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/

Proficiency with Formative Assessment Strategies can Help Faculty Effectively Plan for the Students’ Ethical Utilization of Generative Artificial Intelligence

Planning for differentiated processes in coursework offers a powerful and practical entry point for institutionalizing formative assessment because both approaches share a common foundation: Responsiveness to student needs and a commitment to growth over time. Inherently, differentiated processes offer varied pathways for students to engage with content, develop skills, and demonstrate understanding. By embedding formative assessment into these processes faculty can gather ongoing, meaningful evidence about how students are progressing on different paths.  This can facilitate timely adjustments to teaching based on each student’s zone of proximal development (Vygotsky, 1978). This will reinforce a classroom culture that values learning as a process rather than solely as an outcome. By planning for differentiation, faculty are naturally positioned to build in checkpoints that support personalized learning through formative feedback.

Differentiated planning and processes typically include scaffolding, tiered tasks, and provision of choice, which also creates the opportunity to apply UDL 3.0 Guidelines and Considerations. Each element of differentiation and application of UDL Principles for Postsecondary Professional Development  logically invite formative check-ins through progress journals, draft submissions or peer feedback.  Thus, the opportunity for students to receive guidance tailored to their selected process or challenge level, reinforces a system whereby formative feedback is not just a side activity, but part of the course architecture. This results in formative assessment processes feeling integrated and essential rather than optional.

Differentiated planning acknowledges that students bring diverse experiences, skills, and needs to the classroom. Formative assessment complements this by providing students multiple low-stakes chances to show progress and receive support. This intentional opportunity encourages reflection, self-assessment, and student empowerment in the learning process.  This further helps faculty recognize when differentiation is either working or when it needs adjustment. Together, differentiated planning and formative assessment practices normalize variability and support inclusive teaching.

Proficiency with formative assessment strategies can be instrumental in helping university faculty guide students toward authentic and ethical use of generative artificial intelligence (AI). Formative assessments often include reflective activities.  Journals, drafts with feedback, or self-assessments can prompt students to think critically about how they are using AI tools, why they are using them, and whether their usage aligns with course expectations and academic integrity. This encourages transparency and ethical reflection in their AI utilization.

Faculty can provide opportunities for responsible AI utilization through low-stakes assessments or designing tasks that encourage exploration of AI capabilities.  By Integrating scaffolded discussions on bias, accuracy, originality, and ownership faculty can provide feedback on both content and process, including how students documented or integrated AI. These formative strategies create space for instruction and correction before summative grading.

By frequently using formative assessments through progress checks, drafts, or peer reviews, for example, faculty can monitor student thinking and development over time, identify discrepancies in tone, style, or reasoning that may suggest overreliance upon AI.  Therefore, faculty can then intervene early with feedback, support, or redirection. 

Proficiency with formative assessment strategies helps prevent academic dishonesty and guides students toward ethical habits. Faculty can embed clear expectations and transparent modeling of ethical AI utilization into formative tasks such as requiring students to annotate where and how AI tools were used, planning for reflective writing on the role of AI in their developmental and editing processes or co-constructing norms and policies around AI use with the students. The result is formative assessment approaches becoming a platform for building a shared understanding of acceptable practices.

Obviously, not all students will have the same familiarity or comfort with AI. Employing formative assessment strategies can help faculty identify students who need technical support or ethical guidance so faculty can effectively differentiate instruction accordingly.  Adjusting course design or support resources to meet evolving needs is simply good instruction that invites learners to optimize their human potential. This leads to more equitable, intentional, and ethical integration of generative AI. Yes, formative assessment strategies provide a feedback-rich, low-stakes environment that is ideal for fostering responsible, reflective, and ethical engagement with AI tools in higher education. 

Institutions aiming to scale formative assessment practices should consider adopting differentiation frameworks as UDL to anchor its course design.  Such an adoption can highlight how formative assessment fits seamlessly into differentiated planning and instructional practice.  It would be important to offer professional development that synchronizes differentiated planning and formative assessment practices as mutually reinforcing rather than as separate pedagogies. This could reduce resistance and show instructors how to embed assessment in ways that enhance rather than complicate course design.

Effective differentiated processes generate richer, more individualized data through formative assessment.  The synchronization makes it easier to track student growth, document how instructional flexibility improves outcomes, and build an institutional case for formative assessment as a system-level tool for student success. When coursework embeds differentiated processes, formative assessment becomes both a natural and necessary mechanism for monitoring varied learning paths, supporting diverse learners, and ensuring instruction is dynamic and equitable. Thus, alignment makes it much easier for institutions to normalize and sustain formative assessment practices at scale.

