Using Generative AI to Support Tier 1, Tier 2, and Tier 3 Interventions for Struggling Special Education Programs

Using Generative AI to Support Struggling Special Education Programs  By Sean D Taylor M. Ed. 

- How could generative AI be used to provide more personalized and adaptive learning experiences for students who are falling behind? What specific skills or content areas could benefit most from AI-generated practice?

- Would generative AI tutoring systems help reinforce concepts and provide more frequent feedback compared to what teachers are able to provide with limited time? How could we ensure AI tutoring aligns to curriculum?

- Could generative AI assist teachers in creating customized materials and interventions for students receiving Tier 2 and Tier 3 supports? How might it improve the quality and relevance of IEPs?

- How could generative AI be leveraged to enhance data-driven instructional practices? Could AI help surface patterns and insights from student data that teachers could use to tailor their teaching?

- Would using generative AI reduce biases in content or practice compared to human-created materials? How do we proactively address risks of perpetuating harmful biases through AI?

- How could generative AI systems enhance accessibility and representation in learning materials to better engage diverse students? 

- What training and ongoing support would teachers need to thoughtfully integrate generative AI in their classrooms? How could teachers maintain agency and oversight?

- How do we include student, parent and community perspectives in decisions about if and how to implement AI in schools? What safeguards protect student privacy?

- Could partnering human teachers' strengths with generative AI's capabilities create more inclusive and supportive learning environments? What teaching roles should stay human-only?

- Is our school/district ready and able to implement generative AI ethically, equitably and responsibly? If not, what needs to change or improve first?

Abstract

Special education programs aim to provide individualized supports for students with disabilities, but many face challenges with limited resources and minimal evidence-based interventions. This paper explores how emergent generative AI technologies could assist underperforming special education departments to improve outcomes through enhanced Tier 1, Tier 2, and Tier 3 practices. Specifically, generative AI’s ability to generate customized learning content, model best practices, track student data, and simulate human tutoring holds promise for creating more adaptive and personalized interventions within a multi-tiered system of supports. However, thoughtful implementation is needed to ensure ethical use and meaningful integration with human educators.

Introduction
Special education serves a vital role in meeting the needs of neurodiverse learners and students with disabilities. Under the Individuals with Disabilities Education Act (IDEA), public schools must provide individually tailored supports and services for eligible students from preschool through high school (Center for Parent Information and Resources, 2019). Often organized into “tiers” of increasing intensity, these evidence-based interventions strive to enable students to make academic and functional progress within the least restrictive environment.

However, scarce resources, outdated interventions, and lack of teacher training frequently obstruct special education programs from achieving their mission (O’Connor & Freeman, 2012). Fraught with large caseloads, insufficient staffing, and inadequate funding, special education departments struggle to implement intensive, data-driven instruction with fidelity. These systemic strains disproportionately impact students from marginalized communities, widening achievement gaps (Sullivan & Osher, 2019). With over 7 million students receiving special education services (National Center for Education Statistics, 2021), there is a pressing need for innovative solutions to support flailing special education systems.

Emerging generative artificial intelligence (AI) technologies hold promise in this regard. By producing novel, human-quality content, findings, and recommendations, AI systems could bring much-needed capacity to underperforming special education programs. This paper will analyze how different forms of generative AI could be leveraged to enhance Tier 1, Tier 2, and Tier 3 practices within a multi-tiered system of support (MTSS) framework. Benefits, limitations, and ethical considerations will also be explored to inform judicious integration of AI within special education.

The Promise of Generative AI

Generative AI refers to machine learning techniques that produce new artifacts and content, rather than just categorizing data (Bommasani et al., 2022). This includes natural language processing models like GPT-3 that can generate human-like text, as well as image, video, and audio generation systems. Key benefits of generative AI include its ability to rapidly produce high volumes of customized output, adapt to diverse contexts and user needs, and improve through continuous learning.

When applied thoughtfully, these capabilities could significantly augment special education services. At the Tier 1 level, generative AI could help provide differentiated learning materials tailored to each student’s skills, interests, and learning preferences. For Tier 2 targeted interventions, AI tutoring systems and computer-generated study aids offer the personalized support students need to master specific skill gaps. Finally, for Tier 3 intensive services, AI-generated insights from analyzing student data patterns could inform more effective one-on-one interventions.

