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.
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/
