing pathways. These compensatory approaches ensure students develop genuine mastery that persists beyond the intervention period, creating sustainable academic success rather than temporary performance improvements.

Conclusion: A New Frontier in Educational Equity

Generative AI provides a once-in-a-generation opportunity to reimagine intervention—not as remediation, but as reinvention. Students who once dreaded pull-out sessions will look forward to them as the highlight of their day. With the teacher as creative director and AI as co-producer, education becomes not only equitable and personalized but joyful and empowering.