The Impact of AI Upon MTSS Processes and Policies

Generative AI-driven educational software is going to become more, not less, pervasive. So, teachers need to be highly trained in effective implementation of evolving teaching and learning applications. Therefore, the science of teaching and learning-effective pedagogy- involves more than telling a student to access an application. Teacher preparation programs and institutional policies must lead this professional development endeavor.

Effective multi-tiered systems of support (MTSS) should ensure that differentiated strategies and formative assessment processes are embraced as two sides of the metaphorical coin we call teaching and learning. When both are planned and implemented well, differentiated approaches are part of the leading indicators and then formative assessment processes are the trailing indicators of effective, inclusive success. A pedagogical toolbox requires up-to-date professional soft skills rather than more classroom hardware that promises access to equity through software packages.

Generative AI has the potential to significantly transform multi-tiered system of support (MTSS) processes and policies, especially in the context of educating diverse learners. AI can positively impact MTSS processes through reliable data analysis and effective early identification. Generative AI provides the potential to analyze large, complex datasets such as academic records, behavioral logs, attendance tracking to identify patterns and early warning signs related to student risk factors.  For instance, AI can flag learners who may need Tier 2 or Tier 3 interventions based on writing patterns and student engagement with learning materials.

Generative AI can provide personalized intervention programs. AI can generate customized reading passages, scaffolded assignments, or language supports that are aligned to a student’s language proficiency, reading level, or cultural context. This is especially useful for English language learners (ELLs), students with disabilities, and others with individualized needs.

Generative AI has the potential to collect and analyze progress monitoring data.  Automation creates the opportunity for frequent, adaptive formative assessments and real-time feedback reports. AI-powered application dashboards can dynamically adjust goals and recommend instructional next steps based on student performance.

In this way, generative AI provides real-time teacher support and aid in data-based decision-making. AI can provide suggested interventions and evidence-based practices for specific learner profiles, reducing cognitive load on educators. However, highly-trained teachers must ensure that these recommendations are transparent, bias-sensitive, and evidence-aligned.

Soon generative AI will impact policy related to equity and bias considerations. AI tools may unintentionally reinforce systemic biases, especially if algorithms are developed or teachers are trained on biased datasets that over-identify certain groups for Tier 3. To mitigate this, audits for algorithmic fairness is essential, especially when used with diverse learners related to race, language, or disability.

Generative AI will also soon be utilized to revise data privacy and consent considerations. Updating FERPA, IDEA, and state-specific policies to govern AI use in student data processing is logical. This is especially true for policies involving minors and vulnerable populations.

Being effective with people and efficient with time is just good leadership (Covey, 1989). Generative AI can identify staff training schedules and professional development needs.  Effective MTSS frameworks must include PD for educators on using AI ethically and effectively.  This crucial aspect for AI implementation will ensure more effective human oversight and culturally responsive practices.

Generative AI will help re-define what entails evidence-based interventions. A current challenge is AI-generated interventions might not meet current federal or state definitions of “evidence-based.” Therefore, an immediate policy implication is the need to establish criteria for evaluating AI-assisted practices within MTSS.

Certainly, Generative AI has implications for diverse and English Language Learners (ELLs). In relation to students with disabilities, AI can co-create accessible materials such as text-to-speech or customized visual supports. However, the highly trained teacher must ensure anything generated by AI aligns with IEPs and legal mandates under IDEA.

While AI can dynamically translate and simplify texts, the highly trained teacher needs to ensure culturally relevant examples. While AI can dynamically translate and simplify texts, the highly trained teacher needs to ensure culturally relevant examples. For culturally and linguistically diverse (CLD) students, AI will be able to help personalize learning experiences that honor students' backgrounds. However, there is always a risk of reinforcing stereotypes if generative models are not curated or culturally aware. It must be the teacher rather than AI who ensures culturally responsible practices by ensuring fidelity of language support and maintaining human validation to avoid linguistic misrepresentations.

Given potential risks involved with AI implementation, especially related to developing processes and policies, districts and schools should pilot AI in MTSS within controlled environments with teacher oversight. Planning for AI implementation must seek to establish ethics and equity audits to ensure AI-driven tools do not exacerbate disparities. Begin by developing interdisciplinary teams that involve educators, data scientists, families, and advocates in AI-based tool development and evaluation.

Avoid being efficient with people. Mandating transparency begins with district leaders and its teachers understanding how AI makes decisions and being proficient when explaining it to families and students. Otherwise, teachers are more likely to be led by AI algorithms rather than empowering their effective teaching and educational leadership.

 

 To cite:

Anderson, C.J. (May 31, 2025) The impact of AI upon MTSS processes and policies. [Web log post] Retrieved from http://www.ucan-cja.blogspot.com/

 

References:

Black, P., & Wiliam, D. (2006). Developing a theory of formative assessment. In J. Gardner (Ed.), Assessment and learning (pp. 81–100). Sage.

Covey, S. R. (1999). The 7 habits of highly effective people: Powerful lessons in personal change. Free Press

International Society for Technology in Education. (2025.). AI in education and accessibility. ISTE. https://www.iste.org

OpenAI. (2024). ChatGPT (GPT-4 model) [Large language model]. https://chat.openai.com

U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://files.eric.ed.gov/fulltext/