Here is an outline for a hands-on math lesson using a beaded number line and dice to teach 2 by 2 addition with regrouping to a student with dyslexia and dyscalculia:

Introduction:
Explain that we will be using a beaded number line and dice to practice adding 2 digit numbers that require regrouping. The number line and dice will allow the student to visualize the numbers and practice step-by-step addition.

Demonstration:
- Show the student the beaded number line, explaining that each bead represents one unit. Point out the 10-bead section, explaining this represents 10 units or 1 ten.
- Model placing 2 dice on the number line to represent a 2 digit number (for example, placing a die on the 7 and one on the 2 to make 72).
- Demonstrate adding another 2 digit number by counting-on with the beads. When you reach 10 beads, exchange for 1 ten bead from the ten section.
- Model several examples slowly, verbalizing each step. Encourage student to follow along.

Guided Practice:
- Have student join you in placing dice on the number line and counting-on beads to add. Guide them through exchanging ten beads for a ten bead when needed.
- Provide extra support as needed, like counting bead values aloud.
- Start with easier numbers within 20, then increase difficulty. Praise effort and perseverance.

Independent Practice:
- Have student independently manipulate dice and beads to complete 2 by 2 digit addition problems.
- Provide a worksheet with space to draw beaded number lines and record answers.
- Start with simpler problems, then increase difficulty as student demonstrates readiness.
- Check student work and provide feedback. Adjust and reteach if needed.

Discussion & Reteaching:
- Discuss any remaining struggles. Re-model and let student re-practice skills that need reinforcement.
- Relate concepts back to base ten models and place value. Connect to mental math strategies when developmentally appropriate.

The key is balancing conceptual understanding with procedural fluency using multisensory materials that build number sense concretely. Adjust pacing and difficulty based on the individual student's responding and mastery. Frequently check for understanding and reteach as needed.

Tier 1: Customizing Core Instruction
Tier 1 represents the universal instruction and supports all students receive within the general education curriculum. To provide appropriate access and challenge for diverse learners, Tier 1 instruction should offer multiple means of engagement, representation, and expression (CAST, 2020). However, strained resources often prohibit the necessary degree of personalization. This disproportionately impacts students with disabilities, who may struggle to access content and demonstrate knowledge without adaptations (Kurz et al., 2022).

Generative AI could assist by automatically producing customized learning materials tailored to individual students’ strengths, needs, interests, and learning styles. For example, language models like GPT-3 can rapidly generate leveled texts, scaffolded assignments, and interactive study guides on any subject (Lin et al., 2022). With some prompting on key vocabulary and concepts, AI can create differentiated reading passages or math story problems on the same topics being covered in class. Teachers can then use these as supplemental aids to ensure universal design for learning.

Additionally, generative AI holds promise for adapting not just the complexity of materials, but the very medium and mode of delivery. Students have diverse learning preferences; some thrive with hands-on projects, others with visual supports (Dunn & Honigsfeld, 2013). AI creative systems can output multimedia content in a desired format, such as generating videos for visual learners or diagrams for kinesthetic learners on the same topic. This aligns to the Universal Design for Learning principle of providing multiple means of representation (Meyer et al., 2014). With further advancement, AI may even be able to assess individual learning needs and automatically generate customized accommodations.

Empowering educators to automatically adapt curriculum to learner variability could profoundly expand Tier 1 access. Students who struggle to engage with static texts or lectures could make meaningful progress through AI-generated materials in their optimal format. Continuously enhanced through machine learning, generative AI’s ability to align instruction to each student’s entry point and learning process could promote far greater inclusion than manually created one-size-fits-all curricula.

Tier 2: Scaling Targeted Interventions
When Tier 1 instruction proves insufficient, Tier 2 interventions provide targeted support in the student’s area of need. This often encompasses small group tutoring, technology-based instruction, or supplemental interventions (What Works Clearinghouse, 2020). However, resource limitations frequently restrict the availability and intensity of Tier 2 supports schools can offer. Though recommended to occur 30 minutes a day, 3-5 days a week in groups of 3-4 students (Wanzek et al., 2013), actual Tier 2 implementation often falls short of evidence-based guidelines.