The following activities were designed with the assistance of ChatGPT (OpenAI, 2024), a generative AI tool.  The first example is a potential starting point for Higher Education faculty to use AI to effectively develop students’ ethical utilization of Generative Artificial Intelligence. The second example exhibits how a teacher preparation course requiring application of Universal Design for Learning (UDL) principles in lesson planning and instructional design can further develop students’ ethical utilization of AI.

Formative Activity: "AI in My Process" – Reflective Draft and Annotation

Objective: Help students practice using generative AI as a thinking partner while developing transparency and ethical awareness about its role in their academic work.

 

Part 1: Draft a Short Written Assignment

Students complete an early draft of a course-related assignment (e.g., an essay, research summary, discussion post).

 

They are encouraged—but not required—to use a generative AI tool (e.g., ChatGPT, Grammarly, Bard) as a support resource (e.g., for brainstorming, outlining, or revising).

 

They must document and annotate all AI use directly in the draft using comments or footnotes (e.g., "I used ChatGPT to generate three initial topic ideas" or "I asked the AI to rephrase this paragraph more concisely").

 

Part 2: Reflective Memo (300–500 words)

Students submit a short reflective memo addressing the following prompts:

 

Describe how you used (or chose not to use) AI in the drafting process.

 

What did the AI contribute, and how did you evaluate its reliability, originality, or usefulness?

 

How did you ensure that your work remains your own, both intellectually and ethically?

 

Would you do anything differently next time you use AI? Why or why not?

 

Part 3: Instructor Feedback (Formative)

The instructor reviews the draft and reflection, focusing on:

 

Alignment with course and institutional academic integrity policies,

 

The thoughtfulness and transparency of the student's process,

 

Feedback on how to improve both the work and the ethical integration of AI tools.

 

Why This Works:

Encourages meta-awareness and ethical decision-making.

 

Allows students to experiment without penalty.

 

Helps faculty spot patterns of misuse or misunderstanding early.

 

Builds a classroom culture of open dialogue about technology and responsibility.

 

Formative Activity: “Designing with UDL and AI” – Annotated Lesson Plan + Reflection

Course Context: Teacher preparation course focused on applying Universal Design for Learning (UDL) principles in lesson planning and instructional design.

 

Objective: Help teacher candidates ethically and authentically explore how generative AI can assist in developing inclusive lesson plans aligned with UDL principles, while reflecting on its ethical and pedagogical implications.

 

Part 1: Draft a UDL-Enhanced Lesson Plan

Students draft a short lesson plan (or a portion of one) targeting a specific content area and grade level. The plan must include:

 

At least one goal, one method, and one assessment strategy explicitly aligned with UDL principles (e.g., multiple means of engagement, representation, and action/expression).

 

Students are encouraged to use a generative AI tool (e.g., ChatGPT, Curipod, MagicSchool.ai) to assist with idea generation, scaffolding strategies, or accessible materials.

 

Annotation Instructions

Students must annotate the lesson plan draft using comments (or footnotes) that answer:

 

Where did you use AI?

 

What did the AI generate?

 

How did you adapt or evaluate the AI-generated content to align with UDL?

 

Why was this helpful—or not helpful?

 

✅ Example Annotation:

 

"Used ChatGPT to suggest ways to represent vocabulary visually; adapted one idea (image cards) and rejected another (animated GIFs) due to age-appropriateness."

 

Part 2: Reflective Memo (300–500 words)

Prompt students to reflect on:

 

How did using AI impact your thinking or design process in relation to UDL?

 

How did you ensure the lesson remains responsive to learner variability rather than 'one-size-fits-all'?

 

What ethical considerations did you keep in mind while using AI in an educational planning context?

 

What are the implications of AI-assisted planning for equity, accessibility, and teacher responsibility?

 

Part 3: Instructor Feedback

Provide low-stakes, formative feedback focused on:

 

Appropriate integration of UDL principles,

 

Ethical awareness of AI use in educational contexts,

 

Clarity of student reasoning in annotations and reflection.

 

Optional: Use a rubric that lightly scores on transparency, UDL alignment, and ethical reflection (not AI "accuracy").

 

✅ UDL Tie-In for Faculty

This activity itself models UDL by offering:

 

Multiple means of engagement: students explore real-world tools like AI.