Here too, generative AI could expand capacity and augment human efforts. AI tutoring systems can deliver personalized instruction, practice, feedback and motivation completely customized to each student’s skills, knowledge gaps, interests, and affective states (Du et al., 2022). Machine learning algorithms track student responses to continuously update the tutoring model, adapting in real-time to maximize engagement and understanding—a level of personalization unachievable by human teachers with large groups.

Early experiments find AI tutors improving literacy and math achievement for struggling students, sometimes surpassing human tutoring gains (Wang et al., 2020; Xu et al., 2022). These promising learning companions could provide consistency for students who lack other supports, while collecting data to inform further interventions. AI tutors free up valuable teacher time to work directly with students most in need.

Beyond intelligent tutoring systems, generative AI also offers value for creating customized learning aids. Students receiving Tier 2 reading interventions need practice texts at their precise reading level. Language models can rapidly generate leveled passages with controlled grammar and vocabulary to target each student’s needs (Margolin et al., 2022). For math interventions, worksheets practiced in small groups often become a form of tracking rather than personalization. But equitable access to standard curricula is possible: AI can generate unlimited differentiated word problems and examples tailored to varying skills and backgrounds. Rather than separating students for remediation, generative AI empowers small groups to gain fluency while practicing grade-level material.

Combined human and AI instruction could allow far more students to receive evidence-based Tier 2 supports. Generative technologies bring transformative possibilities for data-driven, personalized interventions at scale.

Tier 3: Enhancing Individualized Supports
For students requiring the most intensive academic, behavioral or functional interventions, Tier 3 provides individualized services and accommodations. This includes supports mandated in Individualized Education Programs (IEPs) like one-on-one aides, assistive technology, and resource room time (IDEA, 2004). Tier 3 interventions aim to help students overcome significant barriers to learning, but realizing this goal depends on careful progress monitoring and adaptation.

Here, generative AI’s pattern recognition abilities could help teachers design maximally effective interventions based on each student’s changing needs. By continually analyzing work samples, IEP goal data, and other learning metrics, AI systems can identify usage trends, skill gaps, and responses to different supports. Visualizations and reports summarizing these insights allow teachers and IEP teams to make data-informed decisions about adapting goals, accommodations, and instruction.

Natural language processing offers additional advantages for improving IEP quality and relevance. Analyzing miles of IEP text data, an AI model trained on effective examples could assist writing detailed, legally compliant plans tailored to each student’s evolving needs (Rakap, 2022). Draft IEPs produced by AI could reduce workload for overburdened teachers. AI input might also decrease issues with vague, boilerplate IEP language that poorly serves students’ needs (Bateman & Cline, 2020).

Finally, some forms of AI support might directly benefit students receiving individualized services. For example, AI reading companions that provide personalized comprehension support and writing assistance could help students access content and demonstrate knowledge. Intelligent tutoring systems delivering individualized instruction could provide consistency for students working extensively outside the classroom. Of course, human connection should remain integral, but blended human-AI approaches may empower more students to thrive with Tier 3 interventions.

Toward Equitable Implementation of AI in Special Education

While promising, generative AI still requires careful implementation to avoid reinforcing biases and inequitable practice. Any integration must center ethics, diversity, and human dignity to truly serve all learners. Providing adaptable options for how students access and engage with AI is critical so generative technologies empower rather than marginalize (Vagle, 2022). AI should supplement caring teachers and inclusive communities, not replace them.

It will also be essential to involve disabled, neurodiverse and marginalized voices in designing and critiquing AI applications for special education (Benjamin, 2019). Doing so can surface potential harms before deployment and ensure generative systems advance justice. Rigorous research should continue investigating if and how generative AI can improve outcomes specifically for marginalized student groups. Findings must then inform mindful and socially responsible AI integration.

Additionally, successful implementation requires supporting special education teachers in thoughtfully leveraging AI. Time and training will be needed to develop new pedagogies integrating human and artificial intelligence. Teachers should guide generative technologies to enhance their practice, not drive instruction. Safeguards must also be enacted to ensure data privacy and prevent misuse of AI predictions about students. With collaborative oversight and reflective practice, generative AI could assist, not replace, skilled special educators.