 

Multiple means of action and expression: students show learning via plans, annotations, and reflection.

 

Multiple means of representation: encourages integrating varied learner needs through AI-assisted supports.

 

To cite:

Anderson, C.J. (April 30, 2025) Proficiency with formative assessment strategies can help faculty effectively develop  students’ ethical utilization of generative artificial intelligence. [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.

CAST (2024) UDL 3.0 Considerations

NWEA. (2016). 4 formative assessment practices that make a difference in classrooms. https://files.eric.ed.gov/fulltext/ED567811.pdf

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

Planar D and Moya S “The effectiveness of instructor personalized and formative feedback provided by instructor in an online setting: some unresolved issues” The Electronic Journal of e-Learning Volume 14 Issue 3 2016, (pp196-203). www.ejel.org

Pozas, M., Letzel, V. and Schneider, C. (2020). "Teachers and differentiated instruction: exploring differentiation practices to address student diversity." Journal of Research in Special Educational Needs, 20: 217-230.

Stanford Center for Teaching and Learning. (2025). Differentiated instruction. https://ctl.stanford.edu/differentiated-instruction

Turner, W.D., Solis, O.J., and Kincade, D.H. (2017). Differentiating instruction for large classes in higher education, International Journal of Teaching and Learning in Higher Education, 29(3), 490-500.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press

Merging Relevant Topics: Improving the Efficacy of Science of Reading Approaches with Generative Artificial Intelligence (Draft)

Generative Artificial Intelligence (AI) has significant potential to improve the efficacy of Science of Reading approaches.  Science of Reading is not a single program or product but is constantly evolving based on scientific consensus and peer-reviewed research (Moats, 2020). While embracing the importance of phonemic and phonetic awareness skill development, Science of Reading also incorporates Scarborough’s Reading Rope, which highlights the need for both word recognition skills and language comprehension strategies or proficiencies. This ensures that students don’t just read words but are able to understand and engage with texts.

AI-powered platforms can assess students’ reading abilities in real time and adjust subsequent instruction accordingly. Adaptive learning tools can provide differentiated instruction based on a student’s phonemic awareness, decoding skills, and fluency. AI can track patterns in errors and provide targeted interventions.

The need for effective Teacher Preparation is more crucial than ever. AI can support teacher candidates by simulating real-life classroom scenarios whereby they practice Science of Reading-aligned strategies.  AI-driven coaching tools can provide immediate feedback on lesson delivery, phonics instruction, or error correction.  AI can analyze large datasets to identify which instructional strategies are most effective in different contexts.

Data-driven formative feedback must be valid to provide reliable insights for instruction.  AI can analyze reading assessments and progress monitoring data to identify trends and predict which students may need early intervention.  AI-driven analytics can help schools and teacher preparation programs refine their Science of Reading-based curricula.

AI-Assisted lesson plans and reading interventions can support struggling readers through speech recognition and natural language processing tools that guide pronunciation, fluency, and comprehension. Diagnostically, intelligent tutoring systems can provide scaffolded tiered support to students based on their diverse needs. AI can assist students with reading disabilities by providing customized text-to-speech, visual modifications, or alternative instructional approaches.

Generative AI can help achieve the goal of being effective with students while being efficient with time.  AI can generate instant feedback on student reading fluency, pronunciation, and comprehension. As a result, AI-powered assessment tools can reduce teacher workload while maintaining high-quality, data-driven instruction.

Crucially, Generative AI should be used as a supplement rather than a replacement for an expert teacher.  The professional educator will ensure concerns around data privacy and bias in AI modeling are ethically addressed. Therefore, despite the potential for generalization indicated by previous studies, the effectiveness of Generative AI being aligned with evidence-based Science of Reading practices depends on the quality of training and reliable use of data.  

A comprehensive meta-analysis evaluated the effectiveness of AI interventions in language learning, considering various contextual, instructional, and social-emotional factors (Wu, 2024) and concluded AI interventions significantly improved language learning outcomes, particularly in listening and speaking skills. Medium-duration AI interventions ranging from 6 weeks to 6 months in online and blended learning environments were most effective. Additionally, AI tools positively influenced motivation and reduced language anxiety among learners. Therefore, personalized and adaptive AI tools, especially those providing diverse feedback sources, exhibits the potential to optimize language learning outcomes.