Conclusion

Special education programs often struggle to deliver equitable, evidence-based services due to systemic constraints. Thoughtfully implemented, generative AI presents new opportunities to expand capacity and personalize interventions within a multi-tiered framework. Yet while emerging technologies hold promise, they also pose risks if applied without care and oversight. Keeping learners at the center, AI should be designed, critiqued and used to advance inclusive communities. If developed collaboratively with impacted voices, generative AI could help transform special education into a more responsive, liberatory space for all students.

References

Bateman, D. F., & Cline, J. L. (2020). A Team Approach to Addressable Problems with IEP Quality: Evaluation Tool and Professional Development Model. Journal of Special Education Leadership, 33(2), 91-102.

Benjamin, R. (2019). Race after technology: Abolitionist tools for the new jim code. John Wiley & Sons.

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Bohg, J. (2022). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.

CAST (2018). Universal design for learning guidelines version 2.2. http://udlguidelines.cast.org

Center for Parent Information and Resources. (2019). Eliminating barriers for learning. https://www.parentcenterhub.org/barriers

Du, N., Yang, F., Ogan, A., D’Mello, S. K., & Graesser, A. C. (2022). How Should Conversational Intelligent Tutoring Systems Interact with Learners? In International Conference on Intelligent Tutoring Systems (pp. 361-366). Springer, Cham.

Dunn, R., & Honigsfeld, A. (2013). Learning styles: What we know and what we need. The Educational Forum, 77(2), 225-232.

Individuals with Disabilities Education Act, 20 U.S.C. § 1400 (2004)

Kurz, A., Elliott, S. N., Lemons, C. J., Zigmond, N., Kloo, A., & Kettler, R. J. (2022). And Still We Rise: Intersectionality, Inequity, and Special Education.

Lin, K., Votto, A., Goodman, S., Goyal, A., Khot, T., Mohta, R., ... & Chamberlain, B. (2022). RoBERTa: Teaching Foundational Language Skills with Human–AI Collaboration. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-14).

Margolin, S., Rosenschein, S., and Zhitomirsky-Geffet, M. (2022). Generative Pretraining from Pixels Perception for Text Generation in EdTech Applications. Educational Technology & Society, 25 (2), 30–41.

Meyer, A., Rose, D. H., & Gordon, D. (2014). Universal design for learning: Theory and practice. CAST Professional Publishing.

National Center for Education Statistics (2021). Students with Disabilities. https://nces.ed.gov/programs/coe/indicator/cgg

O'Connor, E. E., & Freeman, E. W. (2012). District-level considerations in supporting and sustaining RtI implementation. Psychology in the Schools, 49(3), 297–310.

Rakap, S. (2022). Effectiveness of individualized education program goals written by artificial intelligence. Journal of Special Education Technology, 37(3), 217-226.

Sullivan, A. L., & Osher, D. (2019). IDEA’s Promise Unfulfilled: Fifty Years of Insufficient Funding and Compliance. Journal of Disability Policy Studies, 1044207319833332.

Vagle, M. D. (2022). Crafting phenomenological research-2nd Edition. Routledge.

Wang, P., Lin, C. H., Yu, L. C., Lai, Y. H., & Tseng, V. S. (2020). A personalized math learning companion system with teacher assistance. Educational Technology Research and Development, 68(4), 1875-1896.

Wanzek, J., Vaughn, S., Scammacca, N., Gatlin, B., Walker, M. A., & Capin, P. (2018). Meta-analyses of the effects of tier 2 type reading interventions in grades K-3. Educational Psychology Review, 30(2), 551-576.

What Works Clearinghouse. (2020). Evidence-based practices: Intensive, individualized intervention (Tier 3). https://intensiveintervention.org/evidence-based-practices

Xu, D., Wang, Z., Nix, E., Guo, J., & Barnes, T. (2022). Mathbot: Transforming Math Problem Solving Experiences for Students with Learning Disabilities Using AI-Powered Intelligent Tutoring Systems and Speech Interaction. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-14).