A mixed-methods study of university students examined the effects of AI-mediated language instruction on English learning achievement, motivation, and self-regulated learning among English as a Foreign Language (EFL) learners (Wei, 2023). ​ The experimental group that received AI-mediated instruction demonstrated significantly higher outcomes in grammar, vocabulary, reading comprehension, and writing skills compared to the control group. Furthermore, the experimental group exhibited increased motivation and utilized more self-regulated learning strategies.

AI-driven learning environments were found to effectively promote reading comprehension and foster positive learning behaviors in a study by Shafiee Rad (2025) whereby the impact of AI-based platforms on reading comprehension, engagement, and self-regulated learning among students was investigated. Compared to those students receiving traditional instruction, students using the AI-enhanced platform showed significant improvements in reading comprehension and reported higher levels of engagement and self-regulation.​ To what extent can AI-driven learning environments effectively promote reading comprehension and foster positive learning behaviors will require further research.​

As reported by Hollingsworth (2024) AI-powered assistive technologies are being integrated into educational settings to support students with disabilities, including those with reading challenges such as dyslexia.​ By offering features like speech recognition and word prediction, these AI tools aid students in reading and writing tasks and promoting inclusivity in literacy education. While AI provides valuable support, it's essential to balance its use to ensure it complements traditional learning methods without fostering overreliance.​ Although AI is a game changer for students with disabilities, staff and faculty at educational institutions  are still learning how to utilize and implement it.

These studies and potential applications illustrate the growing role of AI in enhancing reading instruction and aligning with the principles of the Science of Reading. They underscore AI's potential to personalize learning, boost engagement, and support diverse learner needs. However, they also highlight the importance of thoughtful implementation and ongoing evaluation to address challenges and maximize benefits.

Several AI-powered tools align with the Science of Reading principles, focusing on phonemic awareness, phonics, fluency, vocabulary, and comprehension. These tools provide personalized learning, adaptive instruction, and real-time feedback to enhance literacy instruction. Based on categorically emphasis, let’s discuss established programs and those gaining traction to better understand how Generative AI can improve the efficacy of Science of Reading approaches.

Amplify Reading and Lexia Core5 Reading provide AI tools for phonemic awareness and phonics. Amplify Reading uses AI to personalize phonics instruction and scaffold phonemic awareness through adaptive learning paths, real-time student data, and Science of Reading -aligned decodable texts. Its programming is most suitable for K-5 students, particularly struggling readers. Lexia Core5 Reading provides structured literacy instruction, targeting phonemic awareness, phonics, and fluency through AI-driven progress monitoring, explicit instruction, and scaffolded support. Its programming addresses the needs of Pre-K-5 students needing systematic phonics instruction.

ReadWorks and Curipod provide AI tools for fluency and reading comprehension.  ReadWorks uses AI to enhance comprehension through text scaffolding and question generation. The program provides differentiated reading assignments, AI-powered text simplification, and comprehension quizzes. It is recommended for students in Grades 1-12, particularly for improving comprehension skills. Curipod AI generates interactive Science of Reading-aligned lesson plans and assessments. Its AI-driven lesson creation, real-time comprehension checks, and engagement analytics can help teachers seeking to design structured reading lessons.

SoapBox Labs and Read AI by Microsoft provide tools for AI-powered reading intervention.  SoapBox Labs uses AI for speech recognition in literacy, through voice AI to analyze reading fluency and pronunciation errors. Its AI-driven speech-to-text analysis, real-time feedback, and Science of Reading-aligned reading fluency support early readers and struggling readers needing pronunciation support. Read AI by Microsoft tracks reading progress, provides real-time feedback, and assesses fluency. Its AI-generated reading reports, automatic error detection, and personalized interventions are a resource for teachers needing automated fluency assessments.

Quillionz and ChatGPT provide AI tools for vocabulary and language development.  Quillionz uses AI to generate comprehension questions and quizzes based on Science of Reading-aligned texts. Its AI-powered question generation, vocabulary reinforcement, and interactive activities supports teachers seeking to design vocabulary and comprehension assessments. ChatGPT can be adjusted to generate decodable texts, phonics exercises, and reading interventions based on Science of Reading principles. The result is the potential for AI-driven lesson planning, real-time student engagement tools, and scaffolded learning activities that support educators seeking to create custom Science of Reading-aligned content.

Regardless of the instructional intervention, it is crucial to recognize efficacy does not entail teaching alone.  A highly qualified teacher understands the ongoing relationship between the curriculum, his or her instruction, and ongoing assessment of learning.  So, again, AI should supplement and never replace structured Science of Reading instruction.  The effective educator will regularly and reliably assess student progress and adapt the utilization of AI accordingly.  Lastly, we all must be mindful and vigilant of student data security in AI-powered platforms.

 

To cite:

Anderson, C.J. (March 31, 2025) Merging relevant topics: Improving the efficacy of science of reading approaches with generative artificial intelligence. [Web log post] Retrieved from http://www.ucan-cja.blogspot.com/

 

References:

 

 Kim, Y.-S., Wagner, R. K., & Lopez, D. (2012). Developmental relations between reading fluency and reading comprehension: A longitudinal study from grade 1 to grade 2. Journal of Experimental Child Psychology , 113(1), 93-111. doi: 10.1016/j.jecp.2012.03.002 

Moats, L. C. (2020). Speech to print: Language essentials for teachers (3rd ed.).Brookes Publishing. 

Shafiee Rad, H. (2025). Reinforcing L2 reading comprehension through artificial intelligence intervention: refining engagement to foster self-regulated learning. Smart Learning. Environments. 12, 23. https://doi.org/10.1186/s40561-025-00377-2

 

Wei L. (2023) Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning. Front Psychology. 14:1261955. doi: 10.3389/fpsyg.2023.1261955. PMID: 38023040; PMCID: PMC10658009.

Wu, X.Y (2024) Artificial intelligence in L2 learning: A meta-analysis of contextual, instructional, and social-emotional moderators. System, Volume 126, 103498, ISSN 0346-251X, https://doi.org/10.1016/j.system.2024.103498

Not Just Good Branding: How Science of Reading Can be a More Effective Process for Teaching Diverse Learners to Read

Regardless of the instructional intervention, it is crucial to recognize efficacy does not entail teaching alone.  A highly qualified teacher will understand the ongoing relationship between the curriculum, his or her instruction, and ongoing assessment of learning.  Competency regarding this relationship should be exhibited through increased classroom assessment literacy whereby standards-based instruction is continually provided and monitored through diverse and consistent formative and criterion assessments.

Undoubtedly, programs that utilize a phonemic and phonological awareness approach in a multisensory, systemic reading intervention model offer research-based Tier 3 RTI.  However, for at least two years following successful participation in any early intervention program, the effective school needs to ensure the student is exposed to “good classroom instruction and moderate personal motivation that should be achievable” (Clay, 2005, p. 52).  Providing instructional leaders with the skills to advance these competencies and promoting professional development in the area of classroom assessment literacy will address the need to optimize learning and sustain success.  Such an endeavor exhibits the vision for excellence in education and promotes the mission of learning for all. 

The Science of Reading is more than just a buzzword; it is grounded in a vast body of interdisciplinary research from cognitive science, neuroscience, linguistics, and education. Its effectiveness in teaching diverse learners to read—especially those who struggle with traditional methods—comes from its evidence-based, structured, and explicit approach to reading instruction. Documented improved literacy outcomes is what makes Science of Reading more than just good branding.

In contrast to spoken language, reading is not a naturally occurring skill that the brain automatically picks up. Science of Reading leverages research on how the brain processes print.  The Five Pillars of early literacy identified by the National Reading Panel (2000), suggests successful reading instruction must include systematic and explicit teaching of foundational skills such as phonemic awareness, phonics, fluency, vocabulary, and comprehension.

Regardless if identified as dyslexic, are English Language Learners (ELLs), or from varying socio-economic backgrounds, diverse learners benefit from explicit phonics instruction. To address this need, Science of Reading emphasizes:

• Teaching phonemes (speech sounds) explicitly so students can decode words efficiently.
• Connecting phonics instruction to spelling and writing.
• Ensuring students develop automaticity in recognizing words so they can focus on comprehension.

Science of Reading approaches have been particularly beneficial for students with dyslexia and reading difficulties. Yet, it supports all learners by providing a structured, cumulative approach to literacy. Unlike a balanced literacy approach that encourages using pictures cues or context clues, Science of Reading ensures that all students learn to systematically decode. Phonemic and phonetic awareness skills are explicitly and directly taught. This foundational emphasis contradicts three-cueing approaches whereby students are encouraged to guess words using context clues or pictures cues rather than using decoding skills. Research shows that struggling readers, in particular, do not benefit from this strategy and the inferential approach can widen literacy gaps over time.

While embracing the importance of phonemic and phonetic awareness skill development, Science of Reading also incorporates Scarborough’s Reading Rope, which highlights the need for both word recognition: decoding and language comprehension: background knowledge, vocabulary, syntax. This ensures that students don’t just read words (word call) but are able to understand and engage with texts.  The latter is especially important for multilingual learners and students from diverse linguistic backgrounds. Therefore, Science of Reading is not a single program or product but is constantly evolving based on scientific consensus and peer-reviewed research. The conceptual framework for Science of Reading is supported by decades of studies on reading acquisition and intervention.

Some critics argue that Science of Reading has become a branding tool.  With publishers and training programs using the term loosely, this will always be a concern for this or any program.  However, when implemented with fidelity rather than cursory-level training or reliance upon overview materials, Science of Reading has been shown to significantly improve literacy outcomes for all students. How can the efficacy of Science of Reading professional development be optimized?

Implementing the Science of Reading through teacher preparation programs will require a shift from traditional literacy instruction to evidence-based practices that align with reading science. To promote this transformational change, teacher preparation programs should implement the following strategies:

• Ensuring coursework covers how the brain learns to read:
• Reading courses need to emphasize phonemic awareness, phonics, fluency, vocabulary, and comprehension.
• Introducing teacher candidates to Scarborough’s Reading Rope and The Simple View of Reading
• Provide opportunities for the model to show how word recognition and language comprehension work together.
• Addressing problems with previous reading instruction systems,
• Identify ineffective practices for diverse learners such as three-cueing and leveled reading approaches that do not embrace explicit phonics instruction.

• Teaching their candidates systematic instruction and explicit phonics techniques.
• Modeling and practicing diagnostic assessments
• Such as phonemic awareness screening and decoding assessments
• Ensures future teachers can identify and address reading difficulties early.
• Training teacher candidates in multisensory approaches
• Orton-Gillingham based methods for engaging diverse learners.

• Incorporating clinical field experiences whereby candidates practice structured literacy with real students.
• Including ELLs and students with dyslexia.
• Partnering with schools implementing Science of Reading-aligned curricula to provide placements that would provide authentic exposure.

• Teaching how to use reading assessments
• DIBELS, Acadience, MAP Reading Fluency, CORE IRA
• To guide instructional decisions
• Help candidates analyze student data to determine effective interventions.

• Integrating decodable texts that reinforce phonics skills with leveled readers for subsequent language comprehension skill development.
• Teaching candidates how to balance knowledge-building with phonics instruction using content-rich texts across disciplines.

• Ensuring Science of Reading practices are adapted for linguistically and culturally diverse learners
• Integrating oral language development, background knowledge building, and morphological instruction
• Crucial for ELLs.
• Incorporating high-quality texts that reflect diverse experiences while maintaining a focus on structured literacy.

• Equipping teacher candidates with research and tools to advocate for evidence-based literacy instruction in their future classrooms and districts.

The Science of Reading is being widely adopted because it has been proven effective.  It is rooted in research on how the brain learns to read.  Science of Reading provides structured, explicit, and systematic instruction that benefits all learners, especially those who struggle. While the term, “Science of Reading” may be marketed, its core principles, especially when correctly applied, typically lead to more equitable and effective literacy instruction across diverse student populations.

 To cite:

Anderson, C.J. (February 27. 2025) Not just good branding: How Science of Reading can be a more effective process for teaching diverse learners to read. [Web log post] Retrieved from http://www.ucan-cja.blogspot.com/

 

References;

 

Big Ideas in Beginning Reading (2009) University of Oregon Center on Teaching and Learning

Retrieved from:  http://reading.uoregon.edu/resources/bibr_pa_concepts.pdf  

Brown, R. (2021). Understanding dyslexia. A whitepaper published by for Illuminate Education

Burkins, J., & Yates, K. (2021). Shifting the balance: 6 ways to bring the science of reading

into the balanced literacy classroom. Stenhouse Publishers.

 

Chall, J.S. (1983) Stages of reading development. McGraw Hill.

Clay, M. M. (1993). Reading recovery. Heinemann

 

Coley, J.D. & Hoffman, D.M (1990). Overcoming learned helplessness in at-risk readers. Journal of Reading v33. n7 497-508.

 

Ehri, L.. Dreyer, L., Flugman,B. and Gross, A. Alan. (2007) Reading Rescue: An effective